1 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2 % Electronic Journal of Mathematics and Technology (eJMT) %
3 % style sheet for LaTeX. Please do not modify sections %
4 % or commands marked 'eJMT'. %
6 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
10 \documentclass[12pt,a4paper]{article}% %
12 \usepackage{amsfonts,amsmath,amssymb} %
13 \usepackage[a4paper]{geometry} %
14 \usepackage{fancyhdr} %
16 \usepackage[pdftex]{hyperref} % see note below %
17 \usepackage{graphicx}% %
23 \newtheorem{theorem}{Theorem} %
24 \newtheorem{acknowledgement}[theorem]{Acknowledgement} %
25 \newtheorem{algorithm}[theorem]{Algorithm} %
26 \newtheorem{axiom}[theorem]{Axiom} %
27 \newtheorem{case}[theorem]{Case} %
28 \newtheorem{claim}[theorem]{Claim} %
29 \newtheorem{conclusion}[theorem]{Conclusion} %
30 \newtheorem{condition}[theorem]{Condition} %
31 \newtheorem{conjecture}[theorem]{Conjecture} %
32 \newtheorem{corollary}[theorem]{Corollary} %
33 \newtheorem{criterion}[theorem]{Criterion} %
34 \newtheorem{definition}[theorem]{Definition} %
35 \newtheorem{example}[theorem]{Example} %
36 \newtheorem{exercise}[theorem]{Exercise} %
37 \newtheorem{lemma}[theorem]{Lemma} %
38 \newtheorem{notation}[theorem]{Notation} %
39 \newtheorem{problem}[theorem]{Problem} %
40 \newtheorem{proposition}[theorem]{Proposition} %
41 \newtheorem{remark}[theorem]{Remark} %
42 \newtheorem{solution}[theorem]{Solution} %
43 \newtheorem{summary}[theorem]{Summary} %
44 \newenvironment{proof}[1][Proof]{\noindent\textbf{#1.} } %
45 {\ \rule{0.5em}{0.5em}} %
47 % eJMT page dimensions %
49 \geometry{left=2cm,right=2cm,top=3.2cm,bottom=4cm} %
51 % eJMT header & footer %
53 \newcounter{ejmtFirstpage} %
54 \setcounter{ejmtFirstpage}{1} %
56 \setlength{\headheight}{14pt} %
57 \geometry{left=2cm,right=2cm,top=3.2cm,bottom=4cm} %
58 \pagestyle{fancyplain} %
60 \fancyhead[c]{\small The Electronic Journal of Mathematics%
61 \ and Technology, Volume 1, Number 1, ISSN 1933-2823} %
63 \ifnum\value{ejmtFirstpage}=0% %
64 {\vtop to\hsize{\hrule\vskip .2cm\thepage}}% %
65 \else\setcounter{ejmtFirstpage}{0}\fi% %
68 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
70 % Please place your own definitions here
72 \def\isac{${\cal I}\mkern-2mu{\cal S}\mkern-5mu{\cal AC}$}
73 \def\sisac{\footnotesize${\cal I}\mkern-2mu{\cal S}\mkern-5mu{\cal AC}$}
76 \definecolor{lgray}{RGB}{238,238,238}
79 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
81 % How to use hyperref %
82 % ------------------- %
84 % Probably the only way you will need to use the hyperref %
85 % package is as follows. To make some text, say %
86 % "My Text Link", into a link to the URL %
87 % http://something.somewhere.com/mystuff, use %
89 % \href{http://something.somewhere.com/mystuff}{My Text Link}
91 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
97 \title{Trials with TP-based Programming
99 for Interactive Course Material}%
101 % Single author. Please supply at least your name,
102 % email address, and affiliation here.
104 \author{\begin{tabular}{c}
105 \textit{Jan Ro\v{c}nik} \\
106 jan.rocnik@student.tugraz.at \\
108 Graz University of Technologie\\
112 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
114 % eJMT commands - do not change these %
119 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
125 Traditional course material in engineering disciplines lacks an
126 important component, interactive support for step-wise problem
127 solving. Theorem-Proving (TP) technology is appropriate for one part
128 of such support, in checking user-input. For the other part of such
129 support, guiding the learner towards a solution, another kind of
130 technology is required. %TODO ... connect to prototype ...
132 A prototype combines TP with a programming language, the latter
133 interpreted in a specific way: certain statements in a program, called
134 tactics, are treated as breakpoints where control is handed over to
135 the user. An input formula is checked by TP (using logical context
136 built up by the interpreter); and if a learner gets stuck, a program
137 describing the steps towards a solution of a problem ``knows the next
138 step''. This kind of interpretation is called Lucas-Interpretation for
139 \emph{TP-based programming languages}.
141 This paper describes the prototype's TP-based programming language
142 within a case study creating interactive material for an advanced
143 course in Signal Processing: implementation of definitions and
144 theorems in TP, formal specification of a problem and step-wise
145 development of the program solving the problem. Experiences with the
146 ork flow in iterative development with testing and identifying errors
147 are described, too. The description clarifies the components missing
148 in the prototype's language as well as deficiencies experienced during
151 These experiences are particularly notable, because the author is the
152 first programmer using the language beyond the core team which
153 developed the prototype's TP-based language interpreter.
157 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
161 \thispagestyle{fancy} %
163 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
165 % Please use the following to indicate sections, subsections,
166 % etc. Please also use \subsubsection{...}, \paragraph{...}
167 % and \subparagraph{...} as necessary.
170 \section{Introduction}\label{intro}
172 % \paragraph{Didactics of mathematics}
173 %WN: wenn man in einem high-quality paper von 'didactics' spricht,
174 %WN muss man am state-of-the-art ankn"upfen -- siehe
175 %WN W.Neuper, On the Emergence of TP-based Educational Math Assistants
176 % faces a specific issue, a gap
177 % between (1) introduction of math concepts and skills and (2)
178 % application of these concepts and skills, which usually are separated
179 % into different units in curricula (for good reasons). For instance,
180 % (1) teaching partial fraction decomposition is separated from (2)
181 % application for inverse Z-transform in signal processing.
183 % \par This gap is an obstacle for applying math as an fundamental
184 % thinking technology in engineering: In (1) motivation is lacking
185 % because the question ``What is this stuff good for?'' cannot be
186 % treated sufficiently, and in (2) the ``stuff'' is not available to
187 % students in higher semesters as widespread experience shows.
189 % \paragraph{Motivation} taken by this didactic issue on the one hand,
190 % and ongoing research and development on a novel kind of educational
191 % mathematics assistant at Graz University of
192 % Technology~\footnote{http://www.ist.tugraz.at/isac/} promising to
193 % scope with this issue on the other hand, several institutes are
194 % planning to join their expertise: the Institute for Information
195 % Systems and Computer Media (IICM), the Institute for Software
196 % Technology (IST), the Institutes for Mathematics, the Institute for
197 % Signal Processing and Speech Communication (SPSC), the Institute for
198 % Structural Analysis and the Institute of Electrical Measurement and
199 % Measurement Signal Processing.
200 %WN diese Information ist f"ur das Paper zu spezielle, zu aktuell
201 %WN und damit zu verg"anglich.
202 % \par This thesis is the first attempt to tackle the above mentioned
203 % issue, it focuses on Telematics, because these specific studies focus
204 % on mathematics in \emph{STEOP}, the introductory orientation phase in
205 % Austria. \emph{STEOP} is considered an opportunity to investigate the
206 % impact of {\sisac}'s prototype on the issue and others.
209 \paragraph{Traditional course material} in engineering disciplines lacks an
210 important component, interactive support for step-wise problem
211 solving. Theorem-Proving (TP) technology can provide such support by
212 specific services. An important part of such services is called
213 ``next-step-guidance'', generated by a specific kind of ``TP-based
214 programming language''. In the
215 {\sisac}-project~\footnote{http://www.ist.tugraz.at/projects/isac/} such
216 a language is prototyped in line with~\cite{plmms10} and built upon
218 Isabelle~\cite{Nipkow-Paulson-Wenzel:2002}\footnote{http://isabelle.in.tum.de/}.
219 The TP services are coordinated by a specific interpreter for the
220 programming language, called
221 Lucas-Interpreter~\cite{wn:lucas-interp-12}. The language and the
222 interpreter will be briefly re-introduced in order to make the paper
225 \subparagraph{The main part} of the paper is an account of first experiences
226 with programming in this TP-based language. The experience was gained
227 in a case study by the author. The author was considered an ideal
228 candidate for this study for the following reasons: as a student in
229 Telematics (computer science with focus on Signal Processing) he had
230 general knowledge in programming as well as specific domain knowledge
231 in Signal Processing; and he was not involved in the development of
232 {\sisac}'s programming language and interpeter, thus a novice to the
235 \subparagraph{The goal} of the case study was (1) some TP-based programs for
236 interactive course material for a specific ``Adavanced Signal
237 Processing Lab'' in a higher semester, (2) respective program
238 development with as little advice from the {\sisac}-team and (3) records
239 and comments for the main steps of development in an Isabelle theory;
240 this theory should provide guidelines for future programmers. An
241 excerpt from this theory is the main part of this paper.
243 The paper will use the problem in Fig.\ref{fig-interactive} as a
247 \includegraphics[width=140mm]{fig/isac-Ztrans-math-3}
248 %\includegraphics[width=140mm]{fig/isac-Ztrans-math}
249 \caption{Step-wise problem solving guided by the TP-based program}
250 \label{fig-interactive}
254 \paragraph{The problem is} from the domain of Signal Processing and requests to
255 determine the inverse Z-transform for a given term. Fig.\ref{fig-interactive}
256 also shows the beginning of the interactive construction of a solution
257 for the problem. This construction is done in the right window named
260 User-interaction on the Worksheet is {\em checked} and {\em guided} by
263 \item Formulas input by the user are {\em checked} by TP: such a
264 formula establishes a proof situation --- the prover has to derive the
265 formula from the logical context. The context is built up from the
266 formal specification of the problem (here hidden from the user) by the
268 \item If the user gets stuck, the program developed below in this
269 paper ``knows the next step'' from behind the scenes. How the latter
270 TP-service is exploited by dialogue authoring is out of scope of this
271 paper and can be studied in~\cite{gdaroczy-EP-13}.
272 \end{enumerate} It should be noted that the programmer using the
273 TP-based language is not concerned with interaction at all; we will
274 see that the program contains neither input-statements nor
275 output-statements. Rather, interaction is handled by services
276 generated automatically.
278 So there is a clear separation of concerns: Dialogues are
279 adapted by dialogue authors (in Java-based tools), using automatically
280 generated TP services, while the TP-based program is written by
281 mathematics experts (in Isabelle/ML). The latter is concern of this
284 \paragraph{The paper is structed} as follows: The introduction
285 \S\ref{intro} is followed by a brief re-introduction of the TP-based
286 programming language in \S\ref{PL}, which extends the executable
287 fragment of Isabelle's language (\S\ref{PL-isab}) by tactics which
288 play a specific role in Lucas-Interpretation and in providing the TP
289 services (\S\ref{PL-tacs}). The main part in \S\ref{trial} describes
290 the main steps in developing the program for the running example:
291 prepare domain knowledge, implement the formal specification of the
292 problem, prepare the environment for the program, implement the
293 program. The workflow of programming, debugging and testing is
294 described in \S\ref{workflow}. The conclusion \S\ref{conclusion} will
295 give directions identified for future development.
298 \section{\isac's Prototype for a Programming Language}\label{PL}
299 The prototype's language extends the executable fragment in the
300 language of the theorem prover
301 Isabelle~\cite{Nipkow-Paulson-Wenzel:2002}\footnote{http://isabelle.in.tum.de/}
302 by tactics which have a specific role in Lucas-Interpretation.
304 \subsection{The Executable Fragment of Isabelle's Language}\label{PL-isab}
305 The executable fragment consists of data-type and function
306 definitions. It's usability even suggests that fragment for
307 introductory courses \cite{nipkow-prog-prove}. HOL is a typed logic
308 whose type system resembles that of functional programming
309 languages. Thus there are
311 \item[base types,] in particular \textit{bool}, the type of truth
312 values, \textit{nat}, \textit{int}, \textit{complex}, and the types of
313 natural, integer and complex numbers respectively in mathematics.
314 \item[type constructors] allow to define arbitrary types, from
315 \textit{set}, \textit{list} to advanced data-structures like
316 \textit{trees}, red-black-trees etc.
317 \item[function types,] denoted by $\Rightarrow$.
318 \item[type variables,] denoted by $^\prime a, ^\prime b$ etc, provide
319 type polymorphism. Isabelle automatically computes the type of each
320 variable in a term by use of Hindley-Milner type inference
321 \cite{pl:hind97,Milner-78}.
324 \textbf{Terms} are formed as in functional programming by applying
325 functions to arguments. If $f$ is a function of type
326 $\tau_1\Rightarrow \tau_2$ and $t$ is a term of type $\tau_1$ then
327 $f\;t$ is a term of type~$\tau_2$. $t\;::\;\tau$ means that term $t$
328 has type $\tau$. There are many predefined infix symbols like $+$ and
329 $\leq$ most of which are overloaded for various types.
331 HOL also supports some basic constructs from functional programming:
332 {\it\label{isabelle-stmts}
333 \begin{tabbing} 123\=\kill
334 \>$( \; {\tt if} \; b \; {\tt then} \; t_1 \; {\tt else} \; t_2 \;)$\\
335 \>$( \; {\tt let} \; x=t \; {\tt in} \; u \; )$\\
336 \>$( \; {\tt case} \; t \; {\tt of} \; {\it pat}_1
337 \Rightarrow t_1 \; |\dots| \; {\it pat}_n\Rightarrow t_n \; )$
339 \noindent The running example's program uses some of these elements
340 (marked by {\tt tt-font} on p.\pageref{s:impl}): for instance {\tt
341 let}\dots{\tt in} in lines {\rm 02} \dots {\rm 13}. In fact, the whole program
342 is an Isabelle term with specific function constants like {\tt
343 program}, {\tt Take}, {\tt Rewrite}, {\tt Subproblem} and {\tt
344 Rewrite\_Set} in lines {\rm 01, 03. 04, 07, 10} and {\rm 11, 12}
347 % Terms may also contain $\lambda$-abstractions. For example, $\lambda
348 % x. \; x$ is the identity function.
350 %JR warum auskommentiert? WN2...
351 %WN2 weil ein Punkt wie dieser in weiteren Zusammenh"angen innerhalb
352 %WN2 des Papers auftauchen m"usste; nachdem ich einen solchen
353 %WN2 Zusammenhang _noch_ nicht sehe, habe ich den Punkt _noch_ nicht
355 %WN2 Wenn der Punkt nicht weiter gebraucht wird, nimmt er nur wertvollen
356 %WN2 Platz f"ur Anderes weg.
358 \textbf{Formulae} are terms of type \textit{bool}. There are the basic
359 constants \textit{True} and \textit{False} and the usual logical
360 connectives (in decreasing order of precedence): $\neg, \land, \lor,
363 \textbf{Equality} is available in the form of the infix function $=$
364 of type $a \Rightarrow a \Rightarrow {\it bool}$. It also works for
365 formulas, where it means ``if and only if''.
367 \textbf{Quantifiers} are written $\forall x. \; P$ and $\exists x. \;
368 P$. Quantifiers lead to non-executable functions, so functions do not
369 always correspond to programs, for instance, if comprising \\$(
370 \;{\it if} \; \exists x.\;P \; {\it then} \; e_1 \; {\it else} \; e_2
373 \subsection{\isac's Tactics for Lucas-Interpretation}\label{PL-tacs}
374 The prototype extends Isabelle's language by specific statements
375 called tactics~\footnote{{\sisac}'s tactics are different from
376 Isabelle's tactics: the former concern steps in a calculation, the
377 latter concern proof steps.} and tacticals. For the programmer these
378 statements are functions with the following signatures:
381 \item[Rewrite:] ${\it theorem}\Rightarrow{\it term}\Rightarrow{\it
382 term} * {\it term}\;{\it list}$:
383 this tactic appplies {\it theorem} to a {\it term} yielding a {\it
384 term} and a {\it term list}, the list are assumptions generated by
385 conditional rewriting. For instance, the {\it theorem}
386 $b\not=0\land c\not=0\Rightarrow\frac{a\cdot c}{b\cdot c}=\frac{a}{b}$
387 applied to the {\it term} $\frac{2\cdot x}{3\cdot x}$ yields
388 $(\frac{2}{3}, [x\not=0])$.
390 \item[Rewrite\_Set:] ${\it ruleset}\Rightarrow{\it
391 term}\Rightarrow{\it term} * {\it term}\;{\it list}$:
392 this tactic appplies {\it ruleset} to a {\it term}; {\it ruleset} is
393 a confluent and terminating term rewrite system, in general. If
394 none of the rules ({\it theorem}s) is applicable on interpretation
395 of this tactic, an exception is thrown.
397 % \item[Rewrite\_Inst:] ${\it substitution}\Rightarrow{\it
398 % theorem}\Rightarrow{\it term}\Rightarrow{\it term} * {\it term}\;{\it
401 % \item[Rewrite\_Set\_Inst:] ${\it substitution}\Rightarrow{\it
402 % ruleset}\Rightarrow{\it term}\Rightarrow{\it term} * {\it term}\;{\it
405 \item[Substitute:] ${\it substitution}\Rightarrow{\it
406 term}\Rightarrow{\it term}$: allows to access sub-terms.
408 \item[Take:] ${\it term}\Rightarrow{\it term}$:
409 this tactic has no effect in the program; but it creates a side-effect
410 by Lucas-Interpretation (see below) and writes {\it term} to the
413 \item[Subproblem:] ${\it theory} * {\it specification} * {\it
414 method}\Rightarrow{\it argument}\;{\it list}\Rightarrow{\it term}$:
415 this tactic is a generalisation of a function call: it takes an
416 \textit{argument list} as usual, and additionally a triple consisting
417 of an Isabelle \textit{theory}, an implicit \textit{specification} of the
418 program and a \textit{method} containing data for Lucas-Interpretation,
419 last not least a program (as an explicit specification)~\footnote{In
420 interactive tutoring these three items can be determined explicitly
423 The tactics play a specific role in
424 Lucas-Interpretation~\cite{wn:lucas-interp-12}: they are treated as
425 break-points where, as a side-effect, a line is added to a calculation
426 as a protocol for proceeding towards a solution in step-wise problem
427 solving. At the same points Lucas-Interpretation serves interactive
428 tutoring and control is handed over to the user. The user is free to
429 investigate underlying knowledge, applicable theorems, etc. And the
430 user can proceed constructing a solution by input of a tactic to be
431 applied or by input of a formula; in the latter case the
432 Lucas-Interpreter has built up a logical context (initialised with the
433 precondition of the formal specification) such that Isabelle can
434 derive the formula from this context --- or give feedback, that no
435 derivation can be found.
437 \subsection{Tacticals for Control of Interpretation}
438 The flow of control in a program can be determined by {\tt if then else}
439 and {\tt case of} as mentioned on p.\pageref{isabelle-stmts} and also
440 by additional tacticals:
442 \item[Repeat:] ${\it tactic}\Rightarrow{\it term}\Rightarrow{\it
443 term}$: iterates over tactics which take a {\it term} as argument as
444 long as a tactic is applicable (for instance, {\tt Rewrite\_Set} might
447 \item[Try:] ${\it tactic}\Rightarrow{\it term}\Rightarrow{\it term}$:
448 if {\it tactic} is applicable, then it is applied to {\it term},
449 otherwise {\it term} is passed on without changes.
451 \item[Or:] ${\it tactic}\Rightarrow{\it tactic}\Rightarrow{\it
452 term}\Rightarrow{\it term}$: If the first {\it tactic} is applicable,
453 it is applied to the first {\it term} yielding another {\it term},
454 otherwise the second {\it tactic} is applied; if none is applicable an
457 \item[@@:] ${\it tactic}\Rightarrow{\it tactic}\Rightarrow{\it
458 term}\Rightarrow{\it term}$: applies the first {\it tactic} to the
459 first {\it term} yielding an intermediate term (not appearing in the
460 signature) to which the second {\it tactic} is applied.
462 \item[While:] ${\it term::bool}\Rightarrow{\it tactic}\Rightarrow{\it
463 term}\Rightarrow{\it term}$: if the first {\it term} is true, then the
464 {\it tactic} is applied to the first {\it term} yielding an
465 intermediate term (not appearing in the signature); the intermediate
466 term is added to the environment the first {\it term} is evaluated in
467 etc as long as the first {\it term} is true.
469 The tacticals are not treated as break-points by Lucas-Interpretation
470 and thus do not contribute to the calculation nor to interaction.
472 \section{Development of a Program on Trial}\label{trial}
473 As mentioned above, {\sisac} is an experimental system for a proof of
474 concept for Lucas-Interpretation~\cite{wn:lucas-interp-12}. The
475 latter interprets a specific kind of TP-based programming language,
476 which is as experimental as the whole prototype --- so programming in
477 this language can be only ``on trial'', presently. However, as a
478 prototype, the language addresses essentials described below.
480 \subsection{Mechanization of Math --- Domain Engineering}\label{isabisac}
482 %WN was Fachleute unter obigem Titel interessiert findet sich
483 %WN unterhalb des auskommentierten Textes.
485 %WN der Text unten spricht Benutzer-Aspekte anund ist nicht speziell
486 %WN auf Computer-Mathematiker fokussiert.
487 % \paragraph{As mentioned in the introduction,} a prototype of an
488 % educational math assistant called
489 % {{\sisac}}\footnote{{{\sisac}}=\textbf{Isa}belle for
490 % \textbf{C}alculations, see http://www.ist.tugraz.at/isac/.} bridges
491 % the gap between (1) introducation and (2) application of mathematics:
492 % {{\sisac}} is based on Computer Theorem Proving (TP), a technology which
493 % requires each fact and each action justified by formal logic, so
494 % {{{\sisac}{}}} makes justifications transparent to students in
495 % interactive step-wise problem solving. By that way {{\sisac}} already
498 % \item Introduction of math stuff (in e.g. partial fraction
499 % decomposition) by stepwise explaining and exercising respective
500 % symbolic calculations with ``next step guidance (NSG)'' and rigorously
501 % checking steps freely input by students --- this also in context with
502 % advanced applications (where the stuff to be taught in higher
503 % semesters can be skimmed through by NSG), and
504 % \item Application of math stuff in advanced engineering courses
505 % (e.g. problems to be solved by inverse Z-transform in a Signal
506 % Processing Lab) and now without much ado about basic math techniques
507 % (like partial fraction decomposition): ``next step guidance'' supports
508 % students in independently (re-)adopting such techniques.
510 % Before the question is answers, how {{\sisac}}
511 % accomplishes this task from a technical point of view, some remarks on
512 % the state-of-the-art is given, therefor follow up Section~\ref{emas}.
514 % \subsection{Educational Mathematics Assistants (EMAs)}\label{emas}
516 % \paragraph{Educational software in mathematics} is, if at all, based
517 % on Computer Algebra Systems (CAS, for instance), Dynamic Geometry
518 % Systems (DGS, for instance \footnote{GeoGebra http://www.geogebra.org}
519 % \footnote{Cinderella http://www.cinderella.de/}\footnote{GCLC
520 % http://poincare.matf.bg.ac.rs/~janicic/gclc/}) or spread-sheets. These
521 % base technologies are used to program math lessons and sometimes even
522 % exercises. The latter are cumbersome: the steps towards a solution of
523 % such an interactive exercise need to be provided with feedback, where
524 % at each step a wide variety of possible input has to be foreseen by
525 % the programmer - so such interactive exercises either require high
526 % development efforts or the exercises constrain possible inputs.
528 % \subparagraph{A new generation} of educational math assistants (EMAs)
529 % is emerging presently, which is based on Theorem Proving (TP). TP, for
530 % instance Isabelle and Coq, is a technology which requires each fact
531 % and each action justified by formal logic. Pushed by demands for
532 % \textit{proven} correctness of safety-critical software TP advances
533 % into software engineering; from these advancements computer
534 % mathematics benefits in general, and math education in particular. Two
535 % features of TP are immediately beneficial for learning:
537 % \paragraph{TP have knowledge in human readable format,} that is in
538 % standard predicate calculus. TP following the LCF-tradition have that
539 % knowledge down to the basic definitions of set, equality,
540 % etc~\footnote{http://isabelle.in.tum.de/dist/library/HOL/HOL.html};
541 % following the typical deductive development of math, natural numbers
542 % are defined and their properties
543 % proven~\footnote{http://isabelle.in.tum.de/dist/library/HOL/Number\_Theory/Primes.html},
544 % etc. Present knowledge mechanized in TP exceeds high-school
545 % mathematics by far, however by knowledge required in software
546 % technology, and not in other engineering sciences.
548 % \paragraph{TP can model the whole problem solving process} in
549 % mathematical problem solving {\em within} a coherent logical
550 % framework. This is already being done by three projects, by
551 % Ralph-Johan Back, by ActiveMath and by Carnegie Mellon Tutor.
553 % Having the whole problem solving process within a logical coherent
554 % system, such a design guarantees correctness of intermediate steps and
555 % of the result (which seems essential for math software); and the
556 % second advantage is that TP provides a wealth of theories which can be
557 % exploited for mechanizing other features essential for educational
560 % \subsubsection{Generation of User Guidance in EMAs}\label{user-guid}
562 % One essential feature for educational software is feedback to user
563 % input and assistance in coming to a solution.
565 % \paragraph{Checking user input} by ATP during stepwise problem solving
566 % is being accomplished by the three projects mentioned above
567 % exclusively. They model the whole problem solving process as mentioned
568 % above, so all what happens between formalized assumptions (or formal
569 % specification) and goal (or fulfilled postcondition) can be
570 % mechanized. Such mechanization promises to greatly extend the scope of
571 % educational software in stepwise problem solving.
573 % \paragraph{NSG (Next step guidance)} comprises the system's ability to
574 % propose a next step; this is a challenge for TP: either a radical
575 % restriction of the search space by restriction to very specific
576 % problem classes is required, or much care and effort is required in
577 % designing possible variants in the process of problem solving
578 % \cite{proof-strategies-11}.
580 % Another approach is restricted to problem solving in engineering
581 % domains, where a problem is specified by input, precondition, output
582 % and postcondition, and where the postcondition is proven by ATP behind
583 % the scenes: Here the possible variants in the process of problem
584 % solving are provided with feedback {\em automatically}, if the problem
585 % is described in a TP-based programing language: \cite{plmms10} the
586 % programmer only describes the math algorithm without caring about
587 % interaction (the respective program is functional and even has no
588 % input or output statements!); interaction is generated as a
589 % side-effect by the interpreter --- an efficient separation of concern
590 % between math programmers and dialog designers promising application
591 % all over engineering disciplines.
594 % \subsubsection{Math Authoring in Isabelle/ISAC\label{math-auth}}
595 % Authoring new mathematics knowledge in {{\sisac}} can be compared with
596 % ``application programing'' of engineering problems; most of such
597 % programing uses CAS-based programing languages (CAS = Computer Algebra
598 % Systems; e.g. Mathematica's or Maple's programing language).
600 % \paragraph{A novel type of TP-based language} is used by {{\sisac}{}}
601 % \cite{plmms10} for describing how to construct a solution to an
602 % engineering problem and for calling equation solvers, integration,
603 % etc~\footnote{Implementation of CAS-like functionality in TP is not
604 % primarily concerned with efficiency, but with a didactic question:
605 % What to decide for: for high-brow algorithms at the state-of-the-art
606 % or for elementary algorithms comprehensible for students?} within TP;
607 % TP can ensure ``systems that never make a mistake'' \cite{casproto} -
608 % are impossible for CAS which have no logics underlying.
610 % \subparagraph{Authoring is perfect} by writing such TP based programs;
611 % the application programmer is not concerned with interaction or with
612 % user guidance: this is concern of a novel kind of program interpreter
613 % called Lucas-Interpreter. This interpreter hands over control to a
614 % dialog component at each step of calculation (like a debugger at
615 % breakpoints) and calls automated TP to check user input following
616 % personalized strategies according to a feedback module.
618 % However ``application programing with TP'' is not done with writing a
619 % program: according to the principles of TP, each step must be
620 % justified. Such justifications are given by theorems. So all steps
621 % must be related to some theorem, if there is no such theorem it must
622 % be added to the existing knowledge, which is organized in so-called
623 % \textbf{theories} in Isabelle. A theorem must be proven; fortunately
624 % Isabelle comprises a mechanism (called ``axiomatization''), which
625 % allows to omit proofs. Such a theorem is shown in
626 % Example~\ref{eg:neuper1}.
628 The running example, introduced by Fig.\ref{fig-interactive} on
629 p.\pageref{fig-interactive}, requires to determine the inverse $\cal
630 Z$-transform for a class of functions. The domain of Signal Processing
631 is accustomed to specific notation for the resulting functions, which
632 are absolutely summable and are called TODO: $u[n]$, where $u$ is the
633 function, $n$ is the argument and the brackets indicate that the
634 arguments are TODO. Surprisingly, Isabelle accepts the rules for
635 ${\cal Z}^{-1}$ in this traditional notation~\footnote{Isabelle
636 experts might be particularly surprised, that the brackets do not
637 cause errors in typing (as lists).}:
641 {\small\begin{tabbing}
642 123\=123\=123\=123\=\kill
644 \>axiomatization where \\
645 \>\> rule1: ``${\cal Z}^{-1}\;1 = \delta [n]$'' and\\
646 \>\> rule2: ``$\vert\vert z \vert\vert > 1 \Rightarrow {\cal Z}^{-1}\;z / (z - 1) = u [n]$'' and\\
647 \>\> rule3: ``$\vert\vert$ z $\vert\vert$ < 1 ==> z / (z - 1) = -u [-n - 1]'' and \\
649 \>\> rule4: ``$\vert\vert$ z $\vert\vert$ > $\vert\vert$ $\alpha$ $\vert\vert$ ==> z / (z - $\alpha$) = $\alpha^n$ $\cdot$ u [n]'' and\\
651 \>\> rule5: ``$\vert\vert$ z $\vert\vert$ < $\vert\vert$ $\alpha$ $\vert\vert$ ==> z / (z - $\alpha$) = -($\alpha^n$) $\cdot$ u [-n - 1]'' and\\
653 \>\> rule6: ``$\vert\vert$ z $\vert\vert$ > 1 ==> z/(z - 1)$^2$ = n $\cdot$ u [n]''\\
659 These 6 rules can be used as conditional rewrite rules, depending on
660 the respective convergence radius. Satisfaction from accordance with traditional notation
661 contrasts with the above word {\em axiomatization}: As TP-based, the
662 programming language expects these rules as {\em proved} theorems, and
663 not as axioms implemented in the above brute force manner; otherwise
664 all the verification efforts envisaged (like proof of the
665 post-condition, see below) would be meaningless.
667 Isabelle provides a large body of knowledge, rigorously proven from
668 the basic axioms of mathematics~\footnote{This way of rigorously
669 deriving all knowledge from first principles is called the
670 LCF-paradigm in TP.}. In the case of the Z-Transform the most advanced
671 knowledge can be found in the theoris on Multivariate
672 Analysis~\footnote{http://isabelle.in.tum.de/dist/library/HOL/HOL-Multivariate\_Analysis}. However,
673 building up knowledge such that a proof for the above rules would be
674 reasonably short and easily comprehensible, still requires lots of
675 work (and is definitely out of scope of our case study).
677 At the state-of-the-art in mechanization of knowledge in engineering
678 sciences, the process does not stop with the mechanization of
679 mathematics traditionally used in these sciences. Rather, ``Formal
680 Methods''~\cite{ fm-03} are expected to proceed to formal and explicit
681 description of physical items. Signal Processing, for instance is
682 concerned with physical devices for signal acquisition and
683 reconstruction, which involve measuring a physical signal, storing it,
684 and possibly later rebuilding the original signal or an approximation
685 thereof. For digital systems, this typically includes sampling and
686 quantization; devices for signal compression, including audio
687 compression, image compression, and video compression, etc. ``Domain
688 engineering''\cite{db:dom-eng} is concerned with {\em specification}
689 of these devices' components and features; this part in the process of
690 mechanization is only at the beginning in domains like Signal
693 TP-based programming, concern of this paper, is determined to
694 add ``algorithmic knowledge'' in Fig.\ref{fig:mathuni} on
695 p.\pageref{fig:mathuni}. As we shall see below, TP-based programming
696 starts with a formal {\em specification} of the problem to be solved.
699 \includegraphics[width=110mm]{fig/math-universe-small}
700 \caption{The three-dimensional universe of mathematics knowledge}
704 The language for both axes is defined in the axis at the bottom, deductive
705 knowledge, in {\sisac} represented by Isabelle's theories.
707 \subsection{Preparation of Simplifiers for the Program}\label{simp}
709 \paragraph{If it is clear} how the later calculation should look like and when
710 which mathematic rule should be applied, it can be started to find ways of
711 simplifications. This includes in e.g. the simplification of reational
712 expressions or also rewrites of an expession.
713 \subparagraph{Obligate is the use} of the function \texttt{drop\_questionmarks}
714 which excludes irrelevant symbols out of the expression. (Irrelevant symbols may
715 be result out of the system during the calculation. The function has to be
716 applied for two reasons. First two make every placeholder in a expression
717 useable as a constant and second to provide a better view at the frontend.)
718 \subparagraph{Most rewrites are represented} through rulesets this
719 rulesets tell the machine which terms have to be rewritten into which
720 representation. In the upcoming programm a rewrite can be applied only in using
721 such rulesets on existing terms.
722 \paragraph{The core} of our implemented problem is the Z-Transformation
723 (remember the description of the running example, introduced by
724 Fig.\ref{fig-interactive} on p.\pageref{fig-interactive}) due the fact that the
725 transformation itself would require higher math which isn't yet avaible in our system we decided to choose the way like it is applied in labratory and problem classes at our university - by applying transformation rules (collected in
726 transformation tables).
727 \paragraph{Rules,} in {\sisac{}}'s programming language can be designed by the
728 use of axiomatizations like shown in Example~\ref{eg:ruledef}. This rules can be
729 collected in a ruleset (collection of rules) and applied to a given expression
730 as follows in Example~\ref{eg:ruleapp}.
737 rule1: ``1 = $\delta$[n]'' and
738 rule2: ``|| z || > 1 ==> z / (z - 1) = u [n]'' and
739 rule3: ``|| z || < 1 ==> z / (z - 1) = -u [-n - 1]''
747 \item Store rules in ruleset:
749 val inverse_Z = append_rls "inverse_Z" e_rls
750 [ Thm ("rule1",num_str @{thm rule1}),
751 Thm ("rule2",num_str @{thm rule2}),
752 Thm ("rule3",num_str @{thm rule3})
754 \item Define exression:
756 val sample_term = str2term "z/(z-1)+z/(z-</delta>)+1";\end{verbatim}
759 val SOME (sample_term', asm) =
760 rewrite_set_ thy true inverse_Z sample_term;\end{verbatim}
764 The use of rulesets makes it much easier to develop our designated applications,
765 but the programmer has to be careful and patient. When applying rulesets
766 two important issues have to be mentionend:
767 \subparagraph{How often} the rules have to be applied? In case of
768 transformations it is quite clear that we use them once but other fields
769 reuqire to apply rules until a special condition is reached (e.g.
770 a simplification is finished when there is nothing to be done left).
771 \subparagraph{The order} in which rules are applied often takes a big effect
772 and has to be evaluated for each purpose once again.
774 In our special case of Signal Processing and the rules defined in
775 Example~\ref{eg:ruledef} we have to apply rule~1 first of all to transform all
776 constants. After this step has been done it no mather which rule fit's next.
778 \subsection{Preparation of ML-Functions}\label{funs}
780 \paragraph{Explicit Problems} require explicit methods to solve them, and within
781 this methods we have some explicit steps to do. This steps can be unique for
782 a special problem or refindable in other problems. No mather what case, such
783 steps often require some technical functions behind. For the solving process
784 of the Inverse Z Transformation and the corresponding partial fraction it was
785 neccessary to build helping functions like \texttt{get\_denominator},
786 \texttt{get\_numerator} or \texttt{argument\_in}. First two functions help us
787 to filter the denominator or numerator out of a fraction, last one helps us to
788 get to know the bound variable in a equation.
790 By taking \texttt{get\_denominator} as an example, we want to explain how to
791 implement new functions into the existing system and how we can later use them
794 \subsubsection{Find a place to Store the Function}
796 The whole system builds up on a well defined structure of Knowledge. This
797 Knowledge sets up at the Path:
798 \begin{center}\ttfamily src/Tools/isac/Knowledge\normalfont\end{center}
799 For implementing the Function \texttt{get\_denominator} (which let us extract
800 the denominator out of a fraction) we have choosen the Theory (file)
801 \texttt{Rational.thy}.
803 \subsubsection{Write down the new Function}
805 In upper Theory we now define the new function and its purpose:
807 get_denominator :: "real => real"
809 This command tells the machine that a function with the name
810 \texttt{get\_denominator} exists which gets a real expression as argument and
811 returns once again a real expression. Now we are able to implement the function
812 itself, Example~\ref{eg:getdenom} now shows the implementation of
813 \texttt{get\_denominator}.
820 02 *("get_denominator",
821 03 * ("Rational.get_denominator", eval_get_denominator ""))
823 05 fun eval_get_denominator (thmid:string) _
824 06 (t as Const ("Rational.get_denominator", _) $
825 07 (Const ("Rings.inverse_class.divide", _) $num
827 09 SOME (mk_thmid thmid ""
828 10 (Print_Mode.setmp []
829 11 (Syntax.string_of_term (thy2ctxt thy)) denom) "",
830 12 Trueprop $ (mk_equality (t, denom)))
831 13 | eval_get_denominator _ _ _ _ = NONE;\end{verbatim}
834 Line \texttt{07} and \texttt{08} are describing the mode of operation the best -
835 there is a fraction\\ (\ttfamily Rings.inverse\_class.divide\normalfont)
837 into its two parts (\texttt{\$num \$denom}). The lines before are additionals
838 commands for declaring the function and the lines after are modeling and
839 returning a real variable out of \texttt{\$denom}.
841 \subsubsection{Add a test for the new Function}
843 \paragraph{Everytime when adding} a new function it is essential also to add
844 a test for it. Tests for all functions are sorted in the same structure as the
845 knowledge it self and can be found up from the path:
846 \begin{center}\ttfamily test/Tools/isac/Knowledge\normalfont\end{center}
847 This tests are nothing very special, as a first prototype the functionallity
848 of a function can be checked by evaluating the result of a simple expression
849 passed to the function. Example~\ref{eg:getdenomtest} shows the test for our
850 \textit{just} created function \texttt{get\_denominator}.
853 \label{eg:getdenomtest}
856 01 val thy = @{theory Isac};
857 02 val t = term_of (the (parse thy "get_denominator ((a +x)/b)"));
858 03 val SOME (_, t') = eval_get_denominator "" 0 t thy;
859 04 if term2str t' = "get_denominator ((a + x) / b) = b" then ()
860 05 else error "get_denominator ((a + x) / b) = b" \end{verbatim}
864 \item[01] checks if the proofer set up on our {\sisac{}} System.
865 \item[02] passes a simple expression (fraction) to our suddenly created
867 \item[04] checks if the resulting variable is the correct one (in this case
868 ``b'' the denominator) and returns.
869 \item[05] handels the error case and reports that the function is not able to
870 solve the given problem.
873 \subsection{Specification of the Problem}\label{spec}
874 %WN <--> \chapter 7 der Thesis
875 %WN die Argumentation unten sollte sich NUR auf Verifikation beziehen..
877 The problem of the running example is textually described in
878 Fig.\ref{fig-interactive} on p.\pageref{fig-interactive}. The {\em
879 formal} specification of this problem, in traditional mathematical
880 notation, could look like is this:
882 %WN Hier brauchen wir die Spezifikation des 'running example' ...
883 %JR Habe input, output und precond vom Beispiel eingefügt brauche aber Hilfe bei
884 %JR der post condition - die existiert für uns ja eigentlich nicht aka
885 %JR haben sie bis jetzt nicht beachtet WN...
886 %WN2 Mein Vorschlag ist, das TODO zu lassen und deutlich zu kommentieren.
890 {\small\begin{tabbing}
891 123,\=postcond \=: \= $\forall \,A^\prime\, u^\prime \,v^\prime.\,$\=\kill
894 \>input \>: filterExpression $X=\frac{3}{(z-\frac{1}{4}+-\frac{1}{8}*\frac{1}{z}}$\\
895 \>precond \>: filterExpression continius on $\mathbb{R}$ \\
896 \>output \>: stepResponse $x[n]$ \\
897 \>postcond \>: TODO - (Mind the following remark)\\ \end{tabbing}}
900 Defining the postcondition requires a high amount mathematical
901 knowledge, the difficult part in our case is not to set up this condition
902 nor it is more to define it in a way the interpreter is able to handle it.
903 Due the fact that implementing that mechanisms is quite the same amount as
904 creating the programm itself, it is not avaible in our prototype.
908 \paragraph{The implementation} of the formal specification in the present
909 prototype, still bar-bones without support for authoring:
910 %WN Kopie von Inverse_Z_Transform.thy, leicht versch"onert:
911 {\footnotesize\label{exp-spec}
913 01 store_specification
914 02 (prepare_specification
916 04 "pbl_SP_Ztrans_inv"
918 06 ( ["Inverse", "Z_Transform", "SignalProcessing"],
919 07 [ ("#Given", ["filterExpression X_eq"]),
920 08 ("#Pre" , ["X_eq is_continuous"]),
921 19 ("#Find" , ["stepResponse n_eq"]),
922 10 ("#Post" , [" TODO "])],
923 11 append_rls Erls [Calc ("Atools.is_continuous", eval_is_continuous)],
925 13 [["SignalProcessing","Z_Transform","Inverse"]]));
927 Although the above details are partly very technical, we explain them
928 in order to document some intricacies of TP-based programming in the
929 present state of the {\sisac} prototype:
931 \item[01..02]\textit{store\_specification:} stores the result of the
932 function \textit{prep\_specification} in a global reference
933 \textit{Unsynchronized.ref}, which causes principal conflicts with
934 Isabelle's asyncronous document model~\cite{Wenzel-11:doc-orient} and
935 parallel execution~\cite{Makarius-09:parall-proof} and is under
936 reconstruction already.
938 \textit{prep\_pbt:} translates the specification to an internal format
939 which allows efficient processing; see for instance line {\rm 07}
941 \item[03..04] are the ``mathematics author'' holding the copy-rights
942 and a unique identifier for the specification within {\sisac},
943 complare line {\rm 06}.
944 \item[05] is the Isabelle \textit{theory} required to parse the
945 specification in lines {\rm 07..10}.
946 \item[06] is a key into the tree of all specifications as presented to
947 the user (where some branches might be hidden by the dialog
949 \item[07..10] are the specification with input, pre-condition, output
950 and post-condition respectively; the post-condition is not handled in
951 the prototype presently. (Follow up Remark~\ref{rm:postcond})
952 \item[11] creates a term rewriting system (abbreviated \textit{rls} in
953 {\sisac}) which evaluates the pre-condition for the actual input data.
954 Only if the evaluation yields \textit{True}, a program con be started.
955 \item[12]\textit{NONE:} could be \textit{SOME ``solve ...''} for a
956 problem associated to a function from Computer Algebra (like an
957 equation solver) which is not the case here.
958 \item[13] is the specific key into the tree of programs addressing a
959 method which is able to find a solution which satiesfies the
960 post-condition of the specification.
964 %WN die folgenden Erkl"arungen finden sich durch "grep -r 'datatype pbt' *"
967 % {guh : guh, (*unique within this isac-knowledge*)
968 % mathauthors: string list, (*copyright*)
969 % init : pblID, (*to start refinement with*)
970 % thy : theory, (* which allows to compile that pbt
971 % TODO: search generalized for subthy (ref.p.69*)
972 % (*^^^ WN050912 NOT used during application of the problem,
973 % because applied terms may be from 'subthy' as well as from super;
974 % thus we take 'maxthy'; see match_ags !*)
975 % cas : term option,(*'CAS-command'*)
976 % prls : rls, (* for preds in where_*)
977 % where_: term list, (* where - predicates*)
979 % (*this is the model-pattern;
980 % it contains "#Given","#Where","#Find","#Relate"-patterns
981 % for constraints on identifiers see "fun cpy_nam"*)
982 % met : metID list}; (* methods solving the pbt*)
984 %WN weil dieser Code sehr unaufger"aumt ist, habe ich die Erkl"arungen
985 %WN oben selbst geschrieben.
990 %WN das w"urde ich in \sec\label{progr} verschieben und
991 %WN das SubProblem partial fractions zum Erkl"aren verwenden.
992 % Such a specification is checked before the execution of a program is
993 % started, the same applies for sub-programs. In the following example
994 % (Example~\ref{eg:subprob}) shows the call of such a subproblem:
1000 % {\ttfamily \begin{tabbing}
1001 % ``(L\_L::bool list) = (\=SubProblem (\=Test','' \\
1002 % ``\>\>[linear,univariate,equation,test],'' \\
1003 % ``\>\>[Test,solve\_linear])'' \\
1004 % ``\>[BOOL equ, REAL z])'' \\
1008 % \noindent If a program requires a result which has to be
1009 % calculated first we can use a subproblem to do so. In our specific
1010 % case we wanted to calculate the zeros of a fraction and used a
1011 % subproblem to calculate the zeros of the denominator polynom.
1016 \subsection{Implementation of the Method}\label{meth}
1024 02 (prep_met thy "met_SP_Ztrans_inv" [] e_metID
1025 03 (["SignalProcessing", "Z_Transform", "Inverse"],
1026 04 [("#Given" ,["filterExpression (X_eq::bool)"]),
1027 05 ("#Find" ,["stepResponse (n_eq::bool)"])],
1028 06 {rew_ord'="tless_true",
1041 \subsection{Implementation of the TP-based Program}\label{progr}
1042 So finally all the prerequisites are described and the very topic can
1043 be addressed. The program below comes back to the running example: it
1044 computes a solution for the problem from Fig.\ref{fig-interactive} on
1045 p.\pageref{fig-interactive}. The reader is reminded of
1046 \S\ref{PL-isab}, the introduction of the programming language:
1047 {\small\it\label{s:impl}
1049 123l\=123\=123\=123\=123\=123\=123\=((x\=123\=(x \=123\=123\=\kill
1050 \>{\rm 00}\>val program =\\
1051 \>{\rm 01}\> "{\tt Program} InverseZTransform (X\_eq::bool) = \\
1052 \>{\rm 02}\>\> {\tt let} \\
1053 \>{\rm 03}\>\>\> X\_eq = {\tt Take} X\_eq ; \\
1054 \>{\rm 04}\>\>\> X\_eq = {\tt Rewrite} ruleZY X\_eq ; \\
1055 \>{\rm 05}\>\>\> (X\_z::real) = lhs X\_eq ; \\ %no inside-out evaluation
1056 \>{\rm 06}\>\>\> (z::real) = argument\_in X\_z; \\
1057 \>{\rm 07}\>\>\> (part\_frac::real) = {\tt SubProblem} \\
1058 \>{\rm 08}\>\>\>\>\>\>\>\> ( Isac, [partial\_fraction, rational, simplification], [] )\\
1059 %\>{\rm 10}\>\>\>\>\>\>\>\>\> [simplification, of\_rationals, to\_partial\_fraction] ) \\
1060 \>{\rm 09}\>\>\>\>\>\>\>\> [ (rhs X\_eq)::real, z::real ]; \\
1061 \>{\rm 10}\>\>\> (X'\_eq::bool) = {\tt Take} ((X'::real =$>$ bool) z = ZZ\_1 part\_frac) ; \\
1062 \>{\rm 11}\>\>\> X'\_eq = (({\tt Rewrite\_Set} ruleYZ) @@ \\
1063 \>{\rm 12}\>\>\>\>\> $\;\;$ ({\tt Rewrite\_Set} inverse\_z)) X'\_eq \\
1064 \>{\rm 13}\>\> {\tt in } \\
1065 \>{\rm 14}\>\>\> X'\_eq"
1067 % ORIGINAL FROM Inverse_Z_Transform.thy
1068 % "Script InverseZTransform (X_eq::bool) = "^(*([], Frm), Problem (Isac, [Inverse, Z_Transform, SignalProcessing])*)
1069 % "(let X = Take X_eq; "^(*([1], Frm), X z = 3 / (z - 1 / 4 + -1 / 8 * (1 / z))*)
1070 % " X' = Rewrite ruleZY False X; "^(*([1], Res), ?X' z = 3 / (z * (z - 1 / 4 + -1 / 8 * (1 / z)))*)
1071 % " (X'_z::real) = lhs X'; "^(* ?X' z*)
1072 % " (zzz::real) = argument_in X'_z; "^(* z *)
1073 % " (funterm::real) = rhs X'; "^(* 3 / (z * (z - 1 / 4 + -1 / 8 * (1 / z)))*)
1075 % " (pbz::real) = (SubProblem (Isac', "^(**)
1076 % " [partial_fraction,rational,simplification], "^
1077 % " [simplification,of_rationals,to_partial_fraction]) "^
1078 % " [REAL funterm, REAL zzz]); "^(*([2], Res), 4 / (z - 1 / 2) + -4 / (z - -1 / 4)*)
1080 % " (pbz_eq::bool) = Take (X'_z = pbz); "^(*([3], Frm), ?X' z = 4 / (z - 1 / 2) + -4 / (z - -1 / 4)*)
1081 % " pbz_eq = Rewrite ruleYZ False pbz_eq; "^(*([3], Res), ?X' z = 4 * (?z / (z - 1 / 2)) + -4 * (?z / (z - -1 / 4))*)
1082 % " pbz_eq = drop_questionmarks pbz_eq; "^(* 4 * (z / (z - 1 / 2)) + -4 * (z / (z - -1 / 4))*)
1083 % " (X_zeq::bool) = Take (X_z = rhs pbz_eq); "^(*([4], Frm), X_z = 4 * (z / (z - 1 / 2)) + -4 * (z / (z - -1 / 4))*)
1084 % " n_eq = (Rewrite_Set inverse_z False) X_zeq; "^(*([4], Res), X_z = 4 * (1 / 2) ^^^ ?n * ?u [?n] + -4 * (-1 / 4) ^^^ ?n * ?u [?n]*)
1085 % " n_eq = drop_questionmarks n_eq "^(* X_z = 4 * (1 / 2) ^^^ n * u [n] + -4 * (-1 / 4) ^^^ n * u [n]*)
1086 % "in n_eq)" (*([], Res), X_z = 4 * (1 / 2) ^^^ n * u [n] + -4 * (-1 / 4) ^^^ n * u [n]*)
1087 The program is represented as a string and part of the method in
1088 \S\ref{meth}. As mentioned in \S\ref{PL} the program is purely
1089 functional and lacks any input statements and output statements. So
1090 the steps of calculation towards a solution (and interactive tutoring
1091 in step-wise problem solving) are created as a side-effect by
1092 Lucas-Interpretation. The side-effects are triggered by the tactics
1093 \texttt{Take}, \texttt{Rewrite}, \texttt{SubProblem} and
1094 \texttt{Rewrite\_Set} in the above lines {\rm 03, 04, 07, 10, 11} and
1095 {\rm 12} respectively. These tactics produce the lines in the
1096 calculation on p.\pageref{flow-impl}.
1098 The above lines {\rm 05, 06} do not contain a tactics, so they do not
1099 immediately contribute to the calculation on p.\pageref{flow-impl};
1100 rather, they compute actual arguments for the \texttt{SubProblem} in
1101 line {\rm 09}~\footnote{The tactics also are break-points for the
1102 interpreter, where control is handed over to the user in interactive
1103 tutoring.}. Line {\rm 11} contains tactical \textit{@@}.
1105 \medskip The above program also indicates the dominant role of interactive
1106 selection of knowledge in the three-dimensional universe of
1107 mathematics as depicted in Fig.\ref{fig:mathuni} on
1108 p.\pageref{fig:mathuni}, The \texttt{SubProblem} in the above lines
1109 {\rm 07..09} is more than a function call with the actual arguments
1110 \textit{[ (rhs X\_eq)::real, z::real ]}. The programmer has to determine
1114 \item the theory, in the example \textit{Isac} because different
1115 methods can be selected in Pt.3 below, which are defined in different
1116 theories with \textit{Isac} collecting them.
1117 \item the specification identified by \textit{[partial\_fraction,
1118 rational, simplification]} in the tree of specifications; this
1119 specification is analogous to the specification of the main program
1120 described in \S\ref{spec}; the problem is to find a ``partial fraction
1121 decomposition'' for a univariate rational polynomial.
1122 \item the method in the above example is \textit{[ ]}, i.e. empty,
1123 which supposes the interpreter to select one of the methods predefined
1124 in the specification, for instance in line {\rm 13} in the running
1125 example's specification on p.\pageref{exp-spec}~\footnote{The freedom
1126 (or obligation) for selection carries over to the student in
1127 interactive tutoring.}.
1130 The program code, above presented as a string, is parsed by Isabelle's
1131 parser --- the program is an Isabelle term. This fact is expected to
1132 simplify verification tasks in the future; on the other hand, this
1133 fact causes troubles in error detectetion which are discussed as part
1134 of the workflow in the subsequent section.
1136 \section{Workflow of Programming in the Prototype}\label{workflow}
1137 The previous section presented all the duties and tasks to be accomplished by
1138 programmers of TP-based languages. Some tasks are interrelated and comprehensive,
1139 so first experiences with the workflow in programming are noted below. The notes
1140 also capture requirements for future language development.
1142 \subsection{Preparations and Trials}\label{flow-prep}
1143 % Build\_Inverse\_Z\_Transform.thy ... ``imports PolyEq DiffApp Partial\_Fractions''
1144 The new graphical user-interface of Isabelle~\cite{makar-jedit-12} is a great
1145 step forward for interactive theory and proof development --- and so it is for
1146 interactive program development; the specific requirements raised by interactive
1147 programming will be mentioned separately.
1149 The development in the {\sisac}-prototype was done in a separate
1150 theory~\footnote{http://www.ist.tugraz.at/projects/isac/publ/Build\_Inverse\_Z\_Transform.thy}.
1151 The workflow tackled the tasks more or less following the order of the
1152 above sections from \S\ref{isabisac} to \S\ref{funs}. At each stage
1153 the interactivity of Isabelle/jEdit is very supportive. For instance,
1154 as soon as the theorems for the Z-transform are established (see
1155 \S\ref{isabisac}) it is tempting to see them at work: First we need
1156 technical prerequisites not worth to mention and parse a string to a
1157 term using {\sisac}'s function \textit{str2term}:
1158 {\footnotesize\label{exp-spec}
1161 val (thy, ro, er) = (@{theory}, tless_true, eval_rls);
1162 val t = str2term "z / (z - 1) + z / (z - \<alpha>) + 1";
1165 Then we call {\sisac}'s rewrite-engine directly by \textit{rewrite\_} (instead via Lucas-Interpreter by \textit{Rewrite}) and yield
1166 a rewritten term \textit{t'} together with assumptions:
1167 {\footnotesize\label{exp-spec}
1170 val SOME (t', asm) = rewrite_ thy ro er true (num_str @{thm rule3}) t;
1173 And any evaluation of an \texttt{ML} section immediately responds with the
1174 values computed, for instance with the result of the rewrites, which above
1175 have been returned in the internal term representation --- here are the more
1176 readable string representations:
1177 {\footnotesize\label{exp-spec}
1183 val it = "- ?u [- ?n - 1] + z / (z - α) + 1": string
1184 val it = "[|| z || < 1]": string
1186 Looking at the last line shows how the system will reliably handle
1187 assumptions like the convergence radius.
1188 %WN gerne w"urde ich oben das Beispiel aus subsection {*Apply Rules*}
1189 %WN aus http://www.ist.tugraz.at/projects/isac/publ/Build_Inverse_Z_Transform.thy.
1190 %WN Leider bekomme ich einen Fehler --- siehst Du eine schnelle Korrektur ?
1195 TODO test the function \textit{argument\_of} which is embedded in the
1196 ruleset which is used to evaluate the program by the Lucas-Interpreter.
1200 %JR: Hier sollte eigentlich stehen was nun bei 4.3.1 ist. Habe das erst kürzlich
1201 %JR: eingefügt; das war der beinn unserer Arbeit in
1202 %JR: Build_Inverse_Z_Transformation und beschreibt die meiner Meinung nach bei
1203 %JR: jedem neuen Programm nötigen Schritte.
1205 \subsection{Implementation in Isabelle/{\isac}}\label{flow-impl}
1207 \paragraph{At the beginning} of the implementation it is good to decide on one
1208 way the problem should be solved. We also did this for our Z-Transformation
1209 Problem and have choosen the way it is also thaugt in the Signal Processing
1211 \subparagraph{By writing down} each of this neccesarry steps we are describing
1212 one line of our upcoming program. In the following example we show the
1213 Calculation on the left and on the right the tactics in the program which
1214 created the respective formula on the left.
1220 123l\=123\=123\=123\=123\=123\=123\=123\=123\=123\=123\=123\=\kill
1221 \>{\rm 01}\> $\bullet$ \> {\tt Problem } (Inverse\_Z\_Transform, [Inverse, Z\_Transform, SignalProcessing]) \`\\
1222 \>{\rm 02}\>\> $\vdash\;\;X z = \frac{3}{z - \frac{1}{4} - \frac{1}{8} \cdot z^{-1}}$ \`{\footnotesize {\tt Take} X\_eq}\\
1223 \>{\rm 03}\>\> $X z = \frac{3}{z + \frac{-1}{4} + \frac{-1}{8} \cdot \frac{1}{z}}$ \`{\footnotesize {\tt Rewrite} ruleZY X\_eq}\\
1224 \>{\rm 04}\>\> $\bullet$\> {\tt Problem } [partial\_fraction,rational,simplification] \`{\footnotesize {\tt SubProblem} \dots}\\
1225 \>{\rm 05}\>\>\> $\vdash\;\;\frac{3}{z + \frac{-1}{4} + \frac{-1}{8} \cdot \frac{1}{z}}=$ \`- - -\\
1226 \>{\rm 06}\>\>\> $\frac{24}{-1 + -2 \cdot z + 8 \cdot z^2}$ \`- - -\\
1227 \>{\rm 07}\>\>\> $\bullet$\> solve ($-1 + -2 \cdot z + 8 \cdot z^2,\;z$ ) \`- - -\\
1228 \>{\rm 08}\>\>\>\> $\vdash$ \> $\frac{3}{z + \frac{-1}{4} + \frac{-1}{8} \cdot \frac{1}{z}}=0$ \`- - -\\
1229 \>{\rm 09}\>\>\>\> $z = \frac{2+\sqrt{-4+8}}{16}\;\lor\;z = \frac{2-\sqrt{-4+8}}{16}$ \`- - -\\
1230 \>{\rm 10}\>\>\>\> $z = \frac{1}{2}\;\lor\;z =$ \_\_\_ \`- - -\\
1231 \> \>\>\>\> \_\_\_ \`- - -\\
1232 \>{\rm 11}\>\> \dots\> $\frac{4}{z - \frac{1}{2}} + \frac{-4}{z - \frac{-1}{4}}$ \`\\
1233 \>{\rm 12}\>\> $X^\prime z = {\cal Z}^{-1} (\frac{4}{z - \frac{1}{2}} + \frac{-4}{z - \frac{-1}{4}})$ \`{\footnotesize {\tt Take} ((X'::real =$>$ bool) z = ZZ\_1 part\_frac)}\\
1234 \>{\rm 13}\>\> $X^\prime z = {\cal Z}^{-1} (4\cdot\frac{z}{z - \frac{1}{2}} + -4\cdot\frac{z}{z - \frac{-1}{4}})$ \`{\footnotesize{\tt Rewrite\_Set} ruleYZ X'\_eq }\\
1235 \>{\rm 14}\>\> $X^\prime z = 4\cdot(\frac{1}{2})^n \cdot u [n] + -4\cdot(\frac{-1}{4})^n \cdot u [n]$ \`{\footnotesize {\tt Rewrite\_Set} inverse\_z X'\_eq}\\
1236 \>{\rm 15}\> \dots\> $X^\prime z = 4\cdot(\frac{1}{2})^n \cdot u [n] + -4\cdot(\frac{-1}{4})^n \cdot u [n]$ \`{\footnotesize {\tt Check\_Postcond}}
1241 % ORIGINAL FROM Inverse_Z_Transform.thy
1242 % "Script InverseZTransform (X_eq::bool) = "^(*([], Frm), Problem (Isac, [Inverse, Z_Transform, SignalProcessing])*)
1243 % "(let X = Take X_eq; "^(*([1], Frm), X z = 3 / (z - 1 / 4 + -1 / 8 * (1 / z))*)
1244 % " X' = Rewrite ruleZY False X; "^(*([1], Res), ?X' z = 3 / (z * (z - 1 / 4 + -1 / 8 * (1 / z)))*)
1245 % " (X'_z::real) = lhs X'; "^(* ?X' z*)
1246 % " (zzz::real) = argument_in X'_z; "^(* z *)
1247 % " (funterm::real) = rhs X'; "^(* 3 / (z * (z - 1 / 4 + -1 / 8 * (1 / z)))*)
1249 % " (pbz::real) = (SubProblem (Isac', "^(**)
1250 % " [partial_fraction,rational,simplification], "^
1251 % " [simplification,of_rationals,to_partial_fraction]) "^
1252 % " [REAL funterm, REAL zzz]); "^(*([2], Res), 4 / (z - 1 / 2) + -4 / (z - -1 / 4)*)
1254 % " (pbz_eq::bool) = Take (X'_z = pbz); "^(*([3], Frm), ?X' z = 4 / (z - 1 / 2) + -4 / (z - -1 / 4)*)
1255 % " pbz_eq = Rewrite ruleYZ False pbz_eq; "^(*([3], Res), ?X' z = 4 * (?z / (z - 1 / 2)) + -4 * (?z / (z - -1 / 4))*)
1256 % " pbz_eq = drop_questionmarks pbz_eq; "^(* 4 * (z / (z - 1 / 2)) + -4 * (z / (z - -1 / 4))*)
1257 % " (X_zeq::bool) = Take (X_z = rhs pbz_eq); "^(*([4], Frm), X_z = 4 * (z / (z - 1 / 2)) + -4 * (z / (z - -1 / 4))*)
1258 % " n_eq = (Rewrite_Set inverse_z False) X_zeq; "^(*([4], Res), X_z = 4 * (1 / 2) ^^^ ?n * ?u [?n] + -4 * (-1 / 4) ^^^ ?n * ?u [?n]*)
1259 % " n_eq = drop_questionmarks n_eq "^(* X_z = 4 * (1 / 2) ^^^ n * u [n] + -4 * (-1 / 4) ^^^ n * u [n]*)
1260 % "in n_eq)" (*([], Res), X_z = 4 * (1 / 2) ^^^ n * u [n] + -4 * (-1 / 4) ^^^ n * u [n]*)
1264 \subsection{Transfer into the Isabelle/{\isac} Knowledge}\label{flow-trans}
1265 TODO http://www.ist.tugraz.at/isac/index.php/Extend\_ISAC\_Knowledge\#Add\_an\_example ?
1268 http://www.ist.tugraz.at/projects/isac/publ/Inverse\_Z\_Transform.thy
1271 % -------------------------------------------------------------------
1273 % Material, falls noch Platz bleibt ...
1275 % -------------------------------------------------------------------
1278 % \subsubsection{Trials on Notation and Termination}
1280 % \paragraph{Technical notations} are a big problem for our piece of software,
1281 % but the reason for that isn't a fault of the software itself, one of the
1282 % troubles comes out of the fact that different technical subtopics use different
1283 % symbols and notations for a different purpose. The most famous example for such
1284 % a symbol is the complex number $i$ (in cassique math) or $j$ (in technical
1285 % math). In the specific part of signal processing one of this notation issues is
1286 % the use of brackets --- we use round brackets for analoge signals and squared
1287 % brackets for digital samples. Also if there is no problem for us to handle this
1288 % fact, we have to tell the machine what notation leads to wich meaning and that
1289 % this purpose seperation is only valid for this special topic - signal
1291 % \subparagraph{In the programming language} itself it is not possible to declare
1292 % fractions, exponents, absolutes and other operators or remarks in a way to make
1293 % them pretty to read; our only posssiblilty were ASCII characters and a handfull
1294 % greek symbols like: $\alpha, \beta, \gamma, \phi,\ldots$.
1296 % With the upper collected knowledge it is possible to check if we were able to
1297 % donate all required terms and expressions.
1299 % \subsubsection{Definition and Usage of Rules}
1301 % \paragraph{The core} of our implemented problem is the Z-Transformation, due
1302 % the fact that the transformation itself would require higher math which isn't
1303 % yet avaible in our system we decided to choose the way like it is applied in
1304 % labratory and problem classes at our university - by applying transformation
1305 % rules (collected in transformation tables).
1306 % \paragraph{Rules,} in {\sisac{}}'s programming language can be designed by the
1307 % use of axiomatizations like shown in Example~\ref{eg:ruledef}
1310 % \label{eg:ruledef}
1313 % axiomatization where
1314 % rule1: ``1 = $\delta$[n]'' and
1315 % rule2: ``|| z || > 1 ==> z / (z - 1) = u [n]'' and
1316 % rule3: ``|| z || < 1 ==> z / (z - 1) = -u [-n - 1]''
1320 % This rules can be collected in a ruleset and applied to a given expression as
1321 % follows in Example~\ref{eg:ruleapp}.
1325 % \label{eg:ruleapp}
1327 % \item Store rules in ruleset:
1329 % val inverse_Z = append_rls "inverse_Z" e_rls
1330 % [ Thm ("rule1",num_str @{thm rule1}),
1331 % Thm ("rule2",num_str @{thm rule2}),
1332 % Thm ("rule3",num_str @{thm rule3})
1334 % \item Define exression:
1336 % val sample_term = str2term "z/(z-1)+z/(z-</delta>)+1";\end{verbatim}
1337 % \item Apply ruleset:
1339 % val SOME (sample_term', asm) =
1340 % rewrite_set_ thy true inverse_Z sample_term;\end{verbatim}
1344 % The use of rulesets makes it much easier to develop our designated applications,
1345 % but the programmer has to be careful and patient. When applying rulesets
1346 % two important issues have to be mentionend:
1347 % \subparagraph{How often} the rules have to be applied? In case of
1348 % transformations it is quite clear that we use them once but other fields
1349 % reuqire to apply rules until a special condition is reached (e.g.
1350 % a simplification is finished when there is nothing to be done left).
1351 % \subparagraph{The order} in which rules are applied often takes a big effect
1352 % and has to be evaluated for each purpose once again.
1354 % In our special case of Signal Processing and the rules defined in
1355 % Example~\ref{eg:ruledef} we have to apply rule~1 first of all to transform all
1356 % constants. After this step has been done it no mather which rule fit's next.
1358 % \subsubsection{Helping Functions}
1360 % \paragraph{New Programms require,} often new ways to get through. This new ways
1361 % means that we handle functions that have not been in use yet, they can be
1362 % something special and unique for a programm or something famous but unneeded in
1363 % the system yet. In our dedicated example it was for example neccessary to split
1364 % a fraction into numerator and denominator; the creation of such function and
1365 % even others is described in upper Sections~\ref{simp} and \ref{funs}.
1367 % \subsubsection{Trials on equation solving}
1368 % %simple eq and problem with double fractions/negative exponents
1369 % \paragraph{The Inverse Z-Transformation} makes it neccessary to solve
1370 % equations degree one and two. Solving equations in the first degree is no
1371 % problem, wether for a student nor for our machine; but even second degree
1372 % equations can lead to big troubles. The origin of this troubles leads from
1373 % the build up process of our equation solving functions; they have been
1374 % implemented some time ago and of course they are not as good as we want them to
1375 % be. Wether or not following we only want to show how cruel it is to build up new
1376 % work on not well fundamentials.
1377 % \subparagraph{A simple equation solving,} can be set up as shown in the next
1384 % ["equality (-1 + -2 * z + 8 * z ^^^ 2 = (0::real))",
1388 % val (dI',pI',mI') =
1390 % ["abcFormula","degree_2","polynomial","univariate","equation"],
1391 % ["no_met"]);\end{verbatim}
1394 % Here we want to solve the equation: $-1+-2\cdot z+8\cdot z^{2}=0$. (To give
1395 % a short overview on the commands; at first we set up the equation and tell the
1396 % machine what's the bound variable and where to store the solution. Second step
1397 % is to define the equation type and determine if we want to use a special method
1398 % to solve this type.) Simple checks tell us that the we will get two results for
1399 % this equation and this results will be real.
1400 % So far it is easy for us and for our machine to solve, but
1401 % mentioned that a unvariate equation second order can have three different types
1402 % of solutions it is getting worth.
1403 % \subparagraph{The solving of} all this types of solutions is not yet supported.
1404 % Luckily it was needed for us; but something which has been needed in this
1405 % context, would have been the solving of an euation looking like:
1406 % $-z^{-2}+-2\cdot z^{-1}+8=0$ which is basically the same equation as mentioned
1407 % before (remember that befor it was no problem to handle for the machine) but
1408 % now, after a simple equivalent transformation, we are not able to solve
1410 % \subparagraph{Error messages} we get when we try to solve something like upside
1411 % were very confusing and also leads us to no special hint about a problem.
1412 % \par The fault behind is, that we have no well error handling on one side and
1413 % no sufficient formed equation solving on the other side. This two facts are
1414 % making the implemention of new material very difficult.
1416 % \subsection{Formalization of missing knowledge in Isabelle}
1418 % \paragraph{A problem} behind is the mechanization of mathematic
1419 % theories in TP-bases languages. There is still a huge gap between
1420 % these algorithms and this what we want as a solution - in Example
1421 % Signal Processing.
1427 % X\cdot(a+b)+Y\cdot(c+d)=aX+bX+cY+dY
1430 % \noindent A very simple example on this what we call gap is the
1431 % simplification above. It is needles to say that it is correct and also
1432 % Isabelle for fills it correct - \emph{always}. But sometimes we don't
1433 % want expand such terms, sometimes we want another structure of
1434 % them. Think of a problem were we now would need only the coefficients
1435 % of $X$ and $Y$. This is what we call the gap between mechanical
1436 % simplification and the solution.
1441 % \paragraph{We are not able to fill this gap,} until we have to live
1442 % with it but first have a look on the meaning of this statement:
1443 % Mechanized math starts from mathematical models and \emph{hopefully}
1444 % proceeds to match physics. Academic engineering starts from physics
1445 % (experimentation, measurement) and then proceeds to mathematical
1446 % modeling and formalization. The process from a physical observance to
1447 % a mathematical theory is unavoidable bound of setting up a big
1448 % collection of standards, rules, definition but also exceptions. These
1449 % are the things making mechanization that difficult.
1458 % \noindent Think about some units like that one's above. Behind
1459 % each unit there is a discerning and very accurate definition: One
1460 % Meter is the distance the light travels, in a vacuum, through the time
1461 % of 1 / 299.792.458 second; one kilogram is the weight of a
1462 % platinum-iridium cylinder in paris; and so on. But are these
1463 % definitions usable in a computer mechanized world?!
1468 % \paragraph{A computer} or a TP-System builds on programs with
1469 % predefined logical rules and does not know any mathematical trick
1470 % (follow up example \ref{eg:trick}) or recipe to walk around difficult
1476 % \[ \frac{1}{j\omega}\cdot\left(e^{-j\omega}-e^{j3\omega}\right)= \]
1477 % \[ \frac{1}{j\omega}\cdot e^{-j2\omega}\cdot\left(e^{j\omega}-e^{-j\omega}\right)=
1478 % \frac{1}{\omega}\, e^{-j2\omega}\cdot\colorbox{lgray}{$\frac{1}{j}\,\left(e^{j\omega}-e^{-j\omega}\right)$}= \]
1479 % \[ \frac{1}{\omega}\, e^{-j2\omega}\cdot\colorbox{lgray}{$2\, sin(\omega)$} \]
1481 % \noindent Sometimes it is also useful to be able to apply some
1482 % \emph{tricks} to get a beautiful and particularly meaningful result,
1483 % which we are able to interpret. But as seen in this example it can be
1484 % hard to find out what operations have to be done to transform a result
1485 % into a meaningful one.
1490 % \paragraph{The only possibility,} for such a system, is to work
1491 % through its known definitions and stops if none of these
1492 % fits. Specified on Signal Processing or any other application it is
1493 % often possible to walk through by doing simple creases. This creases
1494 % are in general based on simple math operational but the challenge is
1495 % to teach the machine \emph{all}\footnote{Its pride to call it
1496 % \emph{all}.} of them. Unfortunately the goal of TP Isabelle is to
1497 % reach a high level of \emph{all} but it in real it will still be a
1498 % survey of knowledge which links to other knowledge and {{\sisac}{}} a
1499 % trainer and helper but no human compensating calculator.
1501 % {{{\sisac}{}}} itself aims to adds \emph{Algorithmic Knowledge} (formal
1502 % specifications of problems out of topics from Signal Processing, etc.)
1503 % and \emph{Application-oriented Knowledge} to the \emph{deductive} axis of
1504 % physical knowledge. The result is a three-dimensional universe of
1505 % mathematics seen in Figure~\ref{fig:mathuni}.
1509 % \includegraphics{fig/universe}
1510 % \caption{Didactic ``Math-Universe'': Algorithmic Knowledge (Programs) is
1511 % combined with Application-oriented Knowledge (Specifications) and Deductive Knowledge (Axioms, Definitions, Theorems). The Result
1512 % leads to a three dimensional math universe.\label{fig:mathuni}}
1516 % %WN Deine aktuelle Benennung oben wird Dir kein Fachmann abnehmen;
1517 % %WN bitte folgende Bezeichnungen nehmen:
1519 % %WN axis 1: Algorithmic Knowledge (Programs)
1520 % %WN axis 2: Application-oriented Knowledge (Specifications)
1521 % %WN axis 3: Deductive Knowledge (Axioms, Definitions, Theorems)
1523 % %WN und bitte die R"ander von der Grafik wegschneiden (was ich f"ur *.pdf
1524 % %WN nicht hinkriege --- weshalb ich auch die eJMT-Forderung nicht ganz
1525 % %WN verstehe, separierte PDFs zu schicken; ich w"urde *.png schicken)
1527 % %JR Ränder und beschriftung geändert. Keine Ahnung warum eJMT sich pdf's
1528 % %JR wünschen, würde ebenfalls png oder ähnliches verwenden, aber wenn pdf's
1529 % %JR gefordert werden WN2...
1530 % %WN2 meiner Meinung nach hat sich eJMT unklar ausgedr"uckt (z.B. kann
1531 % %WN2 man meines Wissens pdf-figures nicht auf eine bestimmte Gr"osse
1532 % %WN2 zusammenschneiden um die R"ander weg zu bekommen)
1533 % %WN2 Mein Vorschlag ist, in umserem tex-file bei *.png zu bleiben und
1534 % %WN2 png + pdf figures mitzuschicken.
1536 % \subsection{Notes on Problems with Traditional Notation}
1538 % \paragraph{During research} on these topic severely problems on
1539 % traditional notations have been discovered. Some of them have been
1540 % known in computer science for many years now and are still unsolved,
1541 % one of them aggregates with the so called \emph{Lambda Calculus},
1542 % Example~\ref{eg:lamda} provides a look on the problem that embarrassed
1549 % \[ f(x)=\ldots\; \quad R \rightarrow \quad R \]
1552 % \[ f(p)=\ldots\; p \in \quad R \]
1555 % \noindent Above we see two equations. The first equation aims to
1556 % be a mapping of an function from the reel range to the reel one, but
1557 % when we change only one letter we get the second equation which
1558 % usually aims to insert a reel point $p$ into the reel function. In
1559 % computer science now we have the problem to tell the machine (TP) the
1560 % difference between this two notations. This Problem is called
1561 % \emph{Lambda Calculus}.
1566 % \paragraph{An other problem} is that terms are not full simplified in
1567 % traditional notations, in {{\sisac}} we have to simplify them complete
1568 % to check weather results are compatible or not. in e.g. the solutions
1569 % of an second order linear equation is an rational in {{\sisac}} but in
1570 % tradition we keep fractions as long as possible and as long as they
1571 % aim to be \textit{beautiful} (1/8, 5/16,...).
1572 % \subparagraph{The math} which should be mechanized in Computer Theorem
1573 % Provers (\emph{TP}) has (almost) a problem with traditional notations
1574 % (predicate calculus) for axioms, definitions, lemmas, theorems as a
1575 % computer program or script is not able to interpret every Greek or
1576 % Latin letter and every Greek, Latin or whatever calculations
1577 % symbol. Also if we would be able to handle these symbols we still have
1578 % a problem to interpret them at all. (Follow up \hbox{Example
1579 % \ref{eg:symbint1}})
1583 % \label{eg:symbint1}
1585 % u\left[n\right] \ \ldots \ unitstep
1588 % \noindent The unitstep is something we need to solve Signal
1589 % Processing problem classes. But in {{{\sisac}{}}} the rectangular
1590 % brackets have a different meaning. So we abuse them for our
1591 % requirements. We get something which is not defined, but usable. The
1592 % Result is syntax only without semantic.
1597 % In different problems, symbols and letters have different meanings and
1598 % ask for different ways to get through. (Follow up \hbox{Example
1599 % \ref{eg:symbint2}})
1603 % \label{eg:symbint2}
1605 % \widehat{\ }\ \widehat{\ }\ \widehat{\ } \ \ldots \ exponent
1608 % \noindent For using exponents the three \texttt{widehat} symbols
1609 % are required. The reason for that is due the development of
1610 % {{{\sisac}{}}} the single \texttt{widehat} and also the double were
1611 % already in use for different operations.
1616 % \paragraph{Also the output} can be a problem. We are familiar with a
1617 % specified notations and style taught in university but a computer
1618 % program has no knowledge of the form proved by a professor and the
1619 % machines themselves also have not yet the possibilities to print every
1620 % symbol (correct) Recent developments provide proofs in a human
1621 % readable format but according to the fact that there is no money for
1622 % good working formal editors yet, the style is one thing we have to
1625 % \section{Problems rising out of the Development Environment}
1627 % fehlermeldungen! TODO
1629 \section{Conclusion}\label{conclusion}
1633 \bibliographystyle{alpha}
1634 \bibliography{references}