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This paper addresses a model of analogy-driven theorem proving that is more general and cognitively more adequate than previous approaches. The model works at the level ofproof-plans. More precisely, we consider analogy as a control strategy in proof planning that employs a source proof-plan to guide the construction of a proof-plan for the target problem. Our approach includes a reformulation of the source proof-plan. This is in accordance with the well known fact that constructing ananalogy in maths often amounts to first finding the appropriate representation which brings out the similarity of two problems, i.e., finding the right concepts and the right level of abstraction. Several well known theorems were processed by our analogy-driven proof-plan construction that could not be proven analogically by previous approaches.

This paper addresses analogy-driven auto-mated theorem proving that employs a sourceproof-plan to guide the search for a proof-planof the target problem. The approach presen-ted uses reformulations that go beyond symbolmappings and that incorporate frequently usedre-representations and abstractions. Severalrealistic math examples were successfully pro-cessed by our analogy-driven proof-plan con-struction. One challenge example, a Heine-Borel theorem, is discussed here. For this ex-ample the reformulaitons are shown step bystep and the modifying actions are demon-strated.

Analogy in CLAM
(1999)

CL A M is a proof planner, developed by the Dream group in Edinburgh,that mainly operates for inductive proofs. This paper addresses the questionhow an analogy model that I developed independently of CL A M can beapplied to CL A M and it presents analogy-driven proof plan construction as acontrol strategy of CL A M . This strategy is realized as a derivational analogythat includes the reformulation of proof plans. The analogical replay checkswhether the reformulated justifications of the source plan methods hold inthe target as a permission to transfer the method to the target plan. SinceCL A M has very efficient heuristic search strategies, the main purpose ofthe analogy is to suggest lemmas, to replay not commonly loaded methods,to suggest induction variables and induction terms, and to override controlrather than to construct a target proof plan that can be built by CL A Mitself more efficiently.

Die Beweisentwicklungsumgebung Omega-Mkrp soll Mathematiker bei einer ihrer Haupttätigkeiten, nämlich dem Beweisen mathematischer Theoreme unterstützen. Diese Unterstützung muß so komfortabel sein, daß die Beweise mit vertretbarem Aufwand formal durchgeführt werden können und daß die Korrektheit der so erzeugten Beweise durch das System sichergestellt wird. Ein solches System wird sich nur dann wirklich durchsetzen, wenn die rechnergestützte Suche nach formalen Beweisen weniger aufwendig und leichter ist, als ohne das System. Um dies zu erreichen, ergeben sich verschiedene Anforderungen an eine solche Entwicklungsumgebung, die wir im einzelnen beschreiben. Diese betreffen insbesondere die Ausdruckskraft der verwendeten Objektsprache, die Möglichkeit, abstrakt über Beweispläne zu reden, die am Menschen orientierte Präsentation der gefundenen Beweise, aber auch die effiziente Unterstützung beim Füllen von Beweislücken. Das im folgenden vorgestellte Omega-Mkrp-System ist eine Synthese der Ansätze des vollautomatischen, des interaktiven und des planbasierten Beweisens und versucht erstmalig die Ergebnisse dieser drei Forschungsrichtungen in einem System zu vereinigen. Dieser Artikel soll eine Übersicht über unsere Arbeit an diesem System geben.

Planning means constructing a course of actions to achieve a specified set of goals when starting from an initial situation. For example, determining a sequence of actions (a plan) for transporting goods from an initial location to some destination is a typical planning problem in the transportation domain. Many planning problems are of practical interest.

In recent years several computational systems and techniques fortheorem proving by analogy have been developed. The obvious prac-tical question, however, as to whether and when to use analogy hasbeen neglected badly in these developments. This paper addresses thisquestion, identifies situations where analogy is useful, and discussesthe merits of theorem proving by analogy in these situations. Theresults can be generalized to other domains.

Constructing an analogy between a known and already proven theorem(the base case) and another yet to be proven theorem (the target case) oftenamounts to finding the appropriate representation at which the base and thetarget are similar. This is a well-known fact in mathematics, and it was cor-roborated by our empirical study of a mathematical textbook, which showedthat a reformulation of the representation of a theorem and its proof is in-deed more often than not a necessary prerequisite for an analogical inference.Thus machine supported reformulation becomes an important component ofautomated analogy-driven theorem proving too.The reformulation component proposed in this paper is embedded into aproof plan methodology based on methods and meta-methods, where the latterare used to change and appropriately adapt the methods. A theorem and itsproof are both represented as a method and then reformulated by the set ofmetamethods presented in this paper.Our approach supports analogy-driven theorem proving at various levels ofabstraction and in principle makes it independent of the given and often acci-dental representation of the given theorems. Different methods can representfully instantiated proofs, subproofs, or general proof methods, and hence ourapproach also supports these three kinds of analogy respectively. By attachingappropriate justifications to meta-methods the analogical inference can oftenbe justified in the sense of Russell.This paper presents a model of analogy-driven proof plan construction andfocuses on empirically extracted meta-methods. It classifies and formally de-scribes these meta-methods and shows how to use them for an appropriatereformulation in automated analogy-driven theorem proving.

The hallmark of traditional Artificial Intelligence (AI) research is the symbolic representation and processing of knowledge. This is in sharp contrast to many forms of human reasoning, which to an extraordinary extent, rely on cases and (typical) examples. Although these examples could themselves be encoded into logic, this raises the problem of restricting the corresponding model classes to include only the intended models.There are, however, more compelling reasons to argue for a hybrid representa-tion based on assertions as well as examples. The problems of adequacy, availability of information, compactness of representation, processing complexity, and last but not least, results from the psychology of human reasoning, all point to the same conclusion: Common sense reasoning requires different knowledge sources and hybrid reasoning principles that combine symbolic as well as semantic-based inference. In this paper we address the problem of integrating semantic representations of examples into automateddeduction systems. The main contribution is a formal framework for combining sentential with direct representations. The framework consists of a hybrid knowledge base, made up of logical formulae on the one hand and direct representations of examples on the other, and of a hybrid reasoning method based on the resolution calculus. The resulting hybrid resolution calculus is shown to be sound and complete.

This case study examines in detail the theorems and proofs that are shownby analogy in a mathematical textbook on semigroups and automata, thatis widely used as an undergraduate textbook in theoretical computer scienceat German universities (P. Deussen, Halbgruppen und Automaten, Springer1971). The study shows the important role of restructuring a proof for findinganalogous subproofs, and of reformulating a proof for the analogical trans-formation. It also emphasizes the importance of the relevant assumptions ofa known proof, i.e., of those assumptions actually used in the proof. In thisdocument we show the theorems, the proof structure, the subproblems andthe proofs of subproblems and their analogues with the purpose to providean empirical test set of cases for automated analogy-driven theorem proving.Theorems and their proofs are given in natural language augmented by theusual set of mathematical symbols in the studied textbook. As a first step weencode the theorems in logic and show the actual restructuring. Secondly, wecode the proofs in a Natural Deduction calculus such that a formal analysisbecomes possible and mention reformulations that are necessary in order toreveal the analogy.

While most approaches to similarity assessment are oblivious of knowledge and goals, there is ample evidence that these elements of problem solving play an important role in similarity judgements. This paper is concerned with an approach for integrating assessment of similarity into a framework of problem solving that embodies central notions of problem solving like goals, knowledge and learning.