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- Case-Based Reasoning (2)
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- Similarity Assessment (1)
- analogical reasoning (1)
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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.

Typical examples, that is, examples that are representative for a particular situationor concept, play an important role in human knowledge representation and reasoning.In real life situations more often than not, instead of a lengthy abstract characteriza-tion, a typical example is used to describe the situation. This well-known observationhas been the motivation for various investigations in experimental psychology, whichalso motivate our formal characterization of typical examples, based on a partial orderfor their typicality. Reasoning by typical examples is then developed as a special caseof analogical reasoning using the semantic information contained in the correspondingconcept structures. We derive new inference rules by replacing the explicit informa-tion about connections and similarity, which are normally used to formalize analogicalinference rules, by information about the relationship to typical examples. Using theseinference rules analogical reasoning proceeds by checking a related typical example,this is a form of reasoning based on semantic information from cases.

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.

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.

The amount of user interaction is the prime cause of costs in interactiveprogram verification. This paper describes an internal analogy techniquethat reuses subproofs in the verification of state-based specifications. Itidentifies common patterns of subproofs and their justifications in orderto reuse these subproofs; thus significant savings on the number of userinteractions in a verification proof are achievable.

Many mathematical proofs are hard to generate forhumans and even harder for automated theoremprovers. Classical techniques of automated theoremproving involve the application of basic rules, of built-in special procedures, or of tactics. Melis (Melis 1993)introduced a new method for analogical reasoning inautomated theorem proving. In this paper we showhow the derivational analogy replay method is relatedand extended to encompass analogy-driven proof planconstruction. The method is evaluated by showing theproof plan generation of the Pumping Lemma for con-text free languages derived by analogy with the proofplan of the Pumping Lemma for regular languages.This is an impressive evaluation test for the analogicalreasoning method applied to automated theorem prov-ing, as the automated proof of this Pumping Lemmais beyond the capabilities of any of the current auto-mated theorem provers.

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.

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.

This paper addresses the decomposition of proofs as a means of constructingmethods in plan-based automated theorem proving. It shows also, howdecomposition can beneficially be applied in theorem proving by analogy.Decomposition is also useful for human-style proof presentation. We proposeseveral decomposition techniques that were found to be useful in automatedtheorem proving and give examples of their application.