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System Description: Proof Planning in HigherOrder Logic with
 15th International Conference on Automated Deduction, volume 1421 of Lecture Notes in Artificial Intelligence
, 1998
"... Introduction Proof planning [4] is an approach to theorem proving which encodes heuristics for constructing mathematical proofs in a metatheory of methods. The Clam system, developed at Edinburgh [3], has been used for several years to develop proof planning, in particular proof plans for induction ..."
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Cited by 59 (8 self)
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Introduction Proof planning [4] is an approach to theorem proving which encodes heuristics for constructing mathematical proofs in a metatheory of methods. The Clam system, developed at Edinburgh [3], has been used for several years to develop proof planning, in particular proof plans for induction. It has become clear that many of the theoremproving tasks that we would like to perform are naturally higherorder. For example, an important technique called middleout reasoning [6] uses metavariables to stand for some unknown objects in a proof, to be instantiated as the proof proceeds. Domains such as the synthesis and verification of software and hardware systems, and techniques such as proof critics [7], benefit greatly from such middleout reasoning. Since in these domains the metavariables often become instantiated with terms of function type, reasoning with them is naturally higherorder, and higherorder unification is a
Invariant Discovery via Failed Proof Attempts
 In Proc. LOPSTR '98, LNCS 1559
, 1998
"... . We present a framework for automating the discovery of loop invariants based upon failed proof attempts. The discovery of suitable loop invariants represents a bottleneck for automatic verification of imperative programs. Using the proof planning framework we reconstruct standard heuristics fo ..."
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Cited by 18 (2 self)
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. We present a framework for automating the discovery of loop invariants based upon failed proof attempts. The discovery of suitable loop invariants represents a bottleneck for automatic verification of imperative programs. Using the proof planning framework we reconstruct standard heuristics for developing invariants. We relate these heuristics to the analysis of failed proof attempts allowing us to discover invariants through a process of refinement. 1 Introduction Loop invariants are a well understood technique for specifying the behaviour of programs involving loops. The discovery of suitable invariants, however, is a major bottleneck for automatic verification of imperative programs. Early research in this area [18, 24] exploited both theorem proving techniques as well as domain specific heuristics. However, the potential for interaction between these components was not fully exploited. The proof planning framework, in which we reconstruct the standard heuristics, couples ...
Higher order rippling in IsaPlanner
 Theorem Proving in Higher Order Logics 2004 (TPHOLs’04), LNCS 3223
, 2004
"... Abstract. We present an account of rippling with proof critics suitable for use in higher order logic in Isabelle/IsaPlanner. We treat issues not previously examined, in particular regarding the existence of multiple annotations during rippling. This results in an efficient mechanism for rippling th ..."
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Cited by 15 (7 self)
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Abstract. We present an account of rippling with proof critics suitable for use in higher order logic in Isabelle/IsaPlanner. We treat issues not previously examined, in particular regarding the existence of multiple annotations during rippling. This results in an efficient mechanism for rippling that can conjecture and prove needed lemmas automatically as well as present the resulting proof plans as Isar style proof scripts. 1
A Proof Planning Framework for Isabelle
, 2005
"... Proof planning is a paradigm for the automation of proof that focuses on encoding intelligence to guide the proof process. The idea is to capture common patterns of reasoning which can be used to derive abstract descriptions of proofs known as proof plans. These can then be executed to provide fully ..."
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Cited by 13 (9 self)
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Proof planning is a paradigm for the automation of proof that focuses on encoding intelligence to guide the proof process. The idea is to capture common patterns of reasoning which can be used to derive abstract descriptions of proofs known as proof plans. These can then be executed to provide fully formal proofs. This thesis concerns the development and analysis of a novel approach to proof planning that focuses on an explicit representation of choices during search. We embody our approach as a proof planner for the generic proof assistant Isabelle and use the Isar language, which is humanreadable and machinecheckable, to represent proof plans. Within this framework we develop an inductive theorem prover as a case study of our approach to proof planning. Our prover uses the difference reduction heuristic known as rippling to automate the step cases of the inductive proofs. The development of a flexible approach to rippling that supports its various modifications and extensions is the second major focus of this thesis. Here, our inductive theorem prover provides a context in which to evaluate rippling experimentally. This work results in an efficient and powerful inductive theorem prover for Isabelle as well as proposals for further improving the efficiency of rippling. We also draw observations in order
Automatic Verification of Functions with Accumulating Parameters
, 1999
"... Proof by mathematical induction plays a crucial role in reasoning about functional programs. A generalization step often holds the key to discovering an inductive proof. We present a generalization technique which is particularly applicable when reasoning about functional programs involving accumula ..."
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Cited by 9 (3 self)
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Proof by mathematical induction plays a crucial role in reasoning about functional programs. A generalization step often holds the key to discovering an inductive proof. We present a generalization technique which is particularly applicable when reasoning about functional programs involving accumulating parameters. We provide empirical evidence for the success of our technique and show how it is contributing to the ongoing development of a parallelising compiler for Standard ML. 1 Introduction and Motivations Functional programs, by their very nature, are highly amenable to formal methods of reasoning. This has been exploited within the formal verification community where the majority of theorem proving based tools have a strong functional bias (Boyer & Moore, 1979; Boyer & Moore, 1988; Bundy et al., 1990; Owre et al., 1992; Kapur & Zhang, 1995; ORA, 1996; Hutter & Sengler, 1996; Kaufmann & Moore, 1997). Proof by mathematical induction plays a crucial role in reasoning about recursiv...
Managing Structural Information by HigherOrder Colored Unification
 JOURNAL OF AUTOMATED REASONING
, 1999
"... Coloring terms (rippling) is a technique developed for inductive theorem proving which uses syntactic dierences of terms to guide the proof search. Annotations (colors) to symbol occurrences in terms are used to maintain this information. This technique has several advantages, e.g. it is highly go ..."
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Cited by 7 (5 self)
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Coloring terms (rippling) is a technique developed for inductive theorem proving which uses syntactic dierences of terms to guide the proof search. Annotations (colors) to symbol occurrences in terms are used to maintain this information. This technique has several advantages, e.g. it is highly goal oriented and involves little search. In this paper we give a general formalization of coloring terms in a higherorder setting. We introduce a simplytyped calculus with color annotations and present appropriate algorithms for the general, pre and pattern unification problems. Our work is a formal basis to the implementation of rippling in a higherorder setting which is required e.g. in case of middleout reasoning. Another application is in the construction of natural language semantics, where the color annotations rule out linguistically invalid readings that are possible using standard higherorder unification.
Proof planning for feature interactions: a preliminary report
 Proceedings of LPAR 2002, LNAI 2514
, 2002
"... Abstract. We report on an initial success obtained in investigating the Feature Interaction problem (FI) via proof planning. FIs arise as an unwanted/unexpected behaviour in large telephone networks and have recently attracted interest not only from the Computer Science community but also from the i ..."
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Cited by 6 (4 self)
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Abstract. We report on an initial success obtained in investigating the Feature Interaction problem (FI) via proof planning. FIs arise as an unwanted/unexpected behaviour in large telephone networks and have recently attracted interest not only from the Computer Science community but also from the industrial world. So far, FIs have been solved mainly via approximation plus finitestate methods (model checking being the most popular); in our work we attack the problem via proof planning in FirstOrder Linear Temporal Logic (FOLTL), therefore making use of no finitestate approximation or restricting assumption about quantification. We have integrated the proof planner λCLAM with an objectlevel FOLTL theorem prover called FTL, and have so far rediscovered a feature interaction in a basic (but far from trivial) example. 1
Ordinal Arithmetic: A Case Study for Rippling in a Higher Order Domain
 In TPHOLs’01, volume 2152 of LNCS
, 2001
"... This paper reports a case study in the use of proof planning in the context of higher order syntax. Rippling is a heuristic for guiding rewriting steps in induction that has been used successfully in proof planning inductive proofs using first order representations. Ordinal arithmetic provides a nat ..."
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Cited by 5 (0 self)
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This paper reports a case study in the use of proof planning in the context of higher order syntax. Rippling is a heuristic for guiding rewriting steps in induction that has been used successfully in proof planning inductive proofs using first order representations. Ordinal arithmetic provides a natural set of higher order examples on which transfinite induction may be attempted using rippling. Previously BoyerMoore style automation could not be applied to such domains. We demonstrate that a higherorder extension of the rippling heuristic is sufficient to plan such proofs automatically. Accordingly, ordinal arithmetic has been implemented in Clam, a higher order proof planning system for induction, and standard undergraduate text book problems have been successfully planned. We show the synthesis of a fixpoint for normal ordinal functions which demonstrates how our automation could be extended to produce more interesting results than the textbook examples tried so far.
CaseAnalysis for Rippling and Inductive Proof
"... Abstract. Rippling is a heuristic used to guide rewriting and is typically used for inductive theorem proving. We introduce a method to support caseanalysis within rippling. Like earlier work, this allows goals containing ifstatements to be proved automatically. The new contribution is that our me ..."
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Cited by 4 (1 self)
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Abstract. Rippling is a heuristic used to guide rewriting and is typically used for inductive theorem proving. We introduce a method to support caseanalysis within rippling. Like earlier work, this allows goals containing ifstatements to be proved automatically. The new contribution is that our method also supports caseanalysis on datatypes. By locating the caseanalysis as a step within rippling we also maintain the termination. The work has been implemented in IsaPlanner and used to extend the existing inductive proof method. We evaluate this extended prover on a large set of examples from Isabelle’s theory library and from the inductive theorem proving literature. We find that this leads to a significant improvement in the coverage of inductive theorem proving. The main limitations of the extended prover are identified, highlight the need for advances in the treatment of assumptions during rippling and when conjecturing lemmas. 1
Planning and patching proofs
 In Proceedings of AISC 2004, 7th International Conference on Artificial Intelligence and Symbolic Computation
, 2004
"... ..."