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Spatial Reasoning with Propositional Logics
 Principles of Knowledge Representation and Reasoning: Proceedings of the 4th International Conference (KR94
, 1994
"... I present a method for reasoning about spatial relationships on the basis of entailments in propositional logic. Formalisms for representing topological and other spatial information (e.g. [2] [10] [11]) have generally employed the 1storder predicate calculus. Whilst this language is much more expr ..."
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Cited by 100 (16 self)
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I present a method for reasoning about spatial relationships on the basis of entailments in propositional logic. Formalisms for representing topological and other spatial information (e.g. [2] [10] [11]) have generally employed the 1storder predicate calculus. Whilst this language is much more expressive than 0order (propositional) calculi it is correspondingly harder to reason with. Hence, by encoding spatial relationships in a propositional representation automated reasoning becomes more effective. I specify representations in both classical and intuitionistic propositional logic, which  together with welldefined metalevel reasoning algorithms  provide for efficient reasoning about a large class of spatial relations. 1 INTRODUCTION This work has developed out of research done by Randell, Cui and Cohn (henceforth RCC) on formalising spatial and temporal concepts used in describing physical situations [11]. A set of classical 1storder logic axioms has been formulated in whi...
leanTAP: Lean Tableaubased Deduction
 Journal of Automated Reasoning
, 1995
"... . "prove((E,F),A,B,C,D) : !, prove(E,[FA],B,C,D). prove((E;F),A,B,C,D) : !, prove(E,A,B,C,D), prove(F,A,B,C,D). prove(all(H,I),A,B,C,D) : !, "+length(C,D), copyterm((H,I,C),(G,F,C)), append(A,[all(H,I)],E), prove(F,E,B,[GC],D). prove(A,,[CD],,) : ((A= (B); (A)=B)) ? (unify ..."
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Cited by 79 (11 self)
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. "prove((E,F),A,B,C,D) : !, prove(E,[FA],B,C,D). prove((E;F),A,B,C,D) : !, prove(E,A,B,C,D), prove(F,A,B,C,D). prove(all(H,I),A,B,C,D) : !, "+length(C,D), copyterm((H,I,C),(G,F,C)), append(A,[all(H,I)],E), prove(F,E,B,[GC],D). prove(A,,[CD],,) : ((A= (B); (A)=B)) ? (unify(B,C); prove(A,[],D,,)). prove(A,[EF],B,C,D) : prove(E,F,[AB],C,D)." implements a firstorder theorem prover based on freevariable semantic tableaux. It is complete, sound, and efficient. 1 Introduction The Prolog program listed in the abstract implements a complete and sound theorem prover for firstorder logic; it is based on freevariable semantic tableaux (Fitting, 1990). We call this lean deduction: the idea is to achieve maximal efficiency from minimal means. We will see that the above program is indeed very efficientnot although but because it is extremely short and compact. Our approach surely does not lead to a deduction system which is superior to highly sophisticated systems li...
Managing Inconsistent Specifications: Reasoning, Analysis, and Action
 ACM Transactions on Software Engineering and Methodology
, 1995
"... This article is a revised and extended version of our earlier work which appeared in Proceedings of the 3rd International Symposium on Requirements Engineering (1997), pages 78  86; Authors' addresses: A. Hunter, Department of Computer Science, University College London, Gower Street, London ..."
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Cited by 76 (22 self)
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This article is a revised and extended version of our earlier work which appeared in Proceedings of the 3rd International Symposium on Requirements Engineering (1997), pages 78  86; Authors' addresses: A. Hunter, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK; email: A.Hunter@cs.ucl.ac.uk; B. Nuseibeh, Department of Computing, Imperial College, 180 Queen's Gate, London, SW7 2BZ, UK; email: ban@doc.ic.ac.uk.
Deductive Composition of Astronomical Software from Subroutine Libraries
 In Proceedings 12th International Conference on Automated Deduction
"... Automated deduction techniques are being used in a system called Amphion to derive, from graphical specifications, programs composed from a subroutine library. The system has been applied to construct software for the planning and analysis of interplanetary missions. The library for that application ..."
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Cited by 73 (5 self)
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Automated deduction techniques are being used in a system called Amphion to derive, from graphical specifications, programs composed from a subroutine library. The system has been applied to construct software for the planning and analysis of interplanetary missions. The library for that application is a collection of subroutines written in FORTRAN77 at JPL to perform computations in solarsystem kinematics. An application domain theory has been developed that describes A preliminary version of this appears in the proceedings of the Twelfth International Conference on Automated Deduction, Nancy, France, June 1994, pages 341355. y fstickel,waldingerg@ai.sri.com z flowry, pressburger,underwoodg@ptolomy.arc.nasa.gov the procedures in a portion of the library, as well as some basic properties of solarsystem astronomy, in the form of firstorder axioms. Specifications are elicited from the user through a menudriven graphical user interface; space scientists have found the graph...
User Modeling: Recent Work, Prospects and Hazards
, 1993
"... User modeling has made considerable progress during its existence now of more than a decade. In this paper, a survey of recent developments will be presented, which concentrates on the modeling of a user's knowledge, plans, and preferences in a domain, on the exploitation of new sources of info ..."
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Cited by 70 (3 self)
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User modeling has made considerable progress during its existence now of more than a decade. In this paper, a survey of recent developments will be presented, which concentrates on the modeling of a user's knowledge, plans, and preferences in a domain, on the exploitation of new sources of information about the user, on issues of representation, inference and revision, on user modeling shell systems and servers, and on the verification of the practical utility of user models. Research trends and research deficiencies in these areas will be outlined, and potential risks described. 1. Introduction User modeling has made considerable progress during its existence now of more than a decade. Particularly in the last few years, the need for software systems to automatically adapt to their current users has been recognized in many application areas. Consequently, research on user modeling (which originated in the field of naturallanguage dialog systems) has spread into many disciplines whi...
Experiments with DiscriminationTree Indexing and Path Indexing for Term Retrieval
 JOURNAL OF AUTOMATED REASONING
, 1990
"... This article addresses the problem of indexing and retrieving firstorder predicate calculus terms in the context of automated deduction programs. The four retrieval operations of concern are to find variants, generalizations, instances, and terms that unify with a given term. Discriminationtree ..."
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Cited by 44 (0 self)
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This article addresses the problem of indexing and retrieving firstorder predicate calculus terms in the context of automated deduction programs. The four retrieval operations of concern are to find variants, generalizations, instances, and terms that unify with a given term. Discriminationtree indexing is reviewed, and several variations are presented. The pathindexing method is also reviewed. Experiments were conducted on large sets of terms to determine how the properties of the terms affect the performance of the two indexing methods. Results of the experiments are presented.
Experiments in automated deduction with condensed detachment
 in Proceedings of the Eleventh International Conference on Automated Deduction (CADE11), Lecture Notes in Artificial Intelligence
, 1992
"... This paper contains the results of experiments with several search strategies on 112 problems involving condensed detachment. The problems are taken from nine di erent logic calculi: three versions of the twovalued sentential calculus, the manyvalued sentential calculus, the implicational calculus ..."
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Cited by 23 (8 self)
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This paper contains the results of experiments with several search strategies on 112 problems involving condensed detachment. The problems are taken from nine di erent logic calculi: three versions of the twovalued sentential calculus, the manyvalued sentential calculus, the implicational calculus, the equivalential calculus, the R calculus, the left group calculus, and the right group calculus. Each problem was given to the theorem prover Otter and was run with at least three strategies: (1) a basic strategy, (2) a strategy with a more re ned method for selecting clauses on which to focus, and (3) a strategy that uses the re ned selection mechanism and deletes deduced formulas according to some simple rules. Two new features of Otter are also presented: the re ned method for selecting the next formula on which to focus, and a method for controlling memory usage. 1
Distributing Equational Theorem Proving
, 1993
"... In this paper we show that distributing the theorem proving task to several experts is a promising idea. We describe the team work method which allows the experts to compete for a while and then to cooperate. In the cooperation phase the best results derived in the competition phase are collected an ..."
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Cited by 22 (6 self)
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In this paper we show that distributing the theorem proving task to several experts is a promising idea. We describe the team work method which allows the experts to compete for a while and then to cooperate. In the cooperation phase the best results derived in the competition phase are collected and the less important results are forgotten. We describe some useful experts and explain in detail how they work together. We establish fairness criteria and so prove the distributed system to be both, complete and correct. We have implemented our system and show by nontrivial examples that drastical time speedups are possible for a cooperating team of experts compared to the time needed by the best expert in the team.
The "Limit" Domain
 In
, 1998
"... Proof planning is an application of AIplanning in mathematical domains. As opposed to planning for domains such as blocks world or transportation, the domain knowledge for mathematical domains is dicult to extract. Hence proof planning requires clever knowledge engineering and representation ..."
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Cited by 20 (11 self)
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Proof planning is an application of AIplanning in mathematical domains. As opposed to planning for domains such as blocks world or transportation, the domain knowledge for mathematical domains is dicult to extract. Hence proof planning requires clever knowledge engineering and representation of the domain knowledge. We think that on the one hand, the resulting domain denitions that include operators, supermethods, controlrules, and constraint solver are interesting in itself. On the other hand, they can provide ideas for modeling other realistic domains and for means of search reduction in planning. Therefore, we present proof planning and an exemplary domain used for planning proofs of socalled limit theorems that lead to proofs that were beyond the capabilities of other current proof planners and theorem provers. 1 Introduction While humans can cope with long and complex proofs and have strategies to avoid less promising proof paths, classical automated theore...