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63
Probabilistic Horn abduction and Bayesian networks
 Artificial Intelligence
, 1993
"... This paper presents a simple framework for Hornclause abduction, with probabilities associated with hypotheses. The framework incorporates assumptions about the rule base and independence assumptions amongst hypotheses. It is shown how any probabilistic knowledge representable in a discrete Bayesia ..."
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Cited by 297 (37 self)
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This paper presents a simple framework for Hornclause abduction, with probabilities associated with hypotheses. The framework incorporates assumptions about the rule base and independence assumptions amongst hypotheses. It is shown how any probabilistic knowledge representable in a discrete Bayesian belief network can be represented in this framework. The main contribution is in finding a relationship between logical and probabilistic notions of evidential reasoning. This provides a useful representation language in its own right, providing a compromise between heuristic and epistemic adequacy. It also shows how Bayesian networks can be extended beyond a propositional language. This paper also shows how a language with only (unconditionally) independent hypotheses can represent any probabilistic knowledge, and argues that it is better to invent new hypotheses to explain dependence rather than having to worry about dependence in the language. Scholar, Canadian Institute for Advanced...
A Foundation for Higherorder Concurrent Constraint Programming
, 1994
"... We present the flcalculus, a computational calculus for higherorder concurrent programming. The calculus can elegantly express higherorder functions (both eager and lazy) and concurrent objects with encapsulated state and multiple inheritance. The primitives of the flcalculus are logic variables ..."
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Cited by 61 (13 self)
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We present the flcalculus, a computational calculus for higherorder concurrent programming. The calculus can elegantly express higherorder functions (both eager and lazy) and concurrent objects with encapsulated state and multiple inheritance. The primitives of the flcalculus are logic variables, names, procedural abstraction, and cells. Cells provide a notion of state that is fully compatible with concurrency and constraints. Although it does not have a dedicated communication primitive, the flcalculus can elegantly express onetomany and manytoone communication. There is an interesting relationship between the flcalculus and the ßcalculus: The flcalculus is subsumed by a calculus obtained by extending the asynchronous and polyadic ßcalculus with logic variables. The flcalculus can be extended with primitives providing for constraintbased problem solving in the style of logic programming. A such extended flcalculus has the remarkable property that it combines firstor...
Logic Programming, Abduction and Probability: a topdown anytime algorithm for estimating prior and posterior probabilities
 New Generation Computing
, 1993
"... Probabilistic Horn abduction is a simple framework to combine probabilistic and logical reasoning into a coherent practical framework. The numbers can be consistently interpreted probabilistically, and all of the rules can be interpreted logically. The relationship between probabilistic Horn abducti ..."
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Cited by 39 (8 self)
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Probabilistic Horn abduction is a simple framework to combine probabilistic and logical reasoning into a coherent practical framework. The numbers can be consistently interpreted probabilistically, and all of the rules can be interpreted logically. The relationship between probabilistic Horn abduction and logic programming is at two levels. At the first level probabilistic Horn abduction is an extension of pure Prolog, that is useful for diagnosis and other evidential reasoning tasks. At another level, current logic programming implementation techniques can be used to efficiently implement probabilistic Horn abduction. This forms the basis of an "anytime" algorithm for estimating arbitrary conditional probabilities. The focus of this paper is on the implementation. Scholar, Canadian Institute for Advanced Research Logic Programming, Abduction and Probability 2 1 Introduction Probabilistic Horn Abduction [22, 21, 23] is a framework for logicbased abduction that incorporates proba...
Nonmonotonic Reasoning with Logic Programming
 LNAI
, 1993
"... Our purpose is to exhibit a modular systematic method of representing non monotonic reasoning problems with the Well Founded Semantics WFS of extended logic programs augmented with eXplicit negation (WFSX), augmented by its Contradiction Removal Semantics (CRSX) when needed. We apply this semantic ..."
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Cited by 38 (17 self)
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Our purpose is to exhibit a modular systematic method of representing non monotonic reasoning problems with the Well Founded Semantics WFS of extended logic programs augmented with eXplicit negation (WFSX), augmented by its Contradiction Removal Semantics (CRSX) when needed. We apply this semantics, and its contradiction removal semantics counterpart, to represent nonmonotonic reasoning problems. We show how to cast in the language of logic programs extended with explicit negation such forms of nonmonotonic reasoning as defeasible reasoning, abductive reasoning and hypothetical reasoning and apply them to such different domains of knowledge representation as hierarchies and reasoning about actions. We then abstract a modular systematic method of representing nonmonotonic problems in a logic programming semantics comprising two forms of negation avoiding some drawbacks of other proposals, with which we relate our work.
MiddleOut Reasoning for Logic Program Synthesis
 IN 10TH INTERNATIONAL CONFERENCE ON LOGIC PROGRAMMING (ICLP93
, 1993
"... We propose a novel approach to automating the synthesis of logic programs: Logic programs are synthesized as a byproduct of the planning of a verification proof. The approach is a twolevel one: At the object level, we prove program verification conjectures in a sorted, firstorder theory. The c ..."
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Cited by 31 (8 self)
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We propose a novel approach to automating the synthesis of logic programs: Logic programs are synthesized as a byproduct of the planning of a verification proof. The approach is a twolevel one: At the object level, we prove program verification conjectures in a sorted, firstorder theory. The conjectures are of the form 8args \Gamma\Gamma\Gamma\Gamma! : prog(args \Gamma\Gamma\Gamma\Gamma! ) $ spec(args \Gamma\Gamma\Gamma\Gamma! ). At the metalevel, we plan the objectlevel verification with an unspecified program definition. The definition is represented with a (secondorder) metalevel variable, which becomes instantiated in the course of the planning.
Setbased Analysis of Reactive Infinitestate Systems
, 1997
"... We present an automated abstract verification method for infinitestate systems specified by logic programs (which are a uniform and intermediate layer to which diverse formalisms such as transition systems, pushdown processes and while programs can be mapped). We establish connections between: logi ..."
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Cited by 27 (8 self)
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We present an automated abstract verification method for infinitestate systems specified by logic programs (which are a uniform and intermediate layer to which diverse formalisms such as transition systems, pushdown processes and while programs can be mapped). We establish connections between: logic program semantics and CTL properties, setbased program analysis and pushdown processes, and also between model checking and constraint solving, viz. theorem proving. We show that setbased analysis can be used to compute supersets of the values of program variables in the states that satisfy a given CTL property.
MiddleOut Reasoning for Synthesis and Induction
, 1995
"... We develop two applications of middleout reasoning in inductive proofs: Logic program synthesis and the selection of induction schemes. Middleout reasoning as part of proof planning was first suggested by Bundy et al [Bundy et al 90a]. Middleout reasoning uses variables to represent unknown te ..."
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Cited by 26 (11 self)
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We develop two applications of middleout reasoning in inductive proofs: Logic program synthesis and the selection of induction schemes. Middleout reasoning as part of proof planning was first suggested by Bundy et al [Bundy et al 90a]. Middleout reasoning uses variables to represent unknown terms and formulae. Unification instantiates the variables in the subsequent planning, while proof planning provides the necessary search control. Middleout reasoning is used for synthesis by planning the verification of an unknown logic program: The program body is represented with a metavariable. The planning results both in an instantiation of the program body and a plan for the verification of that program. If the plan executes successfully, the synthesized program is partially correct and complete. Middleout reasoning is also used to select induction schemes. Finding an appropriate induction scheme during synthesis is difficult, because the recursion of the program, which is un...
Residuation and Guarded Rules for Constraint Logic Programming
, 1993
"... Current constraint logic programming languages provide simplification for builtin constraints (e.g., arithmetic or boolean), but do not offer constraint propagation for userdefined predicates. We present two concepts, residuation and guarded rules, for obtaining userdefined constraint propagat ..."
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Cited by 26 (2 self)
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Current constraint logic programming languages provide simplification for builtin constraints (e.g., arithmetic or boolean), but do not offer constraint propagation for userdefined predicates. We present two concepts, residuation and guarded rules, for obtaining userdefined constraint propagation. Residuation is a
Model Elimination without Contrapositives and its Application to PTTP
 PROCEEDINGS OF CADE12, SPRINGER LNAI 814
, 1994
"... We give modifications of model elimination which do not necessitate the use of contrapositives. These restart model elimination calculi are proven sound and complete and their implementation by PTTP is depicted. The corresponding proof procedures are evaluated by a number of runtime experiments and ..."
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Cited by 22 (8 self)
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We give modifications of model elimination which do not necessitate the use of contrapositives. These restart model elimination calculi are proven sound and complete and their implementation by PTTP is depicted. The corresponding proof procedures are evaluated by a number of runtime experiments and they are compared to other well known provers. Finally we relate our results to other calculi, namely the connection method, modified problem reduction format and NearHorn Prolog.