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Abduction in Logic Programming
"... Abduction in Logic Programming started in the late 80s, early 90s, in an attempt to extend logic programming into a framework suitable for a variety of problems in Artificial Intelligence and other areas of Computer Science. This paper aims to chart out the main developments of the field over th ..."
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Cited by 624 (77 self)
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Abduction in Logic Programming started in the late 80s, early 90s, in an attempt to extend logic programming into a framework suitable for a variety of problems in Artificial Intelligence and other areas of Computer Science. This paper aims to chart out the main developments of the field over the last ten years and to take a critical view of these developments from several perspectives: logical, epistemological, computational and suitability to application. The paper attempts to expose some of the challenges and prospects for the further development of the field.
DecisionTheoretic Planning: Structural Assumptions and Computational Leverage
 JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
, 1999
"... Planning under uncertainty is a central problem in the study of automated sequential decision making, and has been addressed by researchers in many different fields, including AI planning, decision analysis, operations research, control theory and economics. While the assumptions and perspectives ..."
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Cited by 515 (4 self)
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Planning under uncertainty is a central problem in the study of automated sequential decision making, and has been addressed by researchers in many different fields, including AI planning, decision analysis, operations research, control theory and economics. While the assumptions and perspectives adopted in these areas often differ in substantial ways, many planning problems of interest to researchers in these fields can be modeled as Markov decision processes (MDPs) and analyzed using the techniques of decision theory. This paper presents an overview and synthesis of MDPrelated methods, showing how they provide a unifying framework for modeling many classes of planning problems studied in AI. It also describes structural properties of MDPs that, when exhibited by particular classes of problems, can be exploited in the construction of optimal or approximately optimal policies or plans. Planning problems commonly possess structure in the reward and value functions used to de...
Symbolic Dynamic Programming for Firstorder MDPs
 In IJCAI
, 2001
"... We present a dynamic programming approach for the solution of firstorder Markov decisions processes. This technique uses an MDP whose dynamics is represented in a variant of the situation calculus allowing for stochastic actions. It produces a logical description of the optimal value function and p ..."
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Cited by 148 (4 self)
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We present a dynamic programming approach for the solution of firstorder Markov decisions processes. This technique uses an MDP whose dynamics is represented in a variant of the situation calculus allowing for stochastic actions. It produces a logical description of the optimal value function and policy by constructing a set of firstorder formulae that minimally partition state space according to distinctions made by the value function and policy. This is achieved through the use of an operation known as decisiontheoretic regression. In effect, our algorithm performs value iteration without explicit enumeration of either the state or action spaces of the MDP. This allows problems involving relational fluents and quantification to be solved without requiring explicit state space enumeration or conversion to propositional form. 1
S.: Decisiontheoretic, highlevel agent programming in the situation calculus
 In: Proc. AAAI00, AAAI Press
, 2000
"... We propose a framework for robot programming which allows the seamless integration of explicit agent programming with decisiontheoretic planning. Specifically, the DTGolog model allows one to partially specify a control program in a highlevel, logical language, and provides an interpreter that, giv ..."
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Cited by 127 (5 self)
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We propose a framework for robot programming which allows the seamless integration of explicit agent programming with decisiontheoretic planning. Specifically, the DTGolog model allows one to partially specify a control program in a highlevel, logical language, and provides an interpreter that, given a logical axiomatization of a domain, will determine the optimal completion of that program (viewed as a Markov decision process). We demonstrate the utility of this model with results obtained in an office delivery robotics domain. 1
Lifted firstorder probabilistic inference
 In Proceedings of IJCAI05, 19th International Joint Conference on Artificial Intelligence
, 2005
"... Most probabilistic inference algorithms are specified and processed on a propositional level. In the last decade, many proposals for algorithms accepting firstorder specifications have been presented, but in the inference stage they still operate on a mostly propositional representation level. [Poo ..."
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Cited by 126 (8 self)
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Most probabilistic inference algorithms are specified and processed on a propositional level. In the last decade, many proposals for algorithms accepting firstorder specifications have been presented, but in the inference stage they still operate on a mostly propositional representation level. [Poole, 2003] presented a method to perform inference directly on the firstorder level, but this method is limited to special cases. In this paper we present the first exact inference algorithm that operates directly on a firstorder level, and that can be applied to any firstorder model (specified in a language that generalizes undirected graphical models). Our experiments show superior performance in comparison with propositional exact inference. 1
Logic Programming with Ordered Disjunction
 In Proceedings of AAAI02
, 2002
"... Logic programs with ordered disjunction (LPODs) combine ideas underlying Qualitative Choice Logic (Brewka, Benferhat, & Le Berre 2002) and answer set programming. Logic programming under answer set semantics is extended with a new connective called ordered disjunction. The new connective allows ..."
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Cited by 96 (8 self)
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Logic programs with ordered disjunction (LPODs) combine ideas underlying Qualitative Choice Logic (Brewka, Benferhat, & Le Berre 2002) and answer set programming. Logic programming under answer set semantics is extended with a new connective called ordered disjunction. The new connective allows us to represent alternative, ranked options for problem solutions in the heads of rules: A &times; B intuitively means: if possible A, but if A is not possible then at least B. The semantics of logic programs...
Probabilistic reasoning with answer sets
 In Proceedings of LPNMR7
, 2004
"... Abstract. We give a logic programming based account of probability and describe a declarative language Plog capable of reasoning which combines both logical and probabilistic arguments. Several nontrivial examples illustrate the use of Plog for knowledge representation. 1 ..."
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Cited by 91 (11 self)
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Abstract. We give a logic programming based account of probability and describe a declarative language Plog capable of reasoning which combines both logical and probabilistic arguments. Several nontrivial examples illustrate the use of Plog for knowledge representation. 1
Logic programs with annotated disjunctions
 In Proc. Int’l Conf. on Logic Programming
, 2004
"... Abstract. Current literature offers a number of different approaches to what could generally be called "probabilistic logic programming". These are usually based on Horn clauses. Here, we introduce a new formalism, Logic Programs with Annotated Disjunctions, based on disjunctive lo ..."
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Cited by 76 (5 self)
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Abstract. Current literature offers a number of different approaches to what could generally be called &quot;probabilistic logic programming&quot;. These are usually based on Horn clauses. Here, we introduce a new formalism, Logic Programs with Annotated Disjunctions, based on disjunctive logic programs. In this formalism, each of the disjuncts in the head of a clause is annotated with a probability. Viewing such a set of probabilistic disjunctive clauses as a probabilistic disjunction of normal logic programs allows us to derive a possible world semantics, more precisely, a probability distribution on the set of all Herbrand interpretations. We demonstrate the strength of this formalism by some examples and compare it to related work.
Abducing through negation as failure: stable models within the independent choice logic
 J. Log. Program
"... The independent choice logic (ICL) is part of a project to combine logic and decision/game theory into a coherent framework. The ICL has a simple possibleworlds semantics characterised by independent choices and an acyclic logic program that specifies the consequences of these choices. This paper g ..."
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Cited by 50 (8 self)
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The independent choice logic (ICL) is part of a project to combine logic and decision/game theory into a coherent framework. The ICL has a simple possibleworlds semantics characterised by independent choices and an acyclic logic program that specifies the consequences of these choices. This paper gives an abductive characterization of the ICL. The ICL is defined modeltheoretically, but we show that it is naturally abductive: the set of explanations of a proposition g is a concise description of the worlds in which g is true. We give an algorithm for computing explanations and show it is sound and complete with respect to the possibleworlds semantics. What is unique about this approach is that the explanations of the negation of g can be derived from the explanations of g. The use of probabilities over choices in this framework and going beyond acyclic logic programs are also discussed.
Probabilistic description logic programs
, 2006
"... Towards sophisticated representation and reasoning techniques that allow for probabilistic uncertainty in the Rules, Logic, and Proof layers of the Semantic Web, we present probabilistic description logic programs (or pdlprograms), which are a combination of description logic programs (or dlprog ..."
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Cited by 43 (15 self)
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Towards sophisticated representation and reasoning techniques that allow for probabilistic uncertainty in the Rules, Logic, and Proof layers of the Semantic Web, we present probabilistic description logic programs (or pdlprograms), which are a combination of description logic programs (or dlprograms) under the answer set semantics and the wellfounded semantics with Poole’s independent choice logic. We show that query processing in such pdlprograms can be reduced to computing all answer sets of dlprograms and solving linear optimization problems, and to computing the wellfounded model of dlprograms, respectively. Moreover, we show that the answer set semantics of pdlprograms is a refinement of the wellfounded semantics of pdlprograms. Furthermore, we also present an algorithm for query processing in the special case of stratified pdlprograms, which is based on a reduction to computing the canonical model of stratified dlprograms.