abductive logic programming (ALP) and disjunctive logicprogramming (DLP) are two different extensions of logicprogramming. This paper investigates the relationship between ALP and DLP from the program transformation...
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abductive logic programming (ALP) and disjunctive logicprogramming (DLP) are two different extensions of logicprogramming. This paper investigates the relationship between ALP and DLP from the program transformation viewpoint. It is shown that the belief set semantics of an abductive program is expressed by the answer set semantics and the possible model semantics of a disjunctive program. In converse, the possible model semantics of a disjunctive program is equivalently expressed by the belief set semantics of an abductive program, while such a transformation is generally impossible for the answer set semantics. Moreover, it is shown that abductive disjunctive programs are always reducible to disjunctive programs both under the answer set semantics and the possible model semantics. These transformations are verified from the complexity viewpoint, The results of this paper turn out that ALP and DLP are just different ways of looking at the same problem if we choose an appropriate semantics. (C) 2000 Elsevier Science Inc. All rights reserved.
We present a method to compute abduction in logicprogramming. We translate an abductive framework into a normal logic program with integrity constraints and show the correspondence between generalized stable models a...
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We present a method to compute abduction in logicprogramming. We translate an abductive framework into a normal logic program with integrity constraints and show the correspondence between generalized stable models and stable models for the translation of the abductive framework. abductive explanations for an observation can be found from the stable models for the translated program by adding a special kind of integrity constraint for the observation. Then, we show a bottom-up procedure to compute stable models for a normal logic program with integrity constraints. The proposed procedure excludes the unnecessary construction of stable models on early stages of the procedure by checking integrity constraints during the construction and by deriving some facts from integrity constraints. Although a bottom-up procedure has the disadvantage of constructing stable models not related to an observation for computing abductive explanations in general, our procedure avoids the disadvantage by expecting which rule should be used for satisfaction of integrity constraints and starting bottom-up computation based on the expectation. This expectation is not only a technique to scope rule selection but also an indispensable part of our stable model construction because the expectation is done for dynamically generated constraints as well as the constraint for the observation. (C) 2000 Elsevier Science Inc. All rights reserved.
We investigate how abduction and induction can be inte- gratedinto a common learning framework. In particular, we consider anextension of Inductive logicprogramming (ILP) for the case in whichboth the background and ...
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We investigate how abduction and induction can be inte- gratedinto a common learning framework. In particular, we consider anextension of Inductive logicprogramming (ILP) for the case in whichboth the background and the target theories are abductivelogicprograms and where an abductive notion of entailment is used as thebasic coverage relation for learning. This extended learningframework has been called abductive Concept Learning (ACL).
We propose an approach for the integration of abduction and induction in logicprogramming. We define an abductive Learning Problem as an extended Inductive logicprogramming problem where both the background and targ...
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We propose an approach for the integration of abduction and induction in logicprogramming. We define an abductive Learning Problem as an extended Inductive logicprogramming problem where both the background and target theories are abductive theories and where abductive derivability is used as the coverage relation instead of deductive derivability. The two main benefits of this integration are the possibility of learning in presence of incomplete knowledge and the increased expressive power of the background and target theories. We present the system LAP (Learning abductive Programs) that is able to solve this extended learning problem and we describe, by means of examples, four different learning tasks that can be performed by the system: learning from incomplete knowledge, learning rules with exceptions, learning from integrity constraints and learning recursive predicates. (C) 1999 Elsevier Science Inc. All rights reserved.
In the commonsense reasoning, priorities among rules are often required to be found out in order to derive the desired conclusion as a theorem of the reasoning. In this paper, first we present the bottom-up and top-do...
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In the commonsense reasoning, priorities among rules are often required to be found out in order to derive the desired conclusion as a theorem of the reasoning. In this paper, first we present the bottom-up and top-down abduction procedures to compute skeptical explanations and secondly show that priorities of circumscription to infer a desired theorem can be abduced as a skeptical explanation in abduction. In our approach, the required priorities can be computed based on the procedure to compute skeptical explanations provided in this paper as well as Wakaki and Satoh's method of compiling circumscription into extended logic programs. The method, for example, enables us to automatically find the adequate priority w.r.t. the Yale Shooting Problem to express a human natural reasoning in the framework of circumscription.
Recently, Gelfond and Lifschitz presented a formal language for representing incomplete knowledge on actions and states, and a sound translation from this language to extended logicprogramming. We present an alternat...
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Recently, Gelfond and Lifschitz presented a formal language for representing incomplete knowledge on actions and states, and a sound translation from this language to extended logicprogramming. We present an alternative translation to abductive logic programming with integrity constraints and prove the soundness and completeness. In addition, we show how an abductive procedure can be used, not only for explanation, but also for deduction and proving satisfiability under uncertainty. From a more general perspective, this work can be viewed as a-successful-experiment in the declarative representation of and automated reasoning on incomplete knowledge using abductive logic programming.
This article presents the theory and implementation of an artificial intelligence planner, CHICA. CHICA is a non-linear, domain independent planner based on techniques of computational logic. The representation langua...
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This article presents the theory and implementation of an artificial intelligence planner, CHICA. CHICA is a non-linear, domain independent planner based on techniques of computational logic. The representation language of the planner is Horn clause logic which is used to model event calculus, a logical theory of changing properties over time. The reasoning component is an abductive extension of SLDNF resolution for generating assumptions to prove a given goal. In event calculus, this procedure generates a plan of events and temporal relations necessary to prove the planning goal. CHICA uses domain contraints and techniques from contraint logicprogramming to efficiently implement inequality, as well as a specialized module to evaluate temporal relations. CHICA's generic search algorithm lets the implementor of a planning domain define a particular search strategy and specify domain heuristics to prune the search space. CHICA has solved a number of planning problems successfully: multiple robot block world problems, the assembly of a flashlight, and a room decoration problem. Extensions to classical AI-planning can be solved within the same framework, such as plan execution and replanning.
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