the proceedings contain 33 papers from logicprogramming and nonmonotonicreasoning : 7thinternationalconference LPNMR 2004. the topics discussed include: semantics for dynamic logicprogramming;probabilistic reason...
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the proceedings contain 33 papers from logicprogramming and nonmonotonicreasoning : 7thinternationalconference LPNMR 2004. the topics discussed include: semantics for dynamic logicprogramming;probabilistic reasoning with answer sets;answer sets: from constraint programming towards qualitative optimization;a logic of non-monotonic inductive definitions and its modularity properties, and reasoning about actions and change in answer set programming.
We show that the concepts of strong and uniform equivalence of logic programs can be generalized to an abstract algebraic setting of operators on complete lattices. Our results imply characterizations of strong and un...
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In this paper, we propose a formal framework for specifying rule replacements in nonmonotoniclogic programs within the answer-set programming paradigm. Of particular interest are replacement schemas retaining specifi...
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the proceedings contain 39 papers. the topics discussed include: nonmonotonicreasoning in FLORA-2;data integration and answer set programming;unfounded sets for disjunctive logic programs with arbitrary aggregates;on...
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ISBN:
(纸本)3540285385
the proceedings contain 39 papers. the topics discussed include: nonmonotonicreasoning in FLORA-2;data integration and answer set programming;unfounded sets for disjunctive logic programs with arbitrary aggregates;on modular translations and strong equivalence;guarded open answer set programming;external sources of computation for answer set solvers;answer sets for propositional theories;on the local closed-world assumption of data-sources;game-theoretic reasoning about actions in nonmonotic causal theories;some logical properties of nonmonotic causal theories;solving hard ASP programs efficiently;mode-directed fixed point computation;nested epistemic logic programs;and a social semantics for multi-agent systems.
We present a program logic, Lc, which modularly reasons about unstructured control flow in machine-language programs. Unlike previous program logics, the basic reasoning units in Lc are multiple-entry and multiple-exi...
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In most works on negotiation dialogues, agents are supposed to be ideally honest. However, there are many situations where such a behaviour cannot always be expected from the agents (e.g. advertising, political negoti...
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the application of Inductive logicprogramming to scientific datasets has been highly successful. Such applications have led to breakthroughs in the domain of interest and have driven the development of ILP systems. T...
the application of Inductive logicprogramming to scientific datasets has been highly successful. Such applications have led to breakthroughs in the domain of interest and have driven the development of ILP systems. the application of AI techniques to mathematical discovery tasks, however, has largely involved computer algebra systems and theorem provers rather than machine learning systems. We discuss here the application of the HR and Progol machine learning programs to discovery tasks in mathematics. While Progol is an established ILP system, HR has historically not been described as an ILP system. However, many applications of HR have required the production of first order hypotheses given data expressed in a Prolog-style manner, and the core functionality of HR can be expressed in ILP terminology. In Colton (2003), we presented the first partial description of HR as an ILP system, and we build on this work to provide a full description here. HR performs a novel ILP routine called Automated theory Formation, which combines inductive and deductive reasoning to form clausal theories consisting of classification rules and association rules. HR generates definitions using a set of production rules, interprets the definitions as classification rules, then uses the success sets of the definitions to induce hypotheses from which it extracts association rules. It uses third party theorem provers and model generators to check whether the association rules are entailed by a set of user supplied axioms. HR has been applied successfully to a number of predictive, descriptive and subgroup discovery tasks in domains of pure mathematics. We survey various applications of HR which have led to it producing number theory results worthy of journal publication, graph theory results rivalling those of the highly successful Graffiti program and algebraic results leading to novel classification theorems. To further promote mathematics as a challenge domain for ILP systems, we present
the application of Inductive logicprogramming to scientific datasets has been highly successful. Such applications have led to breakthroughs in the domain of interest and have driven the development of ILP systems. T...
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the application of Inductive logicprogramming to scientific datasets has been highly successful. Such applications have led to breakthroughs in the domain of interest and have driven the development of ILP systems. the application of AI techniques to mathematical discovery tasks, however, has largely involved computer algebra systems and theorem provers rather than machine learning systems. We discuss here the application of the HR and Progol machine learning programs to discovery tasks in mathematics. While Progol is an established ILP system, HR has historically not been described as an ILP system. However, many applications of HR have required the production of first order hypotheses given data expressed in a Prolog-style manner, and the core functionality of HR can be expressed in ILP terminology. In Colton (2003), we presented the first partial description of HR as an ILP system, and we build on this work to provide a full description here. HR performs a novel ILP routine called Automated theory Formation, which combines inductive and deductive reasoning to form clausal theories consisting of classification rules and association rules. HR generates definitions using a set of production rules, interprets the definitions as classification rules, then uses the success sets of the definitions to induce hypotheses from which it extracts association rules. It uses third party theorem provers and model generators to check whether the association rules are entailed by a set of user supplied axioms. HR has been applied successfully to a number of predictive, descriptive and subgroup discovery tasks in domains of pure mathematics. We survey various applications of HR which have led to it producing number theory results worthy of journal publication, graph theory results rivalling those of the highly successful Graffiti program and algebraic results leading to novel classification theorems. To further promote mathematics as a challenge domain for ILP systems, we present
FLORA-2 is an advanced knowledge representation system that integrates F-logic, HiLog, and Transaction logic. In this paper we give an overview of the theoretical foundations of the system and of some of the aspects o...
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ISBN:
(纸本)3540285385
FLORA-2 is an advanced knowledge representation system that integrates F-logic, HiLog, and Transaction logic. In this paper we give an overview of the theoretical foundations of the system and of some of the aspects of nonmonotonicreasoning in FLORA-2. these include scoped default negation, behavioral inheritance, and nonmonotonicity that stems from database dynamics.
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