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Titlebook: Logic Programming and Nonmonotonic Reasoning; 10th International C Esra Erdem,Fangzhen Lin,Torsten Schaub Conference proceedings 2009 Sprin

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Computing Stable Models via Reductions to Difference Logicmputing stable models of a logic program using a SAT solver presumes translating the program into a set of clauses which is the input form accepted by most SAT solvers. This leads to fairly complex super-linear translations. There are, however, interesting extensions to plain clausal propositional r
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A Module-Based Framework for Multi-language Constraint Modelingstrengths in a flexible way. In the framework a constraint model consists of modules with clear input/output interfaces. When combining modules, apart from the interface, a module is a black box whose internals are invisible to the outside world. Inside a module a chosen constraint language (approac
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Induction on Failure: Learning Connected Horn Theoriesthers that produce multiple clauses in response to a single seed example. A common denominator of these systems is a restricted hypothesis search space, within which each clause must individually explain some example ., or some member of an abductive explanation for .. This paper proposes a new IE a
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A First Order Forward Chaining Approach for Answer Set Computing to elaborate a first order logic program with default negation. In a preliminary step this program, with variables, is translated in an equivalent propositional one by a first tool: the grounder. Then, the propositional program is given to a second tool: the solver. This last one computes (if they
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Knowledge Qualification through Argumentation domains, and default property persistence in temporal domains. Emphasis in this work is placed on the ., central when dealing with default reasoning, and in any attempt to integrate different forms of such reasoning..Our framework can be viewed as offering a semantics to two natural problems: . tha
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How Flexible Is Answer Set Programming? An Experiment in Formalizing Commonsense in ASPrepresentation and reasoning techniques, including planning and adversarial reasoning. Most importantly, the riddle is difficult enough to make it unclear, at first analysis, whether and how Answer Set Programming or other formalisms can be used to solve it.
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On Reductive Semantics of Aggregates in Answer Set Programmingsitional formulas, which help us compare the semantics and understand their properties in terms of their propositional formula representations. We also present a generalization of semantics of aggregates without involving grounding, and define loop formulas for programs with aggregates guided by the reductive approach.
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Splitting a CR-Prolog Programg Set Theorem, one of the most useful theoretical results available for A-Prolog. The availability of the Splitting Set Theorem for CR-Prolog is expected to simplify significantly the proofs of the properties of CR-Prolog programs.
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Complexity of the Stable Model Semantics for Queries on Incomplete Databasesitions are easier for Datalog without than with negation. Under open-world interpretation, adding negation to non-recursive Datalog already leads to undecidability, but the complexity of certain answers for negation-free queries is the same as under closed-world interpretation.
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