Preferred answer sets for extended logic programs
Artificial Intelligence
Prioritized logic programming and its application to commonsense reasoning
Artificial Intelligence
Complexity and expressive power of logic programming
ACM Computing Surveys (CSUR)
Answer set programming and plan generation
Artificial Intelligence
Nested expressions in logic programs
Annals of Mathematics and Artificial Intelligence
Reasoning with Prioritized Defaults
LPKR '97 Selected papers from the Third International Workshop on Logic Programming and Knowledge Representation
Smodels - An Implementation of the Stable Model and Well-Founded Semantics for Normal LP
LPNMR '97 Proceedings of the 4th International Conference on Logic Programming and Nonmonotonic Reasoning
DEXA '95 Proceedings of the 6th International Conference on Database and Expert Systems Applications
Disjunctive logic programs with inheritance
Theory and Practice of Logic Programming
IEEE Intelligent Systems
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Conformant planning for domains with constraints: a new approach
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A 25-year perspective on logic programming
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In human-like reasoning it often happens that different conditions, partially alternative and hierarchically structured, are mentally grouped in order to derive some conclusion. The hierarchical nature of such knowledge concerns with the possible failure of a chance of deriving a conclusion and the necessity, instead of blocking the reasoning process, of activating a subordinate chance. Traditional logic programming (we refer here to Answer Set Programming) does not allow us to express such situations in a synthetic fashion, since different chances of deriving a conclusion must be distributed over different rules, and conditions enabling the switching among chances must be explicitly represented. We present a new language, relying on Answer Set Programming, which incorporates a new modality able to naturally express the above features. The merits of the proposal about the capability of representing knowledge are shown both by examples and by comparisons with other existing formalisms. A translation to plain ASP is finally provided in order to give a practical tool for computing our programs, since a number of optimized ASP evaluation systems are nowadays available.