Handbook of logic in artificial intelligence and logic programming (vol. 3)
Can we enforce full compositionality in uncertainty calculi?
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Fuzzy relation equations and causal reasoning
Fuzzy Sets and Systems - Special issue: fuzzy relations, part 2
Abduction from logic program: semantics and complexity
Theoretical Computer Science
Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
Probabilistic Default Reasoning with Conditional Constraints
Annals of Mathematics and Artificial Intelligence
A Multi-adjoint Logic Approach to Abductive Reasoning
Proceedings of the 17th International Conference on Logic Programming
Preferred Answer Sets for Ordered Logic Programs
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
LPNMR '01 Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning
Stable Model Semantics for Probabilistic Deductive Databases
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Strong and Weak Constraints in Disjunctive Datalog
LPNMR '97 Proceedings of the 4th International Conference on Logic Programming and Nonmonotonic Reasoning
Planning as Satisfiability in Nondeterministic Domains
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Planning under Incomplete Knowledge
CL '00 Proceedings of the First International Conference on Computational Logic
A logic programming approach to knowledge-state planning: Semantics and complexity
ACM Transactions on Computational Logic (TOCL)
A logic programming framework for possibilistic argumentation with vague knowledge
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Conformant planning via symbolic model checking and heuristic search
Artificial Intelligence
Machine Learning
The DLV system for knowledge representation and reasoning
ACM Transactions on Computational Logic (TOCL)
Possibilistic uncertainty handling for answer set programming
Annals of Mathematics and Artificial Intelligence
Probabilistic reasoning with answer sets
Theory and Practice of Logic Programming
A meta-programming technique for debugging answer-set programs
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Epistemic reasoning in logic programs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Conformant planning via heuristic forward search: A new approach
Artificial Intelligence
Pstable Semantics for Logic Programs with Possibilistic Ordered Disjunction
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
Semantics for possibilistic disjunctive programs
LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
Potassco: The Potsdam Answer Set Solving Collection
AI Communications - Answer Set Programming
Weak and strong disjunction in possibilistic ASP
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
A core language for fuzzy answer set programming
International Journal of Approximate Reasoning
Fuzzy Equilibrium Logic: Declarative Problem Solving in Continuous Domains
ACM Transactions on Computational Logic (TOCL)
Possible and Necessary Answer Sets of Possibilistic Answer Set Programs
ICTAI '12 Proceedings of the 2012 IEEE 24th International Conference on Tools with Artificial Intelligence - Volume 01
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Although Answer Set Programming (ASP) is a powerful framework for declarative problem solving, it cannot in an intuitive way handle situations in which some rules are uncertain, or in which it is more important to satisfy some constraints than others. Possibilistic ASP (PASP) is a natural extension of ASP in which certainty weights are associated with each rule. In this paper we contrast two different views on interpreting the weights attached to rules. Under the first view, weights reflect the certainty with which we can conclude the head of a rule when its body is satisfied. Under the second view, weights reflect the certainty that a given rule restricts the considered epistemic states of an agent in a valid way, i.e. it is the certainty that the rule itself is correct. The first view gives rise to a set of weighted answer sets, whereas the second view gives rise to a weighted set of classical answer sets.