An introduction to fuzzy answer set programming
Annals of Mathematics and Artificial Intelligence
Handling uncertainty and defeasibility in a possibilistic logic setting
International Journal of Approximate Reasoning
Dealing Automatically with Exceptions by Introducing Specificity in ASP
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Answer Sets in a Fuzzy Equilibrium Logic
RR '09 Proceedings of the 3rd International Conference on Web Reasoning and Rule Systems
Possibilistic Well-Founded Semantics
MICAI '09 Proceedings of the 8th Mexican International Conference on 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
Pstable semantics for possibilistic logic programs
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
A possibilistic intuitionistic logic
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
Supporting decision making in urban wastewater systems using a knowledge-based approach
Environmental Modelling & Software
Answer set programming for computing decisions under uncertainty
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Weak and strong disjunction in possibilistic ASP
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
Aggregated Fuzzy Answer Set Programming
Annals of Mathematics and Artificial Intelligence
A core language for fuzzy answer set programming
International Journal of Approximate Reasoning
Possibilistic semantics for logic programs with ordered disjunction
FoIKS'10 Proceedings of the 6th international conference on Foundations of Information and Knowledge Systems
Possibilistic intermediate logic
International Journal of Advanced Intelligence Paradigms
A Possibilistic Argumentation Decision Making Framework with Default Reasoning
Fundamenta Informaticae - Latin American Workshop on Logic Languages, Algorithms and New Methods of Reasoning (LANMR)
Fuzzy Equilibrium Logic: Declarative Problem Solving in Continuous Domains
ACM Transactions on Computational Logic (TOCL)
Dealing with explicit preferences and uncertainty in answer set programming
Annals of Mathematics and Artificial Intelligence
Possibilistic reasoning in multi-context systems: preliminary report
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Using possibilistic logic for modeling qualitative decision: Answer Set Programming algorithms
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Fuzzy autoepistemic logic and its relation to fuzzy answer set programming
Fuzzy Sets and Systems
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In this work, we introduce a new framework able to deal with a reasoning that is at the same time non monotonic and uncertain. In order to take into account a certainty level associated to each piece of knowledge, we use possibility theory to extend the non monotonic semantics of stable models for logic programs with default negation. By means of a possibility distribution we define a clear semantics of such programs by introducing what is a possibilistic stable model. We also propose a syntactic process based on a fix-point operator to compute these particular models representing the deductions of the program and their certainty. Then, we show how this introduction of a certainty level on each rule of a program can be used in order to restore its consistency in case of the program has no model at all. Furthermore, we explain how we can compute possibilistic stable models by using available softwares for Answer Set Programming and we describe the main lines of the system that we have developed to achieve this goal.