Artificial Intelligence
Resolution principles in possibilistic logic
International Journal of Approximate Reasoning
Artificial Intelligence - Special issue on knowledge representation
A general temporal model supporting duration reasoning
AI Communications
Temporal inference with a point-based interval algebra
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Fundamenta Informaticae - Special issue: logics for artificial intelligence
A model and a language for the fuzzy representation and handling of time
Fuzzy Sets and Systems
Handbook of logic in artificial intelligence and logic programming (vol. 3)
A survey on temporal reasoning in artificial intelligence
AI Communications
Spatio-Temporal Reasoning and Linear Inequalitites
Spatio-Temporal Reasoning and Linear Inequalitites
Modeling of, and reasoning with recurrent events with imprecise durations
IEA/AIE '00 Proceedings of the 13th international conference on Industrial and engineering applications of artificial intelligence and expert systems: Intelligent problem solving: methodologies and approaches
A Temporal Many-Valued Logic for Real Time Control Systems
AIMSA '00 Proceedings of the 9th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
A Proof Procedure for Possibilistic Logic Programming with Fuzzy Constants
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Learning event durations from event descriptions
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Exploring Extensions of Possibilistic Logic over Gödel Logic
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Temporal Features in Biological Warfare
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
Modeling and learning vague event durations for temporal reasoning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Qualitative temporal reasoning about vague events
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The algebra IAfuz: a framework for qualitative fuzzy temporal reasoning
Artificial Intelligence
Decision making rules for assessment of students' knowledge
CATE '07 Proceedings of the 10th IASTED International Conference on Computers and Advanced Technology in Education
Computational treatment of temporal notions: the CTTN-system
Proceedings of the 2005 international conference on Annotating, extracting and reasoning about time and events
Extending possibilistic logic over Gödel logic
International Journal of Approximate Reasoning
A complete calculus for possibilistic logic programming with fuzzy propositional variables
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Annotating and learning event durations in text
Computational Linguistics
Discriminating exanthematic diseases from temporal patterns of patient symptoms
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
Computational treatment of temporal notions: the CTTN–System
PPSWR'05 Proceedings of the Third international conference on Principles and Practice of Semantic Web Reasoning
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In this paper we propose a propositional temporal language based on fuzzy temporal constraints which turns out to be expressive enough for domains -like many coming from medicine- where knowledge is of propositional nature and an explicit handling of time, imprecision and uncertainty are required. The language is provided with a natural possibilistic semantics to account for the uncertainty issued by the fuzziness of temporal constraints. We also present an inference system based on specific rules dealing with the temporal constraints and a general fuzzy modus ponens rule whereby behaviour is shown to be sound. The analysis of the different choices as fuzzy operators leads us to identify the well-known Lukasiewicz implication as very appropriate to define the notion of possibilistic entailment, an essential element of our inference system.