Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Temporal reasoning based on semi-intervals
Artificial Intelligence
A survey on temporal reasoning in artificial intelligence
AI Communications
Maintaining knowledge about temporal intervals
Communications of the ACM
Reasoning with BKBs – Algorithms and Complexity
Annals of Mathematics and Artificial Intelligence
A Possibility Theory-based Approach for Handling of Uncertain Relations Between Temporal Points
TIME '04 Proceedings of the 11th International Symposium on Temporal Representation and Reasoning
Temporal reasoning about fuzzy intervals
Artificial Intelligence
Journal of Artificial Intelligence Research
Temporal context representation and reasoning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Probabilistic temporal networks: A unified framework for reasoning with time and uncertainty
International Journal of Approximate Reasoning
Empirical evaluation of adaptive user modeling in a medical information retrieval application
UM'03 Proceedings of the 9th international conference on User modeling
Probabilistic temporal reasoning with endogenous change
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Temporal Bayesian Knowledge Bases - Reasoning about uncertainty with temporal constraints
Expert Systems with Applications: An International Journal
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Time is the key stimulus to change, causality and interaction which are the main components of a dynamic world. Therefore, the modeling of knowledge, especially in complex and dynamic domains like economics, sociology, and ecology, must incorporate the concept of time. Although there has been much research over the years on the representation of knowledge (causality, implication, and uncertainty) and on the representation of time, it has been a continuing challenge to unify them in a meaningful and useful fashion. In this paper, we propose a framework for reasoning under uncertainty with temporal constraints. The framework is extended from Bayesian knowledge-bases (BKBs), which represent knowledge in an "if-then" structure and represent uncertainty based on probability theory. By adding temporal constraints to BKBs, the framework provides a comprehensive model that incorporates the semantics of both time and uncertainty.