A model for reasoning about persistence and causation
Computational Intelligence
From statistical knowledge bases to degrees of belief
Artificial Intelligence
Using temporal logics to express search control knowledge for planning
Artificial Intelligence
Sequential Optimality and Coordination in Multiagent Systems
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning to Cooperate via Policy Search
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
First international workshop on chance discovery
The Knowledge Engineering Review
Representation dependence in probabilistic inference
Journal of Artificial Intelligence Research
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This paper argues that chance (risk or opportunity) discovery is challenging, from a reasoning point of view, because it represents a dilemma for inductive reasoning. Chance discovery shares many features with the grue paradox. Consequently, Bayesian approaches represent a potential solution. The Bayesian solution evaluates alternative models generated using a temporal logic planner to manage the chance. Surprise indices are used in monitoring the conformity of the real world and the assessed probabilities. Game theoretic approaches are proposed to deal with multi-agent interaction in chance management.