193 citations · 950 across the 37 of their papers we have counts for
18 papers · 1 filter
Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning
Zun Li, Marc Lanctot, Kevin R. McKee +7
Opponent modeling methods typically involve two crucial steps: building a belief distribution over opponents' strategies, and exploiting this opponent model by playing a best respo…
Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence (1992)
Bruce D'Ambrosio, Didier Dubois, Philippe Smets +1
This is the Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence, which was held in Stanford, CA, July 17-19, 1992
Qualitative Probabilistic Networks for Planning Under Uncertainty
Michael P. Wellman
Bayesian networks provide a probabilistic semantics for qualitative assertions about likelihood. A qualitative reasoner based on an algebra over these assertions can derive further…
The Role of Calculi in Uncertain Inference Systems
Michael P. Wellman, David Heckerman
Much of the controversy about methods for automated decision making has focused on specific calculi for combining beliefs or propagating uncertainty. We broaden the debate by (1) e…
Exploiting Functional Dependencies in Qualitative Probabilistic Reasoning
Michael P. Wellman
Functional dependencies restrict the potential interactions among variables connected in a probabilistic network. This restriction can be exploited in qualitative probabilistic rea…
State-space Abstraction for Anytime Evaluation of Probabilistic Networks
Michael P. Wellman, Chao-Lin Liu
One important factor determining the computational complexity of evaluating a probabilistic network is the cardinality of the state spaces of the nodes. By varying the granularity…