Partially observable Markov decision process (POMDP) is commonly used to model a stochastic environment with unobservable states for supporting optimal decision making. Computing ...
In this paper we prove that the well-known correspondence between the forward-backward algorithm for hidden Markov models (HMMs) and belief propagation (BP) applied to HMMs can be...
This paper presents a computational model of negotiation based on Nebel's syntax-based belief revision. The model guarantees a unique bargaining solution for each bargaining ...
This paper presents a logic of knowledge, belief and certainty, which allows us to explicitly express the knowledge, belief and certainty of an agent. A computationally grounded m...
Kaile Su, Abdul Sattar, Guido Governatori, Qinglia...
ACT We present ongoing research on large-scale decision models in which there are many invested individuals. We apply our unique Bayesian belief aggregation approach to decision pr...
Kshanti A. Greene, Joe Michael Kniss, George F. Lu...