Partially observable Markov decision processes (POMDPs) allow one to model complex dynamic decision or control problems that include both action outcome uncertainty and imperfect ...
Automated problem solving is viewed typically as the expenditure of computation to solve one or more problems passed to a reasoning system. In response to each problem received, e...
When dealing with signals from complex environments, where multiple time-dependent signal signatures can interfere with each other in stochastically unpredictable ways, traditiona...
In this paper, we propose a stochastic version of a general purpose functional programming language as a method of modeling stochastic processes. The language contains random choi...
The situation calculus is a popular technique for reasoning about action and change. However, its restriction to a firstorder syntax and pure deductive reasoning makes it unsuitab...
Fahiem Bacchus, Adam J. Grove, Joseph Y. Halpern, ...