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...
Traditionally, qualitative simulation uses a global, state-based representation to describe the behavior of the modeled system. For larger, more complex systems this representatio...
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...
We show that a resealed constrainedness parameter provides the basis for accurate numerical models of search cost for both backtracking and local search algorithms. In the past, t...
Ian P. Gent, Ewan MacIntyre, Patrick Prosser, Toby...