We investigate search problems under risk in statespace graphs, with the aim of finding optimal paths for risk-averse agents. We consider problems where uncertainty is due to the...
Most models of decision-making in neuroscience assume an infinite horizon, which yields an optimal solution that integrates evidence up to a fixed decision threshold; however, u...
Abstract— This paper proposes a simulation-based active policy learning algorithm for finite-horizon, partially-observed sequential decision processes. The algorithm is tested i...
Ruben Martinez-Cantin, Nando de Freitas, Arnaud Do...
In this paper some initialwork towards a new approach to qualitative reasoning under uncertainty is presented. This method is not only applicable to qualitative probabilistic reas...
Accurate knowledge of the effect of parameter uncertainty on process design and operation is essential for optimal and feasible operation of a process plant. Existing approaches de...