We introduce point-based dynamic programming (DP) for decentralized partially observable Markov decision processes (DEC-POMDPs), a new discrete DP algorithm for planning strategie...
Given a model of a physical process and a sequence of commands and observations received over time, the task of an autonomous controller is to determine the likely states of the p...
We develop a new graphical representation for interactive partially observable Markov decision processes (I-POMDPs) that is significantly more transparent and semantically clear t...
Abstract. In the aftermath of a large-scale disaster, agents’ decisions derive from self-interested (e.g. survival), common-good (e.g. victims’ rescue) and teamwork (e.g. fire...
Abstract— We propose a planning algorithm that allows usersupplied domain knowledge to be exploited in the synthesis of information feedback policies for systems modeled as parti...
Salvatore Candido, James C. Davidson, Seth Hutchin...