2018
Learning Others' Intentional Models in Multi-Agent Settings Using Interactive POMDPs
NeurIPS 2018poster
Interactive partially observable Markov decision processes (I-POMDPs) provide a principled framework for planning and acting in a partially observable, stochastic and multi-agent environment. It extends POMDPs to multi-agent settings by including models of other agents in the state space and forming…