This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state spac...
RVRL (Rule Value Reinforcement Learning) is a new algorithm which extends an existing learning framework that models the environment of a situated agent using a probabilistic rule...
A helicopter agent has to plan trajectories to track multiple ground targets from the air. The agent has partial information of each target's pose, and must reason about its u...
Modular programming has the usual benefits associated with structured programming, information hiding and reusability, but also has additional benefits to offer when applied in ...
— This paper presents a strategy for planning robot motions in dynamic, cluttered, and uncertain environments. Successful and efficient operation in such environments requires r...