Mathematics, Systems and Robotics Seminar 

07/12/2007, 15:00 — 16:00 — Conference Room, Instituto de Sistemas e Robótica, North Tower, 7th floor, IST
Matthijs Spaan, IST/ISR

Planning in partially observable environments

Planning is the process of computing a sequence of actions that fulfill a given task as well as possible. It is a crucial part of any intelligent agent; human, robot or software agent alike. In real-world planning an agent has to deal with several sources of uncertainty. First of all, the agent might be uncertain regarding the exact consequence of executing a particular action. Furthermore, the agent's sensors may be noisy or provide only a limited view of the environment. Partially observable Markov decision processes (POMDPs) provide a mathematical framework for acting optimally in such partially observable and stochastic environments. In this talk, we will introduce POMDPs and give an overview of available optimal and sub-optimal solution methods.