PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Integrating Distributed Bayesian Inference and Reinforcement Learning for Sensor Management
C. Grappiolo, S. Whiteson, G. Pavlin and B. Bakker
In: SIMPAR 200, 6-7 July 2009, Seattle, Washington, USA.

Abstract

This paper introduces a sensor management approach that integrates distributed Bayesian inference (DBI) and reinforcement learning (RL). DBI is implemented using distributed perception networks (DPNs), a multiagent approach to performing efficient inference, while RL is used to automatically discover a mapping from the beliefs generated by the DPNs to the actions that enable active sensors to gather the most useful observations. The resulting method is evaluated on a simulation of a chemical leak localization task and the results demonstrate 1) that the integrated approach can learn policies that perform effective sensor management, 2) that inference based on a correct observation model, which the DPNs make feasible, is critical to performance, and 3) that the system scales to larger versions of the task.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6608
Deposited By:Christof Monz
Deposited On:08 March 2010