Abstract
In this paper we introduce Dynamics Based Control (DBC), an approach to planning and control of an agent in stochastic environments. Unlike existing approaches, which seek to optimize expected rewards (e.g., in Partially Observable Markov Decision Problems (POMDPs)), DBC optimizes system behavior towards specified system dynamics. We show that a recently developed planning and control approach, Extended Markov Tracking (EMT) is an instantiation of DBC. EMT employs greedy action selection to provide an efficient control algorithm in Markovian environments. We exploit this efficiency in a set of experiments that applied multi-target EMT to a class of area-sweeping problems (searching for moving targets). We show that such problems can be naturally defined and efficiently solved using the DBC framework, and its EMT instantiation.
Original language | English |
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Title of host publication | AAMAS'07 - Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems |
Publisher | Association for Computing Machinery (ACM) |
Pages | 790-797 |
Number of pages | 8 |
DOIs | |
State | Published - 2007 |
Event | 6th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS'07 - Honolulu, HI, United States Duration: 14 May 2008 → 18 May 2008 |
Conference
Conference | 6th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS'07 |
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Country/Territory | United States |
City | Honolulu, HI |
Period | 14/05/08 → 18/05/08 |
Keywords
- Robotics
- Dynamic programming