Elements of information theory
Elements of information theory
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
An Behavior-based Robotics
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
On the undecidability of probabilistic planning and related stochastic optimization problems
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
SIAM Journal on Control and Optimization
Conflicts in teamwork: hybrids to the rescue
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Multiagent coordination by Extended Markov Tracking
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Security in multiagent systems by policy randomization
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
An argumentation based approach for practical reasoning
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
On the complexity of solving Markov decision problems
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Dynamics based control with PSRs
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Coordination and multi-tasking using EMT
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
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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.