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» Markov Decision Processes with Arbitrary Reward Processes
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AMAI
2004
Springer
15 years 7 months ago
A Framework for Sequential Planning in Multi-Agent Settings
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...
Piotr J. Gmytrasiewicz, Prashant Doshi
CONNECTION
2008
178views more  CONNECTION 2008»
15 years 2 months ago
Spoken language interaction with model uncertainty: an adaptive human-robot interaction system
Spoken language is one of the most intuitive forms of interaction between humans and agents. Unfortunately, agents that interact with people using natural language often experienc...
Finale Doshi, Nicholas Roy
ATAL
2011
Springer
14 years 1 months ago
Towards a unifying characterization for quantifying weak coupling in dec-POMDPs
Researchers in the field of multiagent sequential decision making have commonly used the terms “weakly-coupled” and “loosely-coupled” to qualitatively classify problems i...
Stefan J. Witwicki, Edmund H. Durfee
IPSN
2004
Springer
15 years 7 months ago
Estimation from lossy sensor data: jump linear modeling and Kalman filtering
Due to constraints in cost, power, and communication, losses often arise in large sensor networks. The sensor can be modeled as an output of a linear stochastic system with random...
Alyson K. Fletcher, Sundeep Rangan, Vivek K. Goyal
ICML
2007
IEEE
16 years 2 months ago
Constructing basis functions from directed graphs for value function approximation
Basis functions derived from an undirected graph connecting nearby samples from a Markov decision process (MDP) have proven useful for approximating value functions. The success o...
Jeffrey Johns, Sridhar Mahadevan