Communication for Improving Policy Computation in Distributed POMDPs

Distributed Partially Observable Markov Decision Problems (POMDPs) are emerging as a popular approach for modeling multiagent teamwork where a group of agents work together to jointly maximize a reward function. Since the problem of finding the optimal joint policy for a distributed POMDP has been s...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Autonomous Agents and Multiagent Systems: Proceedings, 3rd International Joint Conference, New York City, New York, 2004. s. 1098 - 1105
Hlavní autori:	Nair, Ranjit, Roth, Maayan, Yohoo, Makoto
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	Washington, DC, USA IEEE Computer Society 19.07.2004
Edícia:	ACM Conferences
Predmet:	Computing methodologies > Artificial intelligence > Distributed artificial intelligence > Cooperation and coordination Computing methodologies > Artificial intelligence > Distributed artificial intelligence > Multi-agent systems Mathematics of computing > Probability and statistics > Probabilistic representations > Markov networks Mathematics of computing > Probability and statistics > Stochastic processes > Markov processes Theory of computation > Theory and algorithms for application domains > Machine learning theory > Markov decision processes
ISBN:	9781581138641, 1581138644
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!