A tutorial on learning with Bayesian networks
Learning in graphical models
Theory for coordinating concurrent hierarchical planning agents using summary information
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty
ACM Computing Surveys (CSUR)
Autonomous Agents and Multi-Agent Systems
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Optimizing information exchange in cooperative multi-agent systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Minimizing communication cost in a distributed Bayesian network using a decentralized MDP
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Communication for Improving Policy Computation in Distributed POMDPs
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
On-line Coordination: Event Interaction and State Communication between Cooperative Agents
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Collaborative learning agents supporting service network management
SOCASE'08 Proceedings of the 2008 AAMAS international conference on Service-oriented computing: agents, semantics, and engineering
Comparison of tightly and loosely coupled decision paradigms in multiagent expedition
International Journal of Approximate Reasoning
On-line coordination: Event interaction and state communication between cooperative agents
Web Intelligence and Agent Systems
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Techniques were developed in previous work for managing communication in a controlled satisficing manner in two layer distributed Bayesian Networks. DEC-MDPs were used to sequence the information transferred in order to guarantee the required confidence level. In this paper, we introduce multiple abstraction layers into the Distributed Bayesian Network as a way of carrying more useful information in transmitted data to further reduce the number of messages that need to be sent. An algorithm is developed to automatically generate appropriate abstraction data. Techniques are introduced to effectively incorporate this abstraction data set into the DEC-MDP framework. We show that the appropriate addition of abstraction data actions simplifies the DEC-MDP while reducing the expected communication cost. This work provides us with a formal view of the use of abstraction in agent cooperation and begins to give us an understanding of when the less abstract data needs to be transmitted.