Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
The Complexity of Decentralized Control of Markov Decision Processes
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
The Factored Frontier Algorithm for Approximate Inference in DBNs
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Temporally Invariant Junction Tree for Inference in Dynamic Bayesian Network
AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Optimizing information exchange in cooperative multi-agent systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Algorithms for sequential decision-making
Algorithms for sequential decision-making
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Loopy Belief Propagation: Convergence and Effects of Message Errors
The Journal of Machine Learning Research
Distributed Interpretation: A Model and Experiment
IEEE Transactions on Computers
Taming decentralized POMDPs: towards efficient policy computation for multiagent settings
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The BATmobile: towards a Bayesian automated taxi
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Agent-based distributed intrusion alert system
IWDC'04 Proceedings of the 6th international conference on Distributed Computing
Paper: Multiply sectioned Bayesian networks for neuromuscular diagnosis
Artificial Intelligence in Medicine
A simple graphical approach for understanding probabilistic inference in Bayesian networks
Information Sciences: an International Journal
Comparison of tightly and loosely coupled decision paradigms in multiagent expedition
International Journal of Approximate Reasoning
Distributed Multi-agent Reasoning with Layered Context Modeling and Priority
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Multiagent bayesian forecasting of structural time-invariant dynamic systems with graphical models
International Journal of Approximate Reasoning
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Cooperative multiagent probabilistic inference can be applied in areas such as building surveillance and complex system diagnosis to reason about the states of the distributed uncertain domains. In the static cases, multiply sectioned Bayesian networks (MSBNs) have provided a solution when interactions within each agent are structured and those among agents are limited. However, in the dynamic cases, the agents' inference will not guarantee exact posterior probabilities if each agent evolves separately using a single agent dynamic Bayesian network (DBN). Nevertheless, due to the discount of the past, we may not have to use the whole history of a domain to reason about its current state. In this paper, we propose to reason about the state of a distributed dynamic domain period by period using an MSBN. To reduce the influence of the ignored history on the posterior probabilities to a minimum, we propose to observe as many observable variables as possible in the modeled history. Due to the limitations of the problem domains, it could be very costly to observe all observable variables. We present a distributed algorithm to compute all observable variables that are relevant to our concerns. Experimental results on the relationship between the computational complexity and the length of the represented history, and effectiveness of the approach are presented.