Comparison of multiagent inference methods in multiply sectioned Bayesian networks
نویسندگان
چکیده
منابع مشابه
Comparison of multiagent inference methods in multiply sectioned Bayesian networks
As intelligent systems are being applied to larger, open and more complex problem domains, many applications are found to be more suitably addressed by multiagent systems. Multiply sectioned Bayesian networks provide one framework for agents to estimate what is the true state of a domain so that the agents can act accordingly. Existing methods for multiagent inference in multiply sectioned Baye...
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Multiply sectioned Bayesian networks (MSBNs) provide one framework for agents to estimate the state of a domain. Existing methods for multi-agent inference in MSBNs are based on linked junction forests (LJFs). The methods are extensions of message passing in junction trees for inference in singleagent Bayesian networks (BNs). We consider extending other inference methods in single-agent BNs to ...
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As Bayesian networks are applied to larger and more complex problem domains, search for exible modeling and more eecient inference methods is an ongoing eeort. Multiply sectioned Bayesian networks (MSBNs) extend the HUGIN inference for Bayesian networks into a coherent framework for exible modeling and distributed inference. Lazy propagation extends the Shafer-Shenoy and HUGIN inference methods...
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We consider multiple agents who s task is to determine the true state of a uncertain domain so they can act properly If each agent only has partial knowledge about the domain and local observation how can agents accomplish the task with the least amount of commu nication Multiply sectioned Bayesian networks MSBNs provide an e ective and exact framework for such a task but also impose a set of c...
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Multiply sectioned Bayesian networks (MSBNs) provide a coherent framework for probabilistic reasoning in cooperative multi-agent distributed interpretation systems (CMADISs). Previous work on MSBNs fo-cuses on the suuciency of MSBNs for representation and inference with uncertain knowledge in CMADISs. Since several representation choices were made in the formation of a MSBN, it appears unclear ...
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ژورنال
عنوان ژورنال: International Journal of Approximate Reasoning
سال: 2003
ISSN: 0888-613X
DOI: 10.1016/s0888-613x(03)00017-3