MULTIPLY SECTIONED BAYESIAN NETWORKS AND JUNCTION FORESTS FOR LARGE KNOWLEDGE-BASED SYSTEMS
نویسندگان
چکیده
منابع مشابه
Inference in Multiply Sectioned Bayesian Networks with Lazy Propagation and Linked Junction Forests
Lazy propagation reduces the space complexity. MSBNs extend BNs to multiagent. To combine the benefits of the two, a framework was proposed earlier to apply lazy propagation to inference in MSBNs. We propose an alternative framework with a simpler compiled structure.
متن کاملJustifying Multiply Sectioned Bayesian Networks
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...
متن کاملWhat Necessitate Multiply Sectioned Bayesian Networks?
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 ...
متن کاملComparing Hierarchical Markov Networks and Multiply Sectioned Bayesian Networks
Multiply sectioned Bayesian networks (MSBNs) were originally proposed as a modular representation of uncertain knowledge by sectioning a large Bayesian network (BN) into smaller units. More recently, hierarchical Markov networks (HMNs) were developed in part as an hierarchical representation of the flat BN. In this paper, we compare the MSBN and HMN representations. The MSBN representation does...
متن کاملPractical Issues in Modeling Large Diagnostic Systems with Multiply Sectioned Bayesian Networks
As Bayesian networks become widely accepted as a normative formalism for diagnosis based on probabilistic knowledge, they are applied to increasingly larger problem domains. These large projects demand a systematic approach to handle the complexity in knowledge engineering. The needs include modularity in representation, distribution in computation, as well as coherence in inference. Multiply S...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational Intelligence
سال: 1993
ISSN: 0824-7935,1467-8640
DOI: 10.1111/j.1467-8640.1993.tb00306.x