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A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A Bayesian network can be viewed as representing a factorization of a joint probability into the multiplication of a set of conditional probabilities. We present a notion of causal independence that enables one to further factorize the conditional… (More)

Bayesian belief networks have grown to prominence because they provide compact representations for many problems for which probabilistic inference is appropriate, and there are algorithms to exploit this compactness. The next step is to allow compact representations of the conditional probabilities of a variable given its parents. In this paper we present… (More)

Context-specific independence (CSI) refers to conditional independencies that are true only in specific contexts. It has been found useful in various inference algorithms for Bayesian networks. This paper studies the role of CSI in general. We provide a characterization of the computational leverages offered by CSI without referring to particular inference… (More)

The general problem of computing posterior probabilities in Bayesian networks is NP-hard (Cooper 1990). However eecient algorithms are often possible for particular applications by exploiting problem structures. It is well understood that the key to the materialization of such a possibility i s t o m a k e use of conditional independence and work with… (More)

This paper is about how to represent and solve decision problems in Bayesian decision theory (e.g. 6]). A general representation named decision networks is proposed based on innuence diagrams 10]. This new representation incorporates the idea, from Markov decision process (e.g. 5]), that a decision may be conditionally independent of certain pieces of… (More)

As innuence diagrams become a popular representational tool for decision analysis, innuence diagram evaluation attracts more and more research interest. In this article, we present a new, two{phase method for innuence diagram evaluation. In our method, an innuence diagram is rst mapped into a decision graph and then the analysis is carried out by evaluating… (More)

- References Bratman, M E Israel, D J Pollack, P R Cohen, M L Greenberg, D M Hart +4 others
- 2007

fact, is the existence of a " tension " between the stability that an agent's plans must have in order to provide a focus for the agent's deliberative reasoning processes, and the revocability that the same plans must also exhibit, given that they will only ever have been conceived with partial information about the agent's past, present, and future states.… (More)

This paper discusses how conflicts (as used by the consistency-based diagnosis community) can be adapted to be used in a search-based algorithm for computing prior and posterior probabilities in discrete Bayesian Networks. This is an " anytime " algorithm, that at any stage can estimate the probabilities and give an error bound. Whereas the most popular… (More)

This paper presents a simple framework for Horn clause abduction, with probabilities associated with hypotheses. It is shown how this representation can represent any probabilistic knowledge representable in a Bayesian belief network. The main contributions are in fi nding a relationship between logical and prob abilistic notions of evidential reasoning.… (More)

The logic is the independent choice logic (ICL) that allows 1 This paper introduces the independent choice logic, and in particular the "single agent with na ture" instance of the independent choice logic, namely I CLoT. This is a logical framework for decision making uncertainty that extends both logic programmin g and stochastic models such as influence… (More)