Traditional Dempster Shafer belief theory does not provide a simple method for judging the effect of statistical and probabilistic data on belief functions and vice versa. This puts belief theory in isolation from probability theory and hinders fertile cross-disciplinary developments, both from a theoretic and an application point of view. It can be shown that a bijective mapping exists between...

A belief system is an AI system which is concerned with the structural representation of beliefs, updating belief sets, reasoning with beliefs and the interaction between beliefs. A cognitive model of belief should be able to account for all of these things, and the subproblems which one encounters along the way. Several diierent approaches have been taken to solve various speciic problems for ...

People often discount evidence that contradicts their firmly held beliefs. However, little is known about the neural mechanisms that govern this behavior. We used neuroimaging to investigate the neural systems involved in maintaining belief in the face of counterevidence, presenting 40 liberals with arguments that contradicted their strongly held political and non-political views. Challenges to...

This paper provides an overview of distributed belief networks and how they can model engineering systems. In extending the usual single-processor computational model to multiple processors, several interesting problems arise, which must be solved for the successful implementation of a distributed belief network. In keeping with the distributed computational model, no single processor has infor...

Intelligent agents should regard other agents’ thoughts. If they can guess what you think without asking you, they should do so. We human beln~s notice other people’s thoughts without direct communication. One’s thoughts depend on one’s belief. However, reasoning about belief is very difficult because various factors affect belief and they often lead to inconsistency. This paper presents a simp...

Belief functions are a versatile class of mathematical models which include as cases both first order logic and probability statements. Although belief function models have a higher computational cost than probabilistic models, recent progress with graphical belief function models has made complex belief function models computationally feasible. This is especially true in models which have a na...

In this paper, I discuss whether different interpretations of the aim belief – both teleological and normative have resources to explain certain descriptive features suspended belief, or suspension. argue that despite recent efforts theorists extend these theories account for suspension, they ultimately fail. The implication is we must either develop alternative can abandon assumption ought be ...

Optimized Recovery (OR) adds belief base optimization to the traditional Recovery postulate—improving Recovery adherence without sacrificing adherence to the more accepted postulates or to the foundations approach. Reconsideration and belief liberation systems both optimize a knowledge base through consolidation of a chain of base beliefs; and recovered base beliefs are returned to the base. Th...