In three experiments, undergraduates rated autobiographical memories on scales derived from existing theories of memory. In multiple regression analyses, ratings of the degree to which subjects recollected (i.e., relived) their memories were predicted by visual imagery, auditory imagery, and emotions, whereas ratings of belief in the accuracy of their memories were predicted by knowledge of the...

The Dempster-Shafer theory of belief functions is an important approach to deal with uncertainty in AI. In the theory, belief functions are defined on Boolean algebras of events. In many applications of belief functions in real world problems, however, the objects that we manipulate is no more a Boolean algebra but a distributive lattice. In this paper, we extend the Dempster-Shafer theory to t...

Simple belief-revision tasks were defined by a giving subjects a conditional premise, (p—>q), a categorical premise, (p, for a modus-ponens belief-set, or ~q, for a modus tollens belief-set), and the associated inference (q or ~p, respectively). "New" information contradicted the initial inference (~ q or p, respectively). Subjects indicated their degree of belief in the conditional premise and...

This paper extends the applications of belief-networks to include the revision of belief commitments, i.e., the categorical acceptance of a subset of hypotheses which, together, constitute the most satisfactory explanation of the evidence at hand. A coherent model of non-monotonic reasoning is established and distributed algorithms for belief revision are presented. We show that, in singly conn...

This paper offers a new model of belief by embedding the Peircean account of belief into a formal dialogue system that uses argumentation schemes for practical reasoning and abductive reasoning. A belief is characterised as a stable proposition that is derived abductively by one agent in a dialogue from the commitment set (including commitments derived from actions and goals) of another agent. ...

By analyzing the relationships among chance, weight of evidence and degree of belief, it is shown that the assertion \chances are special cases of belief functions" and the assertion \Dempster's rule can be used to combine belief functions based on distinct bodies of evidence" together lead to an inconsistency in Dempster-Shafer theory. To solve this problem, some fundamental postulates of the ...

In the data mining field many clustering methods have been proposed, yet standard versions do not take into account uncertain databases. This paper deals with a new approach to cluster uncertain data by using a hierarchical clustering defined within the belief function framework. The main objective of the belief hierarchical clustering is to allow an object to belong to one or several clusters....

We formalize several ways of accounting, in the context of logically closed theories, for foundationalist intuitions that underlie change operations applying to belief bases. A positive and a negative concept of entrenchment is defined on the basis of the structure of a given, possibly prioritized belief base. Only the latter, more fine-grained concept proves to be appropriate for a successful ...

In this paper, we discuss the use of Bayesian belief networks as a tool for enhancing social network analysis. Traditional social network analysis (SNA) primarily uses graph-theoretic algorithms to compute properties of nodes in a network. However, these algorithms assume a degree of completeness and reliability of the social network data, which cannot always be assured. Applying Bayesian belie...

We develop a probabilistic criterion for belief expansion that is sensitive to the degree of contextual fit of the new information to our belief set as well as to the reliability of our information source. We contrast our approach with the success postulate in AGM-style belief revision and show how the idealizations in our approach can be relaxed by invoking Bayesian-Network models. ∗A slightly...