In this paper we discuss the difference between instruction and experience induced belief construction and its resistance to conflicting evidence. Experiment 1 is a succesful replication of the renewal effect with experience induced belief acquisition. In Experiment 2, it is shown that the level of belief acquisition that is obtained through experience is also obtained through instruction. Neve...

By studying the accessibility relations in logics of knowledge and belief, we reveal that almost all prominent nonmonotonic reasoning semantics can be characterized in terms of standard Kripke structures by altering the accessibility relation. This result not just extends the expressive power of the Kripke structure but also clearly demonstrates that the nonmonotonic reasoning is a reasoning ab...

Total preorders (tpos) are often used in belief revision to encode an agent’s strategy for revising its belief set in response to new information. Thus the problem of tpo-revision is of critical importance to the problem of iterated belief revision. Booth et al. [1] provide a useful framework for revising tpos by adding extra structure to guide the revision of the initial tpo, but this results ...

This article discusses a design pattern for building belief networks for application domains in which causal models are hard to construct. In this approach we pursue a modular belief network structure that is easily extended by the users themselves, while remaining reliable for decision support. The Hypothesis Management Framework proposed here is a pragmatic attempt to enable analysts and doma...

Due to the intractable nature of exact lifted inference, research has recently focused on the discovery of accurate and efficient approximate inference algorithms in Statistical Relational Models (SRMs), such as Lifted First-Order Belief Propagation. FOBP simulates propositional factor graph belief propagation without constructing the ground factor graph by identifying and lifting over redundan...

Existing methods of parameter and structure learning of Bayesian Networks (BNs) from a database assume that the database is complete. If there are missing values, they are assumed to be missing at random. This paper incorporates the concepts used in Dempster-Shafer theory of belief functions to learn both the parameters and structure of BNs. Instead of filling the missing values by their estima...

In previous work (Nescolarde-Selva and Usó-Doménech, 2014a,b) discussed the theory that complex belief systems have a topological structure. In this paper it is suggested that this structure is also fuzzy. They introduce the concepts of fuzzy sets in the context of beliefs (substantive and derived) , and between derived beliefs themselves. Also introduced are the concepts of fuzzy covering, fuz...

Causally insuucient structures (models with latent or hidden variables, or with confounding etc.) of joint probability distributions have been subject of intense study not only in statistics, but also in various AI systems. In AI, belief networks, being representations of joint probability distribution with an underlying directed acyclic graph structure, are paid special attention due to the fa...

Approximation structure plays an important role in inference on loopy graphs. As a tractable structure, tree approximations have been utilized in the variational method of Ghahramani & Jordan (1997) and the sequential projection method of Frey et al. (2000). However, belief propagation represents each factor of the graph with a product of single-node messages. In this paper, belief propagation ...

Based on a new demand — the commutativity of belief functions combination with refinement/coarsening of the frame of discernment — the role of the disjunctive rule of combination has increased. To compare the nature of this rule with a more frequent but also more controversional one, i.e. with Dempster’s rule, an algebraic analysis was used. The basic necessary definitions both from the Dempste...