This paper describes a current research project investigating belief revision in intelligent systems by modelling the librarian in interaction with a literature-seeking user. The work is designed both to test a theory of agent behaviour based on belief revision proposed by Galliers, and to evaluate a model of the librarian developed by Belkin, Brooks and Daniels, through computational implement...

Since the development of loopy belief propagation, there has been considerable work on advancing the state of the art for approximate inference over distributions defined on discrete random variables. Improvements include guarantees of convergence, approximations that are provably more accurate, and bounds on the results of exact inference. However, extending these methods to continuous-valued ...

In the conclusion of [7] Halpern and Moses expressed their interest in a logical system in which one could talk about knowledge and belief (and belief about knowledge, knowledge about belief and so on), We investigate such systems. In the first part of the paper knowledge and belief, without time, are considered. Common knowledge and common belief are defined and compared. A logical system and ...

In this paper we address the problem of trajectory planning with imperfect state information. In many real-world domains, the position of a mobile agent cannot be known perfectly; instead, the agent maintains a probabilistic belief about its position. Planning in these domains requires computing the best trajectory through the space of possible beliefs. We show that planning in belief space can...

This paper describes a current research project investigating belief revision in intelligent systems by modelling the librarian in interaction with a literature-seeking user. The work is designed both to test a theory of agent behaviour based on belief revision proposed by Galliers, and to evaluate a model of the librarian developed by Belkin, Brooks and Daniels, through computational implement...

POMDPs provide a principled framework for sequential planning in single agent settings. An extension of POMDPs to multiagent settings, called interactive POMDPs (I-POMDPs), replaces POMDP belief spaces with interactive hierarchical belief systems which represent an agent’s belief about the physical world, about beliefs of the other agent(s), about their beliefs about others’ beliefs, and so on....

A Bayesain belief network is a graphical representation of the underlying probabilistic relationships of a complex system. These networks are used for reasoning with uncertainty, such as in decision support systems. This requires probabilistic inference with Bayesian belief networks. Simulation schemes for probabilistic inference with Bayesian belief networks offer many advantages over exact in...