Multi-agent simulations are powerful tools to study complex systems. However, a major difficulty raised by these simulations concerns the design of the agent behavior. Indeed, when the agent behavior is lead by many conflicting criteria (needs and desires), its definition is very complex. In order to address this issue, we propose to use the belief theory to formalize the agent behavior. This f...

In this paper, we propose an environment that assists a scientist working in an experimental field to take decisions to revise his knowledge by giving him contextual explanations. These contextual explanations regard the experimental data and contextual presentation of the tools’ results. K e y w o r d s : knowledge revision, modelling, explanation, knowledge revision, context . _______________...

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 ...

Belief Revision is a ubiquitous process underlying many forms of intelligent behaviour. The AGM paradigm is a powerful framework for modeling and implementing belief revision systems based on the principle of Minimal Change; it provides a rich and rigorous foundation for computer-based belief revision architectures. Maxi-adjustment is a belief revision strategy for theory bases that can be impl...

Game trees are widely used as models of various decision-making situations. Empirical results with game-playing computer programs have led to the general belief that searching deeper on a game tree improves the quality of a decision. The surprising result of the research summarized in this paper is that there is an infinite class of game trees for which increasing the search depth does not impr...

This paper presents a rational approach to the representation and manipulation of imprecise degrees of belief in the framework of evidence theory. We adopt as a starting point the non probabilistic interpretation of belief functions provided by Smets' Transferable Belief Model, as well as previous generalizations of evidence theory allowing to deal with fuzzy propositions. We then introduce the...

This paper presents a rational approach to the representation and manipulation of imprecise degrees of belief in the framework of evidence theory. We adopt as a starting point the non probabilistic interpretation of belief functions provided by Smets’ Transferable Belief Model, as well as previous generalizations of evidence theory allowing to deal with fuzzy propositions. We then introduce the...

This article examines the idea that believing that events occurred in the past is a non-memorial decision that reflects underlying processes that are distinct from recollecting events. Research on autobiographical memory has often focused on events that are both believed to have occurred and remembered, thus tending to overlook the distinction between autobiographical belief and recollection. S...

Belief compression improves the tractability of large-scale partially observable Markov decision processes (POMDPs) by finding projections from high-dimensional belief space onto low-dimensional approximations, where solving to obtain action selection policies requires fewer computations. This paper develops a unified theoretical framework to analyse three existing linear belief compression app...

Loopy Belief propagation is an increasingly popular method of performing approximate inference on arbitrary graphical models. Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data mining. Influence diagrams provide a compact technique to...