Belief revision conditionals: basic iterated systems

Belief Revision Conditionals: Basic Iterated Systems

Iterated Belief Revision, Revised

A central aspect of commonsense reasoning is to sensibly revise one’s beliefs about the world in the presence of new information. The AGM postulates for belief revision, augmented by the DP postulates for iterated belief revision, provide generally accepted criteria for the design of operators by which intelligent agents adapt their beliefs incrementally to new information. These postulates alo...

Conditionals, Probability, and Belief Revision (preliminary Version) Conditionals, Probability, and Belief Revision (preliminary Version)*

A famous result obtained in the mid-seventies by David Lewis shows that a straightforward interpretation of probabilities of conditionals as conditional probabilities runs into serious trouble. In this paper we try to circumvent this trouble by defining extensions of probability functions, called conditional probability functions. We further defend the position that rational partial beliefs (ab...

A Conditional Logic for Iterated Belief Revision

In this paper we propose a conditional logic IBC to represent iterated belief revision. We define an iterated belief revision system by strengthening the postulates proposed by Darwiche and Pearl [3]. First, following the line of Darwiche and Pearl, we modify AGM postulates to make belief revision a function of epistemic states rather than of belief sets. Then we propose a set of postulates for...

Iterated Belief Revision: A Computational Approach

The capability of revising its beliefs upon new information in a rational and efficient way is crucial for an intelligent agent. The classic AGM theory studies mathematically idealized models of belief revision in two aspects: the properties (i.e., the AGM postulates) a rational revision operator should satisfy; and how to construct concrete revision operators. In scenarios where new informatio...