Belief Revision via Lamar kian Evolution 1 Belief Revision via Lamar kian

Multi-agent Logi Aided Lamar kian Learning

Characterizing Relevant Belief Revision Operators

This paper introduces a framework for relevant belief revision. We represent agent’s beliefs in prime implicants and express agent’s preference on beliefs as a pre-order over terms. We define a belief revision operator via minimising the change of the prime implicants of the existing beliefs and the incoming information with respect to agent’s preferences. We show that such a belief revision op...

SATEN: An Object-Oriented Web-Based Revision and Extraction Engine

SATEN is an object-oriented web-based extraction and belief revision engine. It runs on any computer via a Java 1.1 enabled browser such as Netscape 4. SATEN performs belief revision based on the AGM approach (Alchourrón et al 85). The extraction and belief revision reasoning engines operate on a user specified ranking of information. One of the features of SATEN is that it can be used to integ...

Belief Revision: A Vade-Mecum

This paper contains a brief survey of the area of belief revision and its relation to updating of logical databases as it has developed during the last years. A set of rationality postulates for revisions are presented. A couple of semi-algorithmic models of revision and updating are introduced. The models are connected to the postulates via some representation theorems. Finally, the relation b...

A negotiation-style framework for non-prioritised revision

We present a framework for non-prioritised belief revision i.e., belief revision in which newly acquired information is not always fully accepted in which the result of revision is arrived at via a kind of negotiation between old information and new. We show how both ordinary par t ia l meet revision and Fermd and Hansson's selective revision can be captured in this framework, and also how it s...