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An Algorithmic Approach to Knowledge Evolution (1999)
| Content Provider | CiteSeerX |
|---|---|
| Author | Lomuscio, Alessio Ryan, Mark |
| Abstract | Intelligent agents must update their knowledge base as they acquire new information about their environment. The modal logic S5 n has been designed for representing knowledge bases in societies of agents. Halpern and Vardi have proposed the notion of refinement of S5 n Kripke models in order to solve multi-agent problems in which knowledge evolves. We argue that there are some problems with their proposal and attempt to solve them by moving from Kripke models to their corresponding trees. We define refinement of a tree with a formula, show some properties of the notion, and illustrate with the muddy children puzzle. We show how some diagnosis problems in engineering can be modelled as knowledge-based multi-agent systems, and hence how our approach can address them. 1 Introduction 1.1 Temporal epistemic modal logics and their potential for applications In the last few years there is been a growing trend towards applying logical theories (and MultiAgent theories in general) to the ... |
| File Format | |
| Volume Number | 13 |
| Journal | Artificial Intelligence for Engineering Design, Analysis and Manufacturing (AIEDAM |
| Language | English |
| Publisher Date | 1999-01-01 |
| Access Restriction | Open |
| Subject Keyword | Knowledge Evolution Algorithmic Approach Kripke Model Knowledge Base Multiagent Theory Corresponding Tree Diagnosis Problem Logical Theory Trend Towards Muddy Child New Information Knowledge-based Multi-agent System Multi-agent Problem Modal Logic S5 Temporal Epistemic Modal Logic Knowledge Evolves Intelligent Agent Last Year |
| Content Type | Text |
| Resource Type | Article |
