Abstract
Multi-Agent Systems (MASs) and Case-Based Reasoning (CBR) are two recent and hot paradigms in artificial intelligence field. CBR is a reasoning methodology based on old experience reasoning or similarity-based reasoning while MAS is a new paradigm to organize AI applications. CBR offers multi-agent systems the capability of autonomously learning from experience. This study examines the integration of CBR, MASs and Expert Systems (ESs). In addition, it presents a knowledge-based model of multi-agent CBR systems from both a logical and a knowledge-based viewpoint.
Highlights
Case-based reasoning (CBR) and multi-agent systems (MASs) are two different paradigms in AI
The integration of CBR and MASs has drawn increasing attention in the AI community because CBR offers MAS paradigm the capability of autonomously learning from experience[1]
This study will fill this gap by providing knowledge-based models of multi-agent CBR systems (CBRSs)
Summary
Case-based reasoning (CBR) and multi-agent systems (MASs) are two different paradigms in AI. This study will fill this gap by providing knowledge-based models of multi-agent CBR systems (CBRSs).
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