Jason Smiles: Incremental BDI MAS Learning

This work deals with the problem of intentional learning in a multi-agent system (MAS). Smile (sound multi-agent incremental learning), a collaborative learning protocol which shows interesting results in the distributed learning of well known complex boolean formulae, is adopted here by a MAS of BD...

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Bibliographic Details
Main Authors: Guerra-Hernandez, A., Ortiz-Hernandez, G., Luna-Ramirez, W.A.
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence
BDI
Bismuth
Collaboration
Collaborative work
Decision trees
Feedback
Java
Learning
Learning systems
Multi-Agent Systems
Multiagent systems
Protocols
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Summary:This work deals with the problem of intentional learning in a multi-agent system (MAS). Smile (sound multi-agent incremental learning), a collaborative learning protocol which shows interesting results in the distributed learning of well known complex boolean formulae, is adopted here by a MAS of BDI agents to update their practical reasons while keeping MAS-consistency. An incremental algorithm for first-order induction of logical decision trees enables the BDI agents to adopt Smile, reducing the amount of communicated learning examples when compared to our previous non-incremental approaches to intentional learning. The protocol is formalized extending the operational semantics of AgentSpeak(L), and implemented in Jason, its well known Java-based extended interpreter.
DOI:10.1109/MICAI.2007.16