Jason Smiles: Incremental BDI MAS Learning
Alejandro Guerra‐Hernández, Gustavo Ortiz Hernández, Wulfrano Arturo Luna-Ramírez · 2007
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.