Title :
LEAF: a toolkit for developing coordinated learning based MAS
Author :
Lynden, Steven ; Rana, Omer F.
Author_Institution :
Dept. of Comput. Sci., Cardiff Univ., UK
Abstract :
This paper describes LEAF, the "Learning Agent based FIPA-Compliant Community Toolkit", a toolkit for developing multiagent systems coordinated using utility function assignment, based on collective intelligence by Wolpert et al. (1999). LEAF agents use machine learning techniques such as reinforcement learning to maximise local utility functions, where local utility functions are assigned to agents such that the maximisation of local utility by agents within a community maximises a global utility. LEAF provides support via a Java API for developing FIPA-compliant agent systems conforming to this framework, utilising the FIPA-OS agent toolkit, a Java based FIPA compliant agent construction toolkit.
Keywords :
Java; application program interfaces; learning (artificial intelligence); multi-agent systems; optimisation; FIPA-OS agent toolkit; FIPA-compliant agent systems; Java API; LEAF; Learning Agent based FIPA-Compliant Community Toolkit; MAS; collective intelligence; coordinated learning; global utility; local utility functions; machine learning; maximisation; multiagent systems; reinforcement learning; utility function assignment; Computer science; Distributed computing; Environmental management; Intelligent agent; Java; Large-scale systems; Learning systems; Machine learning; Multiagent systems; Resource management;
Conference_Titel :
Parallel and Distributed Processing Symposium, 2003. Proceedings. International
Print_ISBN :
0-7695-1926-1
DOI :
10.1109/IPDPS.2003.1213260