Bottom-up relational learning of pattern matching rules for information extraction
The Journal of Machine Learning Research
Anytime Learning of Decision Trees
The Journal of Machine Learning Research
SMILE: Sound Multi-agent Incremental LEarning
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Learning, Information Exchange, and Joint-Deliberation through Argumentation in Multi-agent Systems
OTM '08 Proceedings of the OTM Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: 2008 Workshops: ADI, AWeSoMe, COMBEK, EI2N, IWSSA, MONET, OnToContent + QSI, ORM, PerSys, RDDS, SEMELS, and SWWS
Intelligent Autonomous Robotics: A Robot Soccer Case Study
Intelligent Autonomous Robotics: A Robot Soccer Case Study
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
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This paper investigates incremental multiagent learning in static or evolving structured networks. Learning examples are incrementally distributed among the agents, and the objective is to build a common hypothesis that is consistent with all the examples present in the system, despite communication constraints. Recently, a first mechanism was proposed to deal with static networks, but its accuracy was reduced in some topologies. We propose here several possible improvements of this mechanism, whose different behaviors with respect to some efficiency requirements (redundancy, computational cost and communicational cost) are experimentally investigated. Then, we provide an experimental analysis of some variants for evolving networks.