Machine Learning
Bottom-up relational learning of pattern matching rules for information extraction
The Journal of Machine Learning Research
Lookahead-based algorithms for anytime induction of decision trees
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Recycling data for multi-agent learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
SMILE: Sound Multi-agent Incremental LEarning
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Intelligent Autonomous Robotics: A Robot Soccer Case Study
Intelligent Autonomous Robotics: A Robot Soccer Case Study
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This paper investigates incremental multiagent learning in 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, different mechanisms have been proposed that allow groups of agents to coordinate their hypotheses. Although these mechanisms have been shown to guarantee (theoretically) convergence to globally consistent states of the system, others notions of effectiveness can be considered to assess their quality. Furthermore, this guaranteed property should not come at the price of a great loss of efficiency (for instance a prohibitive communication cost). We explore these questions theoretically and experimentally (using different boolean formulas learning problems).