Abstract
Adaptability is a fundamental property of any intelligent system. In this paper, we present how adaptability in multi-agent systems can be implemented by means of collaborative logic-based learning. The proposed method is based on two building blocks: (1) a set of operations centred around inductive logic programming for generalizing agents' observations into sets of rules, and (2) a set of communication strategies for sharing acquired knowledge among agents in order to improve the collaborative learning process. Using these modular building blocks, several learning algorithms can be constructed with different trade-offs between the quality of learning, computation and communication requirements, and the disclosure of the agent's private information. The method has been implemented as a modular software component that can be integrated into the control loop of an intelligent agent. The method has been evaluated on a simulated logistic scenario, in which teams of trading agents learn the properties of the environment in order to optimize their operation.
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