AGENTS THAT LEARN TO BEHAVE IN MULTI-AGENT SIMULATIONS

摘要

This paper illustrates the use of Inductive Logic Programming to program agents that learn rules of behaviour from simulated histories of their embedding systems. We have shown how a ILP system can be used to learn rules in a representation very close to the one used to guide the simulation of a multi-agent system. This establishes the feasibility of embedding (resource-bounded) learners as agents that take part in simulating a complex system.