From a multi-agent simulation theory to GALATEA

This paper discusses a simulation theory with learning agents which is serving as a formal specification to guide the development of GALATEA, a multi-agent simulation platform. We have extended an existing simulation language: GLIDER, with abstractions to model systems where autonomous entities (agents) perceive and act upon their environments. We are now applying it to the study of multi-agent systems. In particular, an implementation on Biocomplexity [1] is briefly discussed in the paper. We also show how an Inductive Logic Programming system can be used to learn rules in a representation very close to the one used to guide the simulation in the biocomplex system. This establishes the feasibility of embedding (resource-bounded) learners as agents that take part in simulating a complex system, as defined by the theory.