Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
Theory of Modelling and Simulation
Theory of Modelling and Simulation
ACTILOG: An Agent Activation Language
PADL '03 Proceedings of the 5th International Symposium on Practical Aspects of Declarative Languages
OPENLOG: A Logic Programming Language Based on Abduction
PPDP '99 Proceedings of the International Conference PPDP'99 on Principles and Practice of Declarative Programming
Towards a Unified Agent Architecture that Combines Rationality with Reactivity
LID '96 Proceedings of the International Workshop on Logic in Databases
MultiAgent Distributed Simulation with GALATEA
DS-RT '05 Proceedings of the 9th IEEE International Symposium on Distributed Simulation and Real-Time Applications
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
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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.