ADAPTATION AND LEARNING IN AN AUTONOMOUS PHYSICAL AGENT
Learning and adaptation is presented for a speci¯c but generally
applicable autonomous physical agent (APA) architecture. The paper is providing
a general framework of skills learning within behaviour logic framework of
agents that communicate, sense and act in the physical world. It is shown how
programmed playfulness can be easily implemented that results in learning and
ultimately better skills of agents. Reusability of results in learning algorithms
is supported by ontology based classi¯cation of learning in operational modes.
Ontology based classi¯cation provides object instances that enable modularization
of software and easy interfacing of operational modes with learning algorithms.