The agent network architecture (ANA)

The goal of my work is to develop and implement an architecture for an autonomous agent, which I refer to as "ANA". An ANA agent consists of a distributed set of "competence modules". Competence modules are linked in a network. A spreading activation process operates on the network to decide what the "relevance" or relative strength of a competence module is in the current context. This process implements a competition among modules for activation energy. The higher the activation energy level of a module, the more likely it is that this module determines what the autonomous agent does or communicates to believe. Learning is a central, completely integrated feature of the architecture. The competence module network is continuously being developed and changed on the basis of experience: links are added and deleted depending on real world observations and new "macro modules" are created whenever a goal is achieved. This paper presents an overview of the architecture. It describes the functionalities that have been implemented, the results that have been obtained with robotic and simulated ANA agents, and finally it discusses (current) limitations and future work.