Distributed interaction with computon

Abstract

One of the major challenges of distributed artificial intelligence is to obtain useful and effective emergent behaviors of agents in the system based on the local decision-making of each agent. The effectiveness of the system as a whole is as much dependent on the form of interactions between agents as on the capabilities or strategies of each one. The focus and main theme of this paper is to put forth the idea of distributed interactions through an elementary medium. In this conceptual picture, the interaction between agents is distributed over the collective behavior of a basic unit or “particle”, which we call “computon”. The key feature of distributed interaction is the distribution of contents of information among objects or computational agents. This model allows each agent to make a decision on its behavior based on simple all-local transactions for a possibly effective emergent collective behavior. In order to evaluate and examine the feasibility and possibility of distributed interactions, We consider two examples of distributed interaction models with computons. The first example is a conceptual discussion of a “Quantized” Computational Field Model. In this model, computon is introduced as a “fundamental particle” of the computational field. Interaction of objects and computational field is envisioned as an interaction between objects via exchanges of computons. To gain more quantitative insight into distributed interactions, we constructed a model using computons to address the problem of load balancing. A dynamic load balancing model applied to a ring of processors was investigated using simulations. When compared with a load balancing model without computons, the load was found to be distributed better over a model ring of processors. Through these examples, we infer and discuss general advantages and problems of distributed interactions among distributed agents or computational resources.