International Workshop on Massively Multi-Agent Systems (2004.12. 10-11)

Autonomy Oriented Computing (AOC) with MMAS:

From Problem Solving to Complex Systems Modeling

Jiming Liu

Hong Kong Baptist University

With the advent of computing, we are fast entering a new era of discovery and opportunity. In life and material sciences, specially engineered amorphous computational particles will be able to perform optimal search, whether they are bio-robot agents to kill cancer cells inside human bodies or smart paints to evenly cover and fill cracks on rugged surfaces. In environmental sciences, surveillance applications will be able to deploy wireless, mobile sensor networks to monitor wild vegetation and route the tracking measurements of moving objects back to home stations efficiently and safely. The key to success in such applications lies in large-scale deployment of massively multi-agent systems (MMAS) in which computational autonomous agents are capable of working together to effectively complete overwhelming tasks.

Autonomy Oriented Computing (AOC) has become a new computing paradigm best-suited for the problems that involve large-scale, distributed, locally interacting, and sometimes rational entities. AOC pays special attention to the role of self-organization, and has been found to be especially appealing in the following aspects:

1. To formulate and hence solve computationally hard problems, e.g., large-scale computation, distributed constraint satisfaction, and decentralized optimization, which are dynamically evolving and complex - as far as nonlinear interaction and high dimensionality are concerned;

2. To characterize and hence discover new laws and mechanisms underlying complex phenomena or emergent behavior in natural and artificial systems that involve a vast number of self-organizing, nonlinearly interacting entities.

In this talk, I will present the important theoretical and practical issues in AOC, with both methodologies and illustrative examples.