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About Autonomy Oriented Computing

Introduction

While existing methods for modeling autonomy are successful to some extent, a generic model or framework for handling problems in complex systems, such as ecological, social, economical, mathematical, physical, and natural systems, effectively is still absent. Autonomy oriented computing (AOC) unifies the methodologies for effective analysis, modeling, and simulation of the characteristics of complex systems. In so doing, AOC offers a new computing paradigm that makes use of autonomous entities in solving computational problems and in modeling complex systems. This new paradigm can be classified and studied according to (1) how much human involvement is necessary and (2) how sophisticated a model of computational autonomy is, as follows:

AOC-by-fabrication: Earlier examples with this approach are entity-based image feature extraction, artificial creature animation, and ant colony optimization. Lifelike behavior and emergent intelligence are exhibited in such systems by means of fabricating and operating autonomous entities.

AOC-by-prototyping: This approach attempts to understand self-organized complex phenomena by modeling and simulating autonomous entities. Examples include studies on Web regularities based on self-adaptive information foraging entities.

AOC-by-self-discovery: This approach automatically fine-tunes the parameters of autonomous behaviors in solving and modeling certain problems. A typical example is using autonomous entities to adaptively solve a large-scale, distributed optimization problem in real time.

As compared to other paradigms, such as centralized computation and top-down systems modeling, AOC has been found to be extremely appealing in the following aspects:

  • To capture the essence of autonomy in natural and artificial systems;
  • To solve computationally hard problems, e.g., large-scale computation, distributed constraint satisfaction, and decentralized optimization, that are dynamically evolving and highly complex in terms of interaction and dimensionality;
  • To characterize complex phenomena or emergent behavior in natural and artificial systems that involve a large number of self-organizing, interacting entities;
  • To discover laws and mechanisms underlying complex phenomena or emergent behaviors.

Early Work on AOC

The ideas, formulations, and case studies that we introduce in this portal have resulted largely from the research undertaken in the AOC Research Lab of Hong Kong Baptist University under the direction of Professor Jiming Liu. In what follows, we highlight some of the earlier activities in our journey towards the development of AOC as a new paradigm for computing.

Our first systematic study on AOC originated in 1996. As originally referred to Autonomy Oriented Computation, the notion of AOC first appeared in the book of Autonomous Agents and Multi-Agent Systems (AAMAS). Later, as an effort to promote the AOC research, the First International Workshop on AOC was organized and held in Montreal in 2001.

Earlier projects at the AOC Lab have been trying to explore and demonstrate the effective use of AOC in a variety of domains, covering constraint satisfaction problem solving, mathematical programming, image processing. Since 2000, projects have been launched to study the AOC approaches to characterizing (i.e., modeling and explaining) observed or desired regularities in real-world complex systems, e.g., self-organized Web regularities and HIV infection dynamics, as a white-box alternative to the traditional top-down or statistical modeling.

These AOC projects differ from traditional AI and agent studies in that here we pay special attention to the role of self-organization, a powerful methodology as demonstrated in nature and well suited to the problems that involve large-scale, distributed, locally interacting, and sometimes rational entities. This very emphasis on self-organization was also apparent in the earlier work on collective problem solving with a group of autonomous robots.

Recently, we have started to explore a new frontier, the AOC applications to the Internet. This work has dealt with the theories and techniques essential for the next paradigm shift in the World Wide Web, i.e., the Wisdom Web. It covers a number of key Web Intelligence (WI) capabilities, such as (1) autonomous service planning; (2) distributed resource discovery and optimization; (3) Problem Solver Markup Language (PSML); (4) social network evolution; (5) ubiquitous intelligence.