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.