Autonomy-Oriented Computing (AOC): Formulating Computational Systems With Autonomous Components

Autonomous multientity systems are plentiful in natural and artificial worlds. Many systems have been studied in depth and some models of them have been built as computational systems for problem solving. Central to these computational systems is the notion of autonomy. This article surveys research work done along this direction and presents autonomy-oriented computing (AOC) as a paradigm to describe systems for solving hard computational problems and for characterizing the behaviors of a complex system. AOC differs from major complex-system-related studies such as artificial life, simulated evolution, and multiagent systems in that AOC is not just intended to replicate complex behavior, emulate evolution, or coordinate the functioning of many interacting agents. AOC emphasizes the modeling of autonomy in the entities of a complex system and the self-organization of them in achieving a specific goal. Through implemented applications, we describe three main approaches to AOC, as well as an AOC framework with formal definitions of essential constructs and their interrelationships, including the notions of emergent autonomy, self-organization, and the interactions among entities and environment.