Autonomous multi-entity 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 proposes 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 multi-agent 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 examining implemented applications, we identify three main approaches to AOC. Specifically, we provide a detailed description of the 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.

Abstract. Tax compliance is a field that crosses over several research areas, from economics to machine learning, from sociology to artificial intelligence and multiagent systems. The core of the problem is that the standing general theories cannot even explain why people comply as much as they do, much less make predictions or support prescriptions for the public entities. The compliance decision is a challenge posed to rational choice theory, and one that defies the current choice mechanisms in multi-agent systems. The key idea of this project is that by considering rationallyheterogeneous agents immersed in a highly social environment we can get hold of a better grasp of what is really involved in the individual decisions. Moreover, we aim at understanding how those decisions determine tendencies for the behaviour of the whole society, and how in turn those tendencies influence individual behaviour. This paper presents the results of some exploratory simulations carried out to uncover regularities, correlations and trends in the models that represent first and then expand the standard theories on the field. We conclude that forces like social imitation and local neighbourhood enforcement and reputation are far more important than individual perception of expected utility maximising, in what respects compliance decisions. 1

The issue that is addressed in this paper concerns the way interacting agents should understand their environment so that a common good used by the whole group would last. We synthesise the results of four models with agents interacting in artificial societies in which they have to share a resource. The four societies were built using multi-agent based simulation models that address issues related to the use of common renewable goods. The resources that are used by the artificial communities of agents are of two types: for some, agents must co-ordinate to exploit the resources; for others, the distribution of goods among agents is directly dependent on the distribution of the agents in space.

in space-time

Examples of autonomous multi-entity systems are plentiful, both in the natural and artificial worlds. Many systems have been studied in depth and some models of these have been built in computational systems for problem solving. Central to these computational systems is the notion of autonomy. This article proposes autonomy oriented computation (AOC) as a complementary paradigm for solving hard computational problems and for characterizing the behaviors of a complex system.

Simulation is an appropriate approach for studying complex systems that are inacessible through direct observations and measurements. In a simulation involving a great number of interacting entities, it is difficult to create a reliable and tractable abstraction of the real reference system. One of the involved problems is amount of computational resources required to handle microscopic simulation of large number of entities. One solution is to use macroscopic models. However, this type of models may be at hand unavailable or not reliable, or it doesn’t allow observations of individual behaviours. In this paper a multilevel simulation model is proposed to dynamically adapt the level of simulated behaviours while being as faithful as possible to the reference model. Our approach is based on Holonic Multi-Agent Systems and provides a generic scheduling model for multilevel simulations. 1

Application to real-time pedestrians simulation in virtual urban environment

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Examples of autonomous multi-entity systems are plentiful, both in the natural and arti cial worlds. Many systems have been studied in depth and some models of these have been built in computational systems for problem solving. Central to these computational systems is the notion of autonomy. This article introduces the concept of autonomy oriented computation - a bottom-up problem solving paradigm in which autonomy is the basis of modeling. It aims to model, explain and predict behavior(s) in a complex adaptive system.