Behavior-oriented AI is a scientific discipline that studies how behavior of agents emerges and becomes intelligent and adaptive. Success of the field is defined in terms of success in building physical agents that are capable of maximising their own self-preservation in interaction with a dynamically changing environment. The paper addresses this artificial life route towards artificial intelligence and reviews some of the results obtained so far. 1 Official reference: Steels, L. (1994) The artificial life roots of artificial intelligence. Artificial Life Journal, Vol 1,1. MIT Press, Cambridge. 1 Introduction For several decades, the field of Artificial Intelligence has been pursuing the study of intelligent behavior using the methodology of the artificial [104]. But the focus of this field, and hence the successes, have mostly been on higher order cognitive activities such as expert problem solving. The inspiration for AI theories has mostly come from logic and the cognitive...

In this paper, we examine the problem of controlling multiple behaviour-based autonomous robots. Based on observations made from the study of social insects, we propose ve simple mechanisms used to invoke group behaviour in simple sensor-based mobile robots. The proposed mechanisms allow populations of behaviour-based robots to perform tasks without centralized control or use of explicit communication. We have veri ed our collective control strategies by designing a robot population simulator called SimbotCity. Wehave also constructed a system of ve homogeneous sensor-based mobile robots, capable of achieving simple collective tasks, to demonstrate the feasibility of some of the control mechanisms.

Agent architectures need to organize themselves and adapt dynamically to changing circumstances without top-down control from a system operator. Some researchers provide this capability with complex agents that emulate human intelligence and reason explicitly about their coordination, reintroducing many of the problems of complex system design and implementation that motivated increasing software localization in the first place. Naturally occurring systems of simple agents (such as populations of insects or other animals) suggest that this retreat is not necessary. This paper summarizes several studies of such systems, and derives from them a set of general principles that artificial multi-agent systems can use to support overall system behavior significantly more complex than the behavior of the individuals agents. 1.

This paper presents an analysis of an artificially evolved dynamical network-based control system for a simulated autonomous mobile robot engaged in simple visually guided tasks.

Abstract not found

This paper builds on a previous review of significant research on adaptive behavior in animats. It summarizes the current state of the art and suggests some directions likely to provide interesting results in the near future. 1 Introduction An animat is a simulated animal or a real robot whose rules of behavior are inspired by those of animals. It is usually equipped with sensors, with actuators, and with a behavioral control architecture that allow it to react or to respond to variations in its environment (internal or external), notably to those that might impair its chances of survival. The behavior of an animat is what the animat does. This is characterized by a sequence of actions which reflects the dynamic interplay between the animat and its environment, mediated through the animat's sensors and actuators. The behavior of an animat is adaptive so long as it allows the animat to survive or to fulfill its mission. This requires that the animat's essential variables be monitored a...

This work describes a control architecture based on a hierarchical classifier system. This architecture, which uses both reactive and planning rules, implements a motivationally autonomous animat that chooses the actions it will perform according to the expected consequences of the alternatives. The adaptive faculties of this animat are illustrated through various examples.

This thesis addresses the problem of intelligent control of autonomous mobile robots, particularly under circumstances unforeseen by the designer. As the range of applications for autonomous robots widens and increasingly includes operation in unknown environments (exploration) and tasks which are not clearly specifiable a priori (maintenance work), this question is becoming more and more important. It is argued that in order to achieve such flexibility in unforeseen situations it is necessary to equip a mobile robot with the ability to autonomously acquire the necessary task achieving competences, through interaction with the world. Using mobile robots equipped with self-organising, behaviour-based controllers, experiments in the autonomous acquisition of motor competences and navigational skills were conducted to investigate the viability of this approach. A controller architecture is presented that allows extremely fast acquisition of motor competences such as obstacle avoidance, wa...

This paper is meant as a basis for discussion towards a framework for cognitive architectures integrating remembering, rehearsal, language and empathy. It describes the programmatic background of our concrete work on intelligent autonomous agents. The goal is to motivate a common framework which should inspire research on `artificial cognition' for autonomous robots as well as investigations on cognition in humans or other animals. 1 Introduction Our professional background are biology and artificial intelligence and we are mainly interested in the construction of intelligent autonomous agents based on biological and psychological findings and models. This paper outlines our research framework which grew out of considerations on cognition for artifacts, although we are aware that the successful implementation of these ideas is still a future goal. Instead of reviewing intensively literature of cognitive science and escpecially of cognitive psychology we focus on some points which are ...

This work describes a control architecture based on a hierarchical classifier system. This system, which learns both reactive and planning rules, implements a motivationally autonomous animat that chooses the actions it performs according to its perception of the external environment, to its physiological or internal state, to the consequences of its current behavior, and to the expected consequences of its future behavior. The adaptive faculties of this architecture are illustrated within the context of a navigation task, through various experiments with a simulated and a real robot. I. Introduction The work presented in this paper fits into the so-called animat approach, which aims at designing animats, i.e., simulated animals or real robots whose rules of behavior are inspired by those of animals. The proximate goal of this approach is to discover architectures or working principles that allow an animal or a robot to exhibit an adaptive behavior and, thus, to survive or fulfill i...