Adaptive Behavior, 7 (1)

There is a definite challenge in the air regarding the pivotal notion of internal representation. This challenge is explicit in, e.g., van Gelder, 1995; Beer, 1995; Thelen & Smith, 1994; Wheeler, 1994; and elsewhere. We think it is a challenge that can be met and that (importantly) can be met by arguing from within a general framework that accepts many of the basic premises of the work (in new robotics and in dynamical systems theory) that motivates such scepticism in the first place. Our strategy will be as follows. We begin (Section 1) by offering an account (an example and something close to a definition) of what we shall term Minimal Robust Representationalism (MRR). Sections 2 & 3 address some likely worries and questions about this notion. We end (Section 4) by making explicit the conditions under which, on our account, a science (e.g., robotics) may claim to be addressing cognitive phenomena.

Key Words

representation; forward models; anti-representationalism; reactive systems; motor control; feedback

Pages 17-33

Making Predictions in an Uncertain World: Environmental Structure and Cognitive Maps

By Eric Chown

Abstract

This article examines the relationship between environmental and cognitive structure. One of the key tasks for any agent interacting in the real world is the management of uncertainty; because of this the cognitive structures which interact with real environments, such as would be used in navigation, must effectively cope with the uncertainty inherent in a constantly changing world. Despite this uncertainty, however, real environments usually afford structure that can be effectively exploited by organisms. The article examines environmental characteristics and structures that enable humans to survive and thrive in a wide range of real environments. The relationship between these characteristics and structures, uncertainty, and cognitive structure is explored in the context of PLAN, a proposed model of human cognitive mapping, and R-PLAN, a version of PLAN that has been instantiated on an actual mobile robot. An examination of these models helps to provide insight into environmental characteristics which impact human performance on tasks which require interaction with the world.

Key Words

cognitive maps; navigation; associative networks; gateways

Pages 35-63

DRAMA: A Connectionist Architecture for Control and Learning in Autonomous Robots

By Aude Billard and Gillian Hayes

Abstract

Adaptation to their environment is a fundamental capability for living agents, from which autonomous robots could also benefit. This work proposes a connectionist architecture, DRAMA, for dynamic control and learning of autonomous robots. DRAMA stands fordynamical recurrent associative memory architecture. It is a time-delay recurrent neutral network, using Hebbian update rules. It allows learning of spatio-temporal regularities and time series in discrete sequences of inputs, in the face of an important amount of noise. The first part of this paper gives the mathematical description of the architecture and analyses theoretically and through numerical simulations its performance. The second part of this paper reports on the implementation of DRAMA in simulated and physical robotic experiments. Training and rehearsal of the DRAMA architecture is computationally fast and inexpensive, which makesthe model particularly suitable for controlling "computationally-challenged" robots. In the experiments, we use a basic hardware system with very limited computational capability and show that our robot can carry out real time computation and on-line learning of relatively complex cognitive tasks. In these experiments, two autonomous robots wander randomly in a fixed environment, collecting information about its elements. By mutually associating information of their sensors and actuators, they learn about physical regularities underlying their experience of varying stimuli. The agents learn also from their mutual interactions. We use a teacher-learner scenario, based on mutual following of the two agents, to enable transmission of a vocabulary from one robot to the other.

Key Words

time-delay recurrent neural networks; Hebbian learning; spatio-temporal associations; unsupervised dynamical learning; autonomous robots

Pages 65-97

Insect-Inspired Robotic Homing

By Keven Weber, Svetha Venkathesh and Mandyam Srinivasan

Abstract

Many animals, including insects, successfully engage in visual homing. We describe a system that allows a mobile robot to home. Specifically, we propose a simple, yet robust, homing scheme that only relies upon the observation of the bearings of visible landmarks. However, this can easily be extended to include other visual cues. The homing algorithm allows a mobile robot to home incrementally by moving in such a way as to gradually reduce the discrepancy between the current view and the view obtained from the home position. Both simulation and mobile robot experiments are used to demonstrate the feasibility of the approach.

Key Words

robot homing; insect behavior; visual navigation

Pages 99-127

Layered Control Architectures in Robots and Vertebrates

By Tony J. Prescott, Peter Redgrave and Kevin Gurney

Abstract

We review recent research in robotics, neuroscience, evolutionary neurobiology, and ethology with the aim of highlighting some points of agreement and convergence. Specifically, we compare Brooks' (1986) subsumption architecture for robot control with research in neuroscience demonstrating layered control systems in vertebrate brains, and with research in ethology that emphasizes the decomposition of control into multiple, intertwined behavior systems. From this perspective we then describe interesting parallels between the subsumption architecture and the natural layered behavior system that determines defense reactions in the rat. We then consider the action selection problem for robots and vertebrates and argue that, in addition to subsumption-like conflict resolution mechanisms, the vertebrate nervous system employs specialized selection mechanisms located in a group of central brain structures termed the basal ganglia. We suggest that similar specialized switching mechanisms might be employed in layered robot control architectures to provide effective and flexible action selection.

Key Words

subsumption architecture; brain evolution; behavior systems; defense system; action selection; basal ganglia

Pages 129-136

Jumping to Bold Conclusions

By Seth Bullock

Review of The Handicap Principle: A Missing Piece of Darwin's Puzzle, by Amotz Zahavi and Avishag Zahavi. Oxford: Oxford University Press, 1997.

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Adaptive Behavior, 7 (1)

Adaptive Behavior

Volume 7, Number 1

Winter 1999

Table of Contents

Jean-Arcady Meyer

Editorial

Andy Clark and Rick Grush

Towards a Cognitive Robotics