Adaptation to inversion of the visual eld is studied in a simple simulated model of phototactic behaviour. Inspired by recent ndings in neuroscience, a novel neural architecture based on continuous dynamical neural networks is implemented. Individual cells behave homeostatically by facilitating local plasticity whenever their activity goes out of bounds. Robots are evolved to perform long-term phototaxis on a series of light sources while trying to keep neurons behaving homeostatically. Robots are then tested under the condition of left/right inversion of vision. Initially, their phototactic capability is lost, which in most cases causes neurons to lose homeostasis and trigger plastic changes. After long periods of maladaptation, robots adapt to the new sensorimotor situation, and phototactic behaviour is recovered. The introduction of other disruptions such as radical perturbations to motor and sensor gains also results in eventual adaptation. The model intends t...

On behalf of:

In this paper we address the problem of synthesizing mobile robots able to solve problems in which they cannot merely react to sensory input, but have to maintain an internal state as well. More precisely we will show how autonomous robots synthesized through an evolutionary process can solve problems that necessarily require an ability to integrate sensory-motor information over time. By presenting the result of a set of experiments in which evolving robots are asked to navigate and self-localize in the environment, we will show that successful results can be achieved by providing evolving individuals with neural controllers with neurons that (a) vary their activity at different rates to detect regularities at different time scales in the sensory-motor flow, and (b) use thresholded activation functions to detect events extending over time.

The success of Artificial Life depends on whether it will help solving the conceptual problems of biology. Biology may be viewed as the science of the transformation of organizations. And, yet, biology lacks a theory of organization. We use this as an example of the challenge that Artificial Life must meet. "If - as I believe - physics and chemistry are conceptually inadequate as a theoretical framework for biology, it is because they lack the concept of function, and hence that of organization. [...] [P]erhaps, therefore, we should give the [...] computer scientists more of a say in the formulation of Theoretical Biology." -- Christopher Longuet-Higgins, 1969 [29] 1 Life and the organization problem in biology There are two readings of "life": "life" as an embodied phenomenon and "life" as a concept. Foucault [20] points out that up to the end of the eighteenth century life does not exist: only living beings. Living beings are but a class in the series of all things in the world. T...

Here we describe an architecture for an intelligent distribution agent being designed for the Navy. This autonomous software agent will implement global workspace theory, a psychological theory of consciousness. As a result, it can be expected to react to novel and problematic situations in a more flexible, more human-like way than traditional AI systems. If successful, it will perform a function, namely billet assignment, heretofore reserved for humans. The architecture consists of a more abstract layer overlying a multi-agent system of small processors. The mechanisms implementing the architecture are quite varied and diverse, and are drawn mostly from the "new" AI. This paper is intended as a progress report.

The new wave of robotics aims to provide robots with the capacity to learn, develop and evolve in interaction with their environments using biologically inspired techniques. This work is placed in perspective by considering its biological and psychological basis with reference to some of the grand theorists of living systems. In particular, we examine what it means to have a body by outlining theories of the mechanisms of bodily integration in multicellular organisms and their means of solidarity with the environment. We consider the implications of not having a living body for current ideas on robot learning, evolution, and cognition and issue words of caution about wishful attributions that can smuggle more into observations of robot behaviour than is scientifically supportable. To round off the arguments we take an obligatory swipe at ungrounded artificial intelligence but quickly move on to assess physical grounding and embodiment in terms of the rooted cognition of the living.

Abstract not found

Despite the relevance of much of Jakob von Uexkll's work to artificial intelligence and the cognitive sciences, it was largely ignored until the mid1980s. Since then, much research has been devoted to the study of embodied autonomous agents (robots) and artificial life. Such systems are typically said to `learn', `develop' and `evolve' in interaction with their environments. It could be argued that these self-organizing properties solve the problem of symbol or representation grounding in artificial intelligence research, and thus place autonomous agents in a position of semiotic interest. Here we discuss the relevance and implications of Jakob von Uexkll's theory of meaning to the study of artificial organisms and their use of representation and sign processes. Furthermore, we contrast his position with more mechanistic views, and examine the relation to recent theories of embodied cognition and its biological basis, in particular the work of Maturana and Varela. Finally, we address the issue of whether and to what extent artificial organisms are autonomous and capable of semiosis.

Collective behaviour is often characterised by the so-called "coordination paradox": Looking at individual ants, for example, they do not seem to cooperate or communicate explicitly, but nevertheless at the social level cooperative behaviour, such as nest building, emerges, apparently without any central coordination. In the case of social insects such emergent coordination has been explained by the theory of stigmergy, which describes how individuals can effect the behaviour of others (and their own) through artefacts, i.e. the product of their own activity (e.g., building material in the ants' case). Artefacts clearly also play a strong role in human collective behaviour, which has been emphasised, for example, by proponents of activity theory and distributed cognition. However, the relation between theories of situated/social cognition and theories of social insect behaviour has so far received relatively little attention in the cognitive science literature. This paper aims to take a step in this direction by comparing three theoretical frameworks for the study of cognition in the context of agent-environment interaction (activity theory, situated action, and distributed cognition) to each other and to the theory of stigmergy as a possible minimal common ground. The comparison focuses on what each of the four theories has to say about the role/nature of (a) the agents involved in collective behaviour, (b) their environment, (c) the collective activities addressed, and (d) the role that artefacts play in the interaction between agents and their environments, and in particular in the coordination of cooperation.

In this paper we investigate the use of hormonal feedback as a mechanism to modulate a “motivation-based, ” homeostatic action selection mechanism (ASM) in a robot. We have framed our study in the context of a dynamic, multirobot, competitive “two-resource ” action selection problem. The introduction of competitors has important consequences for action selection. We first show how the interaction between robots introduces new forms of environmental complexity that affect their viability. Secondly, we propose a “hormone-like ” mechanism that, modulating the input of the ASM, tackles these new sources of complexity. 1