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Adapt Behav 2018 Aug 23;26(4):147-163. Epub 2018 May 23.

University of Amsterdam, Amsterdam, The Netherlands.

Enactive approaches to cognitive science aim to explain human cognitive processes across the board without making any appeal to internal, content-carrying representational states. A challenge to such a research programme in cognitive science that immediately arises is how to explain cognition in so-called 'representation-hungry' domains. Examples of representation-hungry domains include imagination, memory, planning and language use in which the agent is engaged in thinking about something that may be absent, possible or abstract. Read More

Adapt Behav 2016 Dec 5;24(6):446-463. Epub 2016 Sep 5.

Acoustics Research Laboratory, Tropical Marine Sciences Institute, Singapore.

In this paper, the role of adaptive group cohesion in a cooperative multi-agent source localization problem is investigated. A distributed source localization algorithm is presented for a homogeneous team of simple agents. An agent uses a single sensor to sense the gradient and two sensors to sense its neighbors. Read More

Adapt Behav 2016 Oct 12;24(5):267-291. Epub 2016 Oct 12.

Embodied Emotion, Cognition and (Inter-)Action Lab, School of Computer Science, University of Hertfordshire, UK.

We present a robot architecture and experiments to investigate some of the roles that pleasure plays in the decision making (action selection) process of an autonomous robot that must survive in its environment. We have conducted three sets of experiments to assess the effect of different types of pleasure-related versus unrelated to the satisfaction of physiological needs-under different environmental circumstances. Our results indicate that pleasure, including pleasure unrelated to need satisfaction, has value for homeostatic management in terms of improved viability and increased flexibility in adaptive behavior. Read More

Adapt Behav 2016 Oct 10;24(5):373-396. Epub 2016 Oct 10.

Department of Informatics, University of Hamburg, Germany.

A robot capable of understanding emotion expressions can increase its own capability of solving problems by using emotion expressions as part of its own decision-making, in a similar way to humans. Evidence shows that the perception of human interaction starts with an innate perception mechanism, where the interaction between different entities is perceived and categorized into two very clear directions: positive or negative. While the person is developing during childhood, the perception evolves and is shaped based on the observation of human interaction, creating the capability to learn different categories of expressions. Read More

Adapt Behav 2016 Aug 26;24(4):249-260. Epub 2016 Jul 26.

Department of Engineering Mathematics, University of Bristol, UK.

Consensus formation is investigated for multi-agent systems in which agents' beliefs are both vague and uncertain. Vagueness is represented by a third truth state meaning . This is combined with a probabilistic model of uncertainty. Read More

Adapt Behav 2014 Jun;22(3):207-216

University College London, UK.

A stimulus is a reliable signal of an outcome when the probability that the outcome occurs in its presence is different from in its absence. Reliable signals of important outcomes are responsible for triggering critical anticipatory or preparatory behavior, which is any form of behavior that prepares the organism to receive a biologically significant event. Previous research has shown that humans and other animals prepare more for outcomes that occur in the presence of highly reliable (i. Read More

Adapt Behav 2009 ;17(3):213-235

Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061,

The Iterated Classification Game (ICG) combines the Classification Game with the Iterated Learning Model (ILM) to create a more realistic model of the cultural transmission of language through generations. It includes both learning from parents and learning from peers. Further, it eliminates some of the chief criticisms of the ILM: that it does not study grounded languages, that it does not include peer learning, and that it builds in a bias for compositional languages. Read More