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Behavior-Based Robotics

TLDR
Whence behaviour? animal behaviour robot behaviour behaviour based architectures representational issues for behavioural systems hybrid deliberative/rective architectures perceptual basis for behaviour-based control adaptive behaviour social behaviour fringe robotics - beyond behaviour.
Abstract
Whence behaviour? animal behaviour robot behaviour behaviour-based architectures representational issues for behavioural systems hybrid deliberative/rective architectures perceptual basis for behaviour-based control adaptive behaviour social behaviour fringe robotics - beyond behaviour.

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Journal ArticleDOI

A survey of motivation frameworks for intelligent systems

TL;DR: An existing account of motivation in intelligent systems is extended to provide a framework for surveying relevant literature in AI and robotics, and a design for a new motive management framework is sketched which goes beyond the current state of the art.
Journal ArticleDOI

Anchoring symbols to conceptual spaces: the case of dynamic scenarios

TL;DR: This paper deals with the anchoring of one of the most influential symbolic formalisms used in cognitive robotics, namely the situation calculus, to a conceptual representation of dynamic scenarios in the context of robot vision.
Proceedings ArticleDOI

Hierarchical controller learning in a First-Person Shooter

TL;DR: It is argued that FPS games provide good environments for studying the learning of complex behaviours, and that the methods proposed here can help developing interesting opponents for games.
Proceedings ArticleDOI

Application of the Distributed Field Robot Architecture to a Simulated Demining Task

TL;DR: The DFRA is a distributed, object-oriented implementation of the SFX hybrid robot architecture that allows for dynamic discovery and acquisition of robot resources and the seamless integration of humans and artificial agents in the robot team.
Journal ArticleDOI

On Learning Navigation Behaviors for Small Mobile Robots With Reservoir Computing Architectures

TL;DR: A general reservoir computing (RC) learning framework that can be used to learn navigation behaviors for mobile robots in simple and complex unknown partially observable environments and three learning approaches for navigation behaviors are shown.