The extended ramp model: A biomimetic model of behaviour arbitration for lightweight cognitive architectures

Gaudl, Swen and Bryson, Joanna (2018) The extended ramp model: A biomimetic model of behaviour arbitration for lightweight cognitive architectures. Cognitive Systems Research, 50. pp. 1-9. ISSN 1389-0417 (In Press)

[img] Text
2018-CognitiveSystems-ExtendedRamp-Gaudl-Bryson-preprint.pdf - Accepted Version
Restricted to Repository staff only until 1 August 2020.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (211kB) | Request a copy
Official URL: https://www.sciencedirect.com/science/article/pii/...

Abstract / Summary

In this article, we present an idea for a more intuitive, low-cost, adjustable mechanism for behaviour control and management. One focus of current development in virtual agents, robotics and digital games is on increasingly complex and realistic systems that more accurately simulate intelligence found in nature. This development introduces a multitude of control parameters creating high computational costs. The resulting complexity limits the applicability of AI systems. One solution to this problem is to focus on smaller, more manageable, and flexible systems which can be simultaneously created, instantiated, and controlled. Here we introduce a biologically inspired systems-engineering approach for enriching behaviour arbitration with a low computational overhead. We focus on an easy way to control the maintenance, inhibition and alternation of high-level behaviours (goals) in cases where static priorities are undesirable. The models we consider here are biomimetic, based on neuro-cognitive research findings from dopaminic cells responsible for controlling goal switching and maintenance in the mammalian brain. The most promising model we find is applicable to selection problems with multiple conflicting goals. It utilizes a ramp function to control the execution and inhibition of behaviours more accurately than previous mechanisms, allowing an additional layer of control on existing behaviour prioritization systems.

Item Type: Article
Identification Number: https://doi.org/10.1016/j.cogsys.2018.02.001
ISSN: 1389-0417
Subjects: Research
Science
Courses by Department: The Games Academy > Computing for Games
Depositing User: Swen Gaudl
Date Deposited: 12 Apr 2018 10:40
Last Modified: 12 Apr 2018 10:40
URI: http://repository.falmouth.ac.uk/id/eprint/2836

Actions

View Item View Item (login required)