Article

Using an ethologically-inspired model to learn apparent temporal causality for planning in synthetic creatures

Authors:

Bruce BlumbergAuthors Info & Claims

AAMAS '02: Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1

Pages 326 - 333

https://doi.org/10.1145/544741.544820

Published: 15 July 2002 Publication History

Abstract

Inspired by recent work in ethology and animal training, we integrate representations for time and rate into a behavior-based architecture for autonomous virtual creatures. The resulting computational model of affect and action selection allows creatures to discover and refine their understanding of apparent temporal causality relationships which may or may not involve self-action. The fundamental action selection choice that a creature must make in order to satisfy its internal needs is whether to explore, react or exploit. In this architecture, that choice is informed by an understanding of apparent temporal causality, the representation for which is integrated into the representation for action. The ability to accommodate changing ideas about causality allows the creature to exist in and adapt to a dynamic world. Not only is such a model suitable for computational systems, but its derivation from biological models suggests that it may also be useful for gaining a new perspective on learning in biological systems. The implementation of a complete character built using this architecture is able to reproduce a variety of conditioning phenomena, as well as learn in real-time using a training technique used with live animals.

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Cited By

Zambetta F(2008)Modelling Interactive Non-Linear StoriesComputational Intelligence in Multimedia Processing: Recent Advances10.1007/978-3-540-76827-2_5(119-138)Online publication date: 2008
https://doi.org/10.1007/978-3-540-76827-2_5
Shuen-Huei Guan Sheng-Yao Cho Yu-Te Shen Rung-Huei Liang Bing-Yu Chen Ming Ouhyoung (2004)Conceptual Farm2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763)10.1109/ICME.2004.1394701(2179-2182)Online publication date: 2004
https://doi.org/10.1109/ICME.2004.1394701

Index Terms

Using an ethologically-inspired model to learn apparent temporal causality for planning in synthetic creatures
1. Computing methodologies
  1. Machine learning
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Markov decision processes

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cover image ACM Conferences

AAMAS '02: Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1

July 2002

540 pages

ISBN:1581134800

DOI:10.1145/544741

Conference Chairs:
Maria Gini
University of Minnesota, USA
,
Toru Ishida
Kyoto University, Japan
,
Program Chairs:
Cristiano Castelfranchi
CNR and Università di Siena, Italy
,
W. Lewis Johnson
University of Southern California, USA

Copyright © 2002 ACM.

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Published: 15 July 2002

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AAMAS02: The First International Joint Conference on Autonomous Agents and Multi-Agent Systems ( formerly known as Autonomous Agents )

July 15 - 19, 2002

Bologna, Italy

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Cited By

Zambetta F(2008)Modelling Interactive Non-Linear StoriesComputational Intelligence in Multimedia Processing: Recent Advances10.1007/978-3-540-76827-2_5(119-138)Online publication date: 2008
https://doi.org/10.1007/978-3-540-76827-2_5
Shuen-Huei Guan Sheng-Yao Cho Yu-Te Shen Rung-Huei Liang Bing-Yu Chen Ming Ouhyoung (2004)Conceptual Farm2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763)10.1109/ICME.2004.1394701(2179-2182)Online publication date: 2004
https://doi.org/10.1109/ICME.2004.1394701

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