A self-organizing neural architecture integrating desire, intention and reinforcement learning

This paper presents a self-organizing neural architecture that integrates the features of belief, desire, and intention (BDI) systems with reinforcement learning. Based on fusion Adaptive Resonance Theory (fusion ART), the proposed architecture provides a unified treatment for both intentional and r...

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Main Authors: TAN, Ah-hwee, FENG, Yu-Hong, ONG, Yew-Soon
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2010
Subjects:
BDI
Online Access:https://ink.library.smu.edu.sg/sis_research/5217
https://ink.library.smu.edu.sg/context/sis_research/article/6220/viewcontent/1_s2.0_S0925231209004196_main.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-62202020-07-23T18:36:05Z A self-organizing neural architecture integrating desire, intention and reinforcement learning TAN, Ah-hwee FENG, Yu-Hong ONG, Yew-Soon This paper presents a self-organizing neural architecture that integrates the features of belief, desire, and intention (BDI) systems with reinforcement learning. Based on fusion Adaptive Resonance Theory (fusion ART), the proposed architecture provides a unified treatment for both intentional and reactive cognitive functionalities. Operating with a sense-act-learn paradigm, the low level reactive module is a fusion ART network that learns action and value policies across the sensory, motor, and feedback channels. During performance, the actions executed by the reactive module are tracked by a high level intention module (also a fusion ART network) that learns to associate sequences of actions with context and goals. The intention module equips the architecture with deliberative planning capabilities, enabling it to purposefully maintain an agenda of actions to perform and to reduce the need of constantly sensing the environment. Through reinforcement learning, plans can also be evaluated and refined without the rigidity of user-defined plans. We examine two strategies for combining the intention and reactive modules for decision making in a real time environment. Our experiments based on a minefield navigation domain show that the integrated architecture is able to learn plans efficiently, achieve good plan utilization, and combine both intentional and reactive action execution to yield a robust performance. 2010-03-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5217 info:doi/10.1016/j.neucom.2009.11.012 https://ink.library.smu.edu.sg/context/sis_research/article/6220/viewcontent/1_s2.0_S0925231209004196_main.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Reinforcement learning Plan learning Self-organizing neural networks BDI Minefield navigation Computer and Systems Architecture Databases and Information Systems OS and Networks
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Reinforcement learning
Plan learning
Self-organizing neural networks
BDI
Minefield navigation
Computer and Systems Architecture
Databases and Information Systems
OS and Networks
spellingShingle Reinforcement learning
Plan learning
Self-organizing neural networks
BDI
Minefield navigation
Computer and Systems Architecture
Databases and Information Systems
OS and Networks
TAN, Ah-hwee
FENG, Yu-Hong
ONG, Yew-Soon
A self-organizing neural architecture integrating desire, intention and reinforcement learning
description This paper presents a self-organizing neural architecture that integrates the features of belief, desire, and intention (BDI) systems with reinforcement learning. Based on fusion Adaptive Resonance Theory (fusion ART), the proposed architecture provides a unified treatment for both intentional and reactive cognitive functionalities. Operating with a sense-act-learn paradigm, the low level reactive module is a fusion ART network that learns action and value policies across the sensory, motor, and feedback channels. During performance, the actions executed by the reactive module are tracked by a high level intention module (also a fusion ART network) that learns to associate sequences of actions with context and goals. The intention module equips the architecture with deliberative planning capabilities, enabling it to purposefully maintain an agenda of actions to perform and to reduce the need of constantly sensing the environment. Through reinforcement learning, plans can also be evaluated and refined without the rigidity of user-defined plans. We examine two strategies for combining the intention and reactive modules for decision making in a real time environment. Our experiments based on a minefield navigation domain show that the integrated architecture is able to learn plans efficiently, achieve good plan utilization, and combine both intentional and reactive action execution to yield a robust performance.
format text
author TAN, Ah-hwee
FENG, Yu-Hong
ONG, Yew-Soon
author_facet TAN, Ah-hwee
FENG, Yu-Hong
ONG, Yew-Soon
author_sort TAN, Ah-hwee
title A self-organizing neural architecture integrating desire, intention and reinforcement learning
title_short A self-organizing neural architecture integrating desire, intention and reinforcement learning
title_full A self-organizing neural architecture integrating desire, intention and reinforcement learning
title_fullStr A self-organizing neural architecture integrating desire, intention and reinforcement learning
title_full_unstemmed A self-organizing neural architecture integrating desire, intention and reinforcement learning
title_sort self-organizing neural architecture integrating desire, intention and reinforcement learning
publisher Institutional Knowledge at Singapore Management University
publishDate 2010
url https://ink.library.smu.edu.sg/sis_research/5217
https://ink.library.smu.edu.sg/context/sis_research/article/6220/viewcontent/1_s2.0_S0925231209004196_main.pdf
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