Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2011

Abstract

Hierarchical planning is an approach of planning by composing and executing hierarchically arranged plans to solve some problems. Most symbolic-based hierarchical planners have been devised to allow the knowledge to be described expressively. However, a great challenge is to automatically seek and acquire new plans on the fly. This paper presents a novel neural-based model of hierarchical planning that can seek and acquired new plans on-line if the necessary knowledge are lacking. Inspired by findings in neuropsychology, plans can be inherently learnt, retrieved, and manipulated simultaneously rather than discretely processed like in most symbolic approaches. Using a multi-channel adaptive resonance theory (fusion ART) neural network as the basic building block, the so called iFALCON architecture can capture and manipulate sequential and hierarchical relations of plans on the fly. Case studies using a blocks world domain and unreal tournament video game demonstrate that the model can be used to execute, plan, and discover plans and procedural knowledge through experiences.

Keywords

adaptive resonance theory, hierarchical planning, plan learning

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Data Science and Engineering; Intelligent Systems and Optimization

Publication

Proceedings of the Conference on Technologies and Applications of Artificial Intelligence (TAAI 2011)

ISBN

9781457721748

Identifier

10.1109/TAAI.2011.24

Publisher

IEEE

City or Country

Taoyuan, Taiwan

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