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A Motivation Based Planning and Execution Framework

A Motivation Based Planning and Execution Framework
A Motivation Based Planning and Execution Framework
AI planning systems tend to be disembodied and are not situated within the environment for which plans are generated, thus losing information concerning the interaction between the system and its environment. This paper argues that such information may potentially be valuable in constraining plan formulation, and presents both an agent- and domainindependent architecture that extends the classical AI planning framework to take into account context, or the interaction between an autonomous situated planning agent and its environment. The paper describes how context constrains the goals an agent might generate, enables those goals to be prioritised, and constrains plan selection.
AI planning, agent architectures, motivations, plan evaluation
5-25
Coddington, A.M.
6e2fd5c3-0368-486f-9e82-965a41cc396e
Luck, Michael
94f6044f-6353-4730-842a-0334318e6123
Coddington, A.M.
6e2fd5c3-0368-486f-9e82-965a41cc396e
Luck, Michael
94f6044f-6353-4730-842a-0334318e6123

Coddington, A.M. and Luck, Michael (2004) A Motivation Based Planning and Execution Framework. International Journal on Artificial Intelligence Tools, 5-25.

Record type: Article

Abstract

AI planning systems tend to be disembodied and are not situated within the environment for which plans are generated, thus losing information concerning the interaction between the system and its environment. This paper argues that such information may potentially be valuable in constraining plan formulation, and presents both an agent- and domainindependent architecture that extends the classical AI planning framework to take into account context, or the interaction between an autonomous situated planning agent and its environment. The paper describes how context constrains the goals an agent might generate, enables those goals to be prioritised, and constrains plan selection.

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More information

Published date: 2004
Keywords: AI planning, agent architectures, motivations, plan evaluation
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 260223
URI: http://eprints.soton.ac.uk/id/eprint/260223
PURE UUID: 39b8e388-115d-423a-a8de-97d630036138

Catalogue record

Date deposited: 12 Jan 2005
Last modified: 23 Sep 2020 16:35

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