research-article

Evolutionary-based learning of generalised policies for AI planning domains

Authors:

Henrik Westerberg,

Michelle Galea,

Dave HumphreysAuthors Info & Claims

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

Pages 1195 - 1202

https://doi.org/10.1145/1569901.1570062

Published: 08 July 2009 Publication History

Abstract

This work investigates the application of Evolutionary Computation (EC) to the induction of generalised policies used to solve AI planning problems. A policy is defined as an ordered list of rules that specifies which action to perform under which conditions; a solution (plan) to a planning problem is a sequence of actions suggested by the policy. We compare an evolved policy with one produced by a state-of-the art approximate policy iteration approach. We discuss the relative merits of the two approaches with a focus on the impact of the knowledge representation and the learning strategy. In particular we note that a strategy commonly and successfully used for the induction of classification rules, that of Iterative Rule Learning, is not necessarily an optimal strategy for the induction of generalised policies aimed at minimising the number of actions in a plan.

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Cited By

Pradheep Kumar K. Srinivasan N. (2019)Modified Backward Chaining Algorithm Using Artificial Intelligence Planning IoT ApplicationsEdge Computing and Computational Intelligence Paradigms for the IoT10.4018/978-1-5225-8555-8.ch009(153-169)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-8555-8.ch009
Azaria IElyasaf ASipper M(2018)Evolving Artificial General Intelligence for Video Game ControllersGenetic Programming Theory and Practice XIV10.1007/978-3-319-97088-2_4(53-63)Online publication date: 25-Oct-2018
https://doi.org/10.1007/978-3-319-97088-2_4
Elyasaf AVaks PMilo NSipper MZiv-Ukelson M(2016)Learning Heuristics for Mining RNA Sequence-Structure MotifsGenetic Programming Theory and Practice XIII10.1007/978-3-319-34223-8_2(21-38)Online publication date: 22-Dec-2016
https://doi.org/10.1007/978-3-319-34223-8_2
Show More Cited By

Index Terms

Evolutionary-based learning of generalised policies for AI planning domains
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
  2. Machine learning
    1. Learning settings
    2. Machine learning approaches
      1. Logical and relational learning
        Inductive logic learning

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cover image ACM Conferences

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

July 2009

2036 pages

ISBN:9781605583259

DOI:10.1145/1569901

General Chair:
Franz Rothlauf
University of Mainz, Germany

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 08 July 2009

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GECCO09: Genetic and Evolutionary Computation Conference

July 8 - 12, 2009

Québec, Montreal, Canada

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Pradheep Kumar K. Srinivasan N. (2019)Modified Backward Chaining Algorithm Using Artificial Intelligence Planning IoT ApplicationsEdge Computing and Computational Intelligence Paradigms for the IoT10.4018/978-1-5225-8555-8.ch009(153-169)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-8555-8.ch009
Azaria IElyasaf ASipper M(2018)Evolving Artificial General Intelligence for Video Game ControllersGenetic Programming Theory and Practice XIV10.1007/978-3-319-97088-2_4(53-63)Online publication date: 25-Oct-2018
https://doi.org/10.1007/978-3-319-97088-2_4
Elyasaf AVaks PMilo NSipper MZiv-Ukelson M(2016)Learning Heuristics for Mining RNA Sequence-Structure MotifsGenetic Programming Theory and Practice XIII10.1007/978-3-319-34223-8_2(21-38)Online publication date: 22-Dec-2016
https://doi.org/10.1007/978-3-319-34223-8_2
Pappa GOchoa GHyde MFreitas AWoodward JSwan J(2014)Contrasting meta-learning and hyper-heuristic researchGenetic Programming and Evolvable Machines10.1007/s10710-013-9186-915:1(3-35)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1007/s10710-013-9186-9
Elyasaf ASipper MAlba E(2013)HH-evolverProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2482707(1285-1292)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2464576.2482707
Elyasaf AHauptman ASipper M(2012)Evolutionary Design of FreeCell SolversIEEE Transactions on Computational Intelligence and AI in Games10.1109/TCIAIG.2012.22104234:4(270-281)Online publication date: Dec-2012
https://doi.org/10.1109/TCIAIG.2012.2210423
Elyasaf AHauptman ASipper MLanzi P(2011)GA-FreeCellProceedings of the 13th annual conference on Genetic and evolutionary computation10.1145/2001576.2001836(1931-1938)Online publication date: 12-Jul-2011
https://dl.acm.org/doi/10.1145/2001576.2001836

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