Using genetic programming to learn and improve control knowledge.
type: Item
instance of: scholarly article
title: Using genetic programming to learn and improve control knowledge.
zbMATH Open document ID: 1043.68084
DOI: 10.1016/S0004-3702(02)00246-1
author: Ricardo Aler
author: Daniel Borrajo
author: Pedro Isasi
published in: Artificial Intelligence
publication date: 2003-01-21T00:00:00Z
review text: The purpose of this article is to present a multi-strategy approach to learn heuristics for planning. This multi-strategy system, called Hamlet-EvoCK, combines a learning algorithm specialized in planning (Hamlet) and a Genetic Programming (GP) based system (EvoCK: Evolution of Control Knowledge). Both systems are able to learn heuristics for planning on their own, but both of them have weaknesses. Based on previous experience and some experiments performed in this article, it is hypothesized that Hamlet handicaps are due to its example-driven operators and not having a way to evaluate the usefulness of its control knowledge. It is also hypothesized that even if Hamlet control knowledge is sometimes incorrect, it might be easily correctable. For this purpose, a GP-based stage is added, because of its complementary biases: GP genetic operators are not example-driven and it can use a fitness function to evaluate control knowledge. Hamlet and EvoCK are combined by seeding EvoCK initial population with Hamlet control knowledge. It is also useful for EvoCK to start from a knowledge-rich population instead of a random one. By adding the GP stage to Hamlet, the number of solved problems increases from 58\% to 85\% in the blocks world and from 50\% to 87\% in the logistics domain (0\% to 38\% and 0\% to 42\% for the hardest instances of problems considered).
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 1856156
zbMATH Keywords: Speedup learning
zbMATH Keywords: Genetic programming
zbMATH Keywords: Multi-strategy learning
zbMATH Keywords: Planning
Wikidata QID: Q62043506
describes a project that uses: Graphplan
describes a project that uses: Prodigy
describes a project that uses: UCPOP
describes a project that uses: Walksat
MaRDI profile type: Publication
cites work: Q4845460
cites work: Q4079017
cites work: Q4954178
cites work: Macro-operators: A weak method for learning
cites work: Q4845469
cites work: Integrating planning and learning: the PRODIGY architecture
full work available at URL: s0004-3702(02)00246-1
OpenAlex ID: W2038719169
DBLP publication ID: journals/ai/AlerBI02
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description: scientific article; zbMATH DE number 1856156