Learning to Solve Planning Problems Efficiently by Means of Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{aler:2001:ECJ,
-
author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi",
-
title = "Learning to Solve Planning Problems Efficiently by
Means of Genetic Programming",
-
journal = "Evolutionary Computation",
-
year = "2001",
-
volume = "9",
-
number = "4",
-
pages = "387--420",
-
month = "Winter",
-
keywords = "genetic algorithms, genetic programming, genetic
planning, evolving heuristics, planning, search. EvoCK,
STGP, blocks world, logistics, Prodigy4.0, STRIPS,
PDL40.",
-
ISSN = "1063-6560",
-
URL = "http://www.mitpressjournals.org/doi/pdf/10.1162/10636560152642841",
-
DOI = "doi:10.1162/10636560152642841",
-
size = "34 pages",
-
abstract = "Declarative problem solving, such as planning, poses
interesting challenges for Genetic Programming (GP).
There have been recent attempts to apply GP to planning
that fit two approaches: (a) using GP to search in plan
space or (b) to evolve a planner. In this article, we
propose to evolve only the heuristics to make a
particular planner more efficient. This approach is
more feasible than (b) because it does not have to
build a planner from scratch but can take advantage of
already existing planning systems. It is also more
efficient than (a) because once the heuristics have
been evolved, they can be used to solve a whole class
of different planning problems in a planning domain,
instead of running GP for every new planning problem.
Empirical results show that our approach (EVOCK) is
able to evolve heuristics in two planning domains (the
blocks world and the logistics domain) that improve
PRODIGY4.0 performance. Additionally, we experiment
with a new genetic operator Instance-Based Crossover
that is able to use traces of the base planner as raw
genetic material to be injected into the evolving
population.",
- }
Genetic Programming entries for
Ricardo Aler Mur
Daniel Borrajo
Pedro Isasi Vinuela
Citations