Learning to Solve Planning Problems Efficiently by Means of Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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