- author = "Ricardo Aler Mur",
- title = "Programacion Genetica de Heuristicas para Planificacion",
- school = "Facultad de Informatica de la Universidad Politecnica de Madrid",
- year = "1999",
- address = "Spain",
- month = jul,
- keywords = "genetic algorithms, genetic programming, Planning, Problem Solving, Rule Based System",
-
URL = "
http://oa.upm.es/1101/1/10199907.pdf",
- size = "167 pages",
-
abstract = "The aim of this thesis is to use and extend the
machine learning genetic programming (GP) paradigm to
learn control knowledge for domain independent
planning. GP will be used as a standalone technique and
as part of a multi-strategy system.
Planning is the problem of finding a sequence of steps to transform an initial state in a final state. Finding a correct plan is NP-hard. A solution proposed by Artificial Intelligence is to augment a domain independent planner with control knowledge, to improve its efficiency. Machine learning techniques are used for that purpose. However, although a lot has been achieved, the domain independent planning problem has not been solved completely, therefore there is still room for research.
The reason for using GP to learn planning control knowledge is twofold. First, it is intended for exploring the control knowledge space in a less biased way than other techniques. Besides, learning search control knowledge with GP will consider the planning system, the domain theory, planning search and efficiency measures in a global manner, all at the same time. Second, GP flexibility will be used to add useful biases and characteristics to another learning method that lacks them (that is, a multi-strategy GP based system). In the present work, Prodigy will be used as the base planner and Hamlet will be used as the learning system to which useful characteristics will be added through GP. In other words, GP will be used to solve some of Hamlet limitations by adding new biases/characteristics to Hamlet.
In addition to the main goal, this thesis will design and experiment with methods to add background knowledge to a GP system, without modifying its basic algorithm. The first method seeds the initial population with individuals obtained by another method (Hamlet). Actually, this is the multi-strategy system discussed in the later paragraph. The second method uses a new genetic operator (instance based crossover) that is able to use instances/examples to bias its search, like other machine learning techniques.
To test the validity of the methods proposed, extensive empirical and statistical validation will be carried out.",
-
notes = "In Spanish: Genetic Programming of Heuristics for
Planning School of Computer Science at Polytechnic
University of Madrid Author: Ricardo Aler Mur
Supervisors: Daniel Borrajo Millan and Pedro Isasi
Vinuela
",
