Knowledge representation issues in control knowledge learning

Seventeenth International Conference on Machine Learning. Stanford, CA, USA, 29 June-2 July, 2000Knowledge representation is a key issue for any machine learning task. There have already been many comparative studies about knowledge representation with respect to machine learning in classication tasks. However, apart from some work done on reinforcement learning techniques in relation to state representation, very few studies have concentrated on the eect of knowledge representation for machine learning applied to problem solving, and more specically, to planning. In this paper, we present an experimental comparative study of the eect of changing the input representation of planning domain knowledge on control knowledge learning. We show results in two classical domains using three dierent machine learning systems, that have previously shown their eectiveness on learning planning control knowledge: a pure ebl mechanism, a combination of ebl and induction (hamlet), and a Genetic Programming based system (evock).Publicad

Learning to solve planning problems efficiently by means of genetic programming

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.Publicad

Collective and Single-particle Motion in Beyond Mean Field Approaches

We present a novel nuclear energy density functional method to calculate spectroscopic properties of atomic nuclei. Intrinsic nuclear quadrupole deformations and rotational frequencies are considered simultaneously as the degrees of freedom within a symmetry conserving configuration mixing framework. The present method allows the study of nuclear states with collective and single-particle character. We calculate the fascinating structure of the semi-magic 44S nucleus as a first application of the method, obtaining an excellent quantitative agreement both with the available experimental data and with state-of-the-art shell model calculations.Comment: 5 pages, 4 figures, accepted for publication in Phys. Rev. Let

Planning and learning group

Peer Reviewe

The School of Doloriñas: memory, heartbeat and presence of an educational time

[Resumo] Exalumnos da escola de ferrado inmortalizada por dona Julia Minguillón na famosa pintura “A Escola de Doloriñas”, evocan a súa infancia. As típicas trasnadas, as leccións aprendidas a carón de Doloriñas e algunha que outra privación afloran na mente destes nenos e nenas que hoxe roldan os setenta anos. Un destes alumnos completou a súa formación na escola pública e explica con todo luxo de detalles a principais diferencias dela co pobre, pero acolledor habitáculo onde aprendeu as primeiras letras. A memoria de dous mestres e os datos aportados polos investigadores Narciso de Gabriel e Vicente Peña veñen a completar esta panorámica sobre algunhas das escolas de Galicia[Abstract] Former alumni of the “escola de ferrado” (rural school system in which the schoolteacher was paid in kind instead of receiving salary; a “ferrado” is a measure of corn, for example) immortalized by Julia Minguillón in the famous painting entitled “A Escola de Doloriñas” conjure up memories from their childhood. The typical pranks, the lessons learned with Doloriñas and some of the deprivations experienced are still very much alive in the minds of these boys and girls, who today are nearing seventy. One of these former students finished his education in the public school system and explains in detail, the main differences between the latter and the poor, but cozy little schoolhouse where he learned his ABC’s. The memory of the teachers and the data provided by researchers Narciso de Gabriel and Vicente Peña complete this panoramic overview of some of the schools in Galici

Error Analysis and Correction for Weighted A*'s Suboptimality (Extended Version)

Weighted A* (wA*) is a widely used algorithm for rapidly, but suboptimally, solving planning and search problems. The cost of the solution it produces is guaranteed to be at most W times the optimal solution cost, where W is the weight wA* uses in prioritizing open nodes. W is therefore a suboptimality bound for the solution produced by wA*. There is broad consensus that this bound is not very accurate, that the actual suboptimality of wA*'s solution is often much less than W times optimal. However, there is very little published evidence supporting that view, and no existing explanation of why W is a poor bound. This paper fills in these gaps in the literature. We begin with a large-scale experiment demonstrating that, across a wide variety of domains and heuristics for those domains, W is indeed very often far from the true suboptimality of wA*'s solution. We then analytically identify the potential sources of error. Finally, we present a practical method for correcting for two of these sources of error and experimentally show that the correction frequently eliminates much of the error.Comment: Published as a short paper in the 12th Annual Symposium on Combinatorial Search, SoCS 201