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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(More)
In competitive domains, the knowledge about the opponent can give players a clear advantage. This idea lead us in the past to propose an approach to acquire models of opponents, based only on the observation of their input-output behavior. If opponent outputs could be accessed directly, a model can be constructed by feeding a machine learning method with(More)
One of the most important issues in Adaptive and Intelligent Educational Systems (AIES) is to define effective pedagogical policies for tutoring students according to their needs. This paper proposes to use Reinforcement Learning (RL) in the pedagogical module of an educational system so that the system learns automatically which is the best peda-gogical(More)
In the last decade, there has been a strong and increasing interest on building fast planning systems. From it, we have seen an enormous improvement in relation to the solvability horizon of current planning techniques. Most of these techniques rely on the translation of the predicate logic description of domains into propositional representations of them.(More)
Stacking is one of the most used techniques for combining classifiers and improves prediction accuracy. Early research in stacking showed that selecting the right classifiers, their parameters and the metaclassifiers was the main bottleneck for its use. Most of the research on this topic selects by hand the right combination of classifiers and their(More)
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(More)
The Robosoccer simulator is a challenging environment for artificial intelligence, where a human has to program a team of agents and introduce it into a soccer virtual environment. Most usually, Robosoccer agents are programmed by hand. In some cases, agents make use of Machine learning (ML) to adapt and predict the behavior of the opposite team, but the(More)
This paper presents a model to define heterogeneous agents that solve problems by sharing the knowledge retrieved from the Web and cooperating among them. The control structure of those agents is based on a general purpose Multi-Agent architecture (SkeletonA-gent) based on a deliberative approach. Any agent in the architecture is built by means of several(More)