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The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Wright Laboratory or the U. S. Government. The third and fourth authors were supported by fellowships from the Ministerio de Educación y Ciencia of(More)
One of the main obstacles in applying AI planning techniques to real problems is the difficulty to model the domains. Usually, this requires that people that have developed the planning system carry out the modeling phase since the representation depends very much on a deep knowledge of the internal working of the planning tools. On some domains such as(More)
When applying reinforcement learning in domains with very large or continuous state spaces, the experience obtained by the learning agent in the interaction with the environment must be generalized. The generalization methods are usually based on the approximation of the value functions used to compute the action policy and tackled in two different ways. On(More)
In this paper we advocate a learning method where a deduc-tive and an inductive strategies are combined to eeciently learn control knowledge. The approach consists of initially bounding the explanation to a predetermined set of problem solving features. Since there is no proof that the set is suucient to capture the correct and complete explanation for the(More)
Heuristic search with reachability-based heuristics is arguably the most successful paradigm in Automated Planning to date. In its earlier stages of development , heuristic search was proposed as both forward and backward search. Due to the disadvantages of backward search, in the last decade researchers focused mainly on forward search, and backward search(More)
General-purpose generative planners use domain-independent search heuristics to generate solutions for problems in a variety of domains. However, in some situations these heuristics force the planner to perform inefficiently or obtain solutions of poor quality. Learning from experience can help to identify the particular situations for which the(More)
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 this paper, we present samap, whose goal is to build a software tool to help different people visit different cities. This tool integrates modules that dynamically capture user models, determine lists of activities that can provide more utility to a user given the past experience of the system with similar users, and generates plans that can be executed(More)
Agents (hardware or software) that act autonomously in an environment have to be able to integrate three basic behaviors: planning, execution, and learning. This integration is mandatory when the agent has no knowledge about how its actions can affect the environment, how the environment reacts to its actions, or, when the agent does not receive as an(More)