Daniel Borrajo

Learn More
Planning is a complex reasoning task that is well suited for the study of improving performance and knowledge by learning, i.e. by accumulation and interpretation of planning experience. PRODIGY is an architecture that integrates planning with multiple learning mechanisms. Learning occurs at the planner’s decision points and integration in PRODIGY is(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)
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)
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)
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)
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)
Human activity recognition aims to infer the actions of one or more persons from a set of observations captured by sensors. Usually, this is performed by following a fixed length sliding window approach for the features extraction where two parameters have to be fixed: the size of the window and the shift. In this paper we propose a different approach using(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)
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)