Inference and Learning in Planning
(invited lecture for DS 2009)

Author: Hector Geffner

Affiliation: ICREA & Universitat Pompeu Fabra, Barcelona, Spain

Abstract. Planning is concerned with the development of solvers for a wide range of models where actions must be selected for achieving goals. In these models, actions may be deterministic or not, full or partial sensing may be available or not, and so on. In the last few years, significant progress has been made, resulting in algorithms that can produce plans effectively in a variety of settings. These developments have to do with the formulation and use of general inference techniques and transformations.

In this talk, I'll review the inference techniques used for solving individual planning instances, and discuss the use of learning methods and transformations for solving complete planning domains. The former methods lead to the automatic derivations of heuristic functions; the latter, to the automatic derivation of compact policies and useful domain concepts.

Biography: Hector Geffner got his Ph.D in UCLA with a dissertation that was co-winner of the 1990 ACM Dissertation Award. Then he worked as Staff Research Member at the IBM T.J. Watson Research Center in NY, USA and at the Universidad Simon Bolivar, in Caracas, Venezuela. He is currently a researcher at ICREA and a professor at the Universitat Pompeu Fabra in Barcelona, Spain. His work in the last ten years has been in the area of planning. Hector Geffner is a AAAI fellow, Associate Editor of JAIR, and Editorial Board member at AIJ.

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