@book {IOPORT.05817416,
    author       = {Frommberger, Lutz},
    title        = {Qualitative spatial abstraction in reinforcement learning. Foreword by Christian Freksa.},
    year         = {2010},
    isbn         = {978-3-642-16589-4},
    pages        = {xvii, 174~p.},
    publisher    = {Berlin: Springer},
    abstract     = {The volume under review aims to analyze the possibilities of overcomming difficiencies of reinforcement learning by means of suitable abstraction methods. Various methods of spatial abstraction are discussed from the point of view of effective support of the learning process. The volume is divided into eight main chapters, where the first two of them characterize introductory and auxiliary concepts, and the last one summarizes the presented results and outlines the trends of future research. The remaining five chapters form the main part of the book and focus on various aspects of spatial abstraction methods (the knowledge transfer in reinforcement learning, qualitative state space abstraction, generalization and transfer learning in the frame of the considered model), and proposition abstraction as a state space representation. Five demonstrations of empirical evaluation of the presented model are given at the end of this part. The volume is completed by a representative list of references (158 items) and an index.},
    reviewer     = {Milan Mare\v s (Praha)},
    identifier   = {05817416},
}