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Qualitative Spatial Abstraction in Reinforcement Learning (Cognitive Technologies)

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Teaching and learning are difficult tasks not only when people are involved but also with regard to computer programs and machines: When the teaching/learning units are too small, we cannot express sufficient context to teach a differentiated lesson; when they are too large, the complexity of the learning task can increase dramatically such that it will take forever to teach and learn a lesson. Thus, the question arises, how we can teach and learn complex concepts and strategies, or more specifically: How can the lesson be structured and scaled such that efficient and effective learning can be achieved?

Reinforcement learning has developed as a successful learning approach for domains that are not fully understood and that are too complex to be described in closed form. However, reinforcement learning does not scale well to large and continuous problems; furthermore, knowledge acquired in one environment cannot be transferred to new environments. Although this latter phenomenon also has been observed in human learning situations to a certain extent, it is desirable to generalize suitable insights for application also in new situations.

In this book, Lutz Frommberger investigates whether deficiencies of reinforcement learning can be overcome by suitable abstraction methods. He discusses various forms of spatial abstraction, in particular qualitative abstraction, a form of representing knowledge that has been thoroughly investigated and successfully applied in spatial cognition research. With his approach, Lutz Frommberger exploits spatial structures and structural similarity to support the learning process by abstracting from less important features and stressing the essential ones. The author demonstrates his learning approach and the transferability of knowledge by having his system learn in a virtual robot simulation system and consequently transferring the acquired knowledge to a physical robot.

Reinforcement learning has developed as a successful learning approach for domains that are not fully understood and that are too complex to be described in closed form. However, reinforcement learning does not scale well to large and continuous problems. Furthermore, acquired knowledge specific to the learned task, and transfer of knowledge to new tasks is crucial.   In this book the author investigates whether deficiencies of reinforcement learning can be overcome by suitable abstraction methods. He discusses various forms of spatial abstraction, in particular qualitative abstraction, a form of representing knowledge that has been thoroughly investigated and successfully applied in spatial cognition research. With his approach, he exploits spatial structures and structural similarity to support the learning process by abstracting from less important features and stressing the essential ones. The author demonstrates his learning approach and the transferability of knowledge by having his system learn in a virtual robot simulation system and consequently transfer the acquired knowledge to a physical robot. The approach is influenced by findings from cognitive science.   The book is suitable for researchers working in artificial intelligence, in particular knowledge representation, learning, spatial cognition, and robotics.  

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