This thesis is concerned with intention recognition for a humanoid robot and investigates how the challenges of uncertain and incomplete observations, a high degree of detail of the used models, and real-time inference may be addressed by modeling the human rationale as hybrid, dynamic Bayesian networks and performing inference with these models. The key focus lies on the automatic identification of the employed nonlinear stochastic dependencies and the situation-specific inference.
This thesis is concerned with intention recognition for a humanoid robot and investigates how the challenges of uncertain and incomplete observations, a high degree of detail of the used models, and real-time inference may be addressed by modeling the human rationale as hybrid, dynamic Bayesian networks and performing inference with these models. The key focus lies on the automatic identification of the employed nonlinear stochastic dependencies and the situation-specific inference.
Neural plasticity--the brain's ability to change in response to normal developmental processes, experience, and injury--is a critically important phenomenon for both neuroscience and psychology. Increasing evidence about the extent of plasticity--long past the supposedly critical first three years--has recently emerged. Neural Plasticity offers the first succinct and lucid integration of this research and its implications. Pointing out the negative and the positive consequences of plasticity, Peter Huttenlocher describes plasticity in children and adults (in normal aging and in response to trauma), in sensory systems, the motor cortex, higher cortical functions, and language development, proceeding system by system, and paying particular attention to the cerebral cortex. One of the book's strengths is its range of references, not only to studies on human subjects but to the experimental study of animal models as well. This book will be a unique contribution to research and to the literature on clinical neuroscience.
In enforcing EU competition law, the Commission employs a unique doctrine of parental antitrust liability: it imposes fines on the parent company of an infringing subsidiary in cases where the parent exercises decisive influence over the subsidiary's commercial policy. Critics of this contentious aspect of EU competition law believe that the doctrine is unfair, ineffective, obscure, disproportionate, contrary to due process, and based upon a dubious, if not extremely flimsy, justificatory foundation. Such criticism raises serious and unanswered questions about the legitimacy of the Commission's efforts to enforce competition law. Parental Liability in EU Competition Law: A Legitimacy-Focused Approach is the first monograph to be dedicated to this controversial topic. Written by Professor Peter Whelan, the book contends that, although the general concept of parental liability can be justified in principle, the current EU-level doctrine of parental antitrust liability in fact suffers from a distinct and problematic lack of legitimacy. More specifically, the said doctrine displays significant deficiencies with respect to effectiveness, fairness, and legality. Given this undesirable state of affairs, Parental Liability in EU Competition Law offers a fully-rationalised, reformulated approach to parental antitrust liability for EU competition law violations that is built around the notion of parental fault. That approach provides a solid normative account of how to impose parental antitrust liability in a manner that is theoretically robust, effective in practice, fair in substance, and legally sound.
This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model uncertainties consistently into account during long-term planning to reduce model bias. Second, we propose principled algorithms for robust filtering and smoothing in GP dynamic systems.
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