Understanding consciousness is a truly multidisciplinary project, attracting intense interest from researchers and theorists from diverse backgrounds. Thus, we now have computational scientists, neuroscientists, and philosophers all engaged in the same effort. This book draws together the work of leading researchers around the world, providing insights from these three general perspectives. The work is highlighted by a rare look at work being conducted by Japanese researchers.

Wegner's monograph presents the view that conscious will is a feeling that we experience when we perform an action through a mechanistic process of the brain, rather than a mental force that causes the action. The view is supported by several lines of evidence in which conscious will is dissociated from the actual performance of voluntary movements, as in automatism. The book further extends an insightful analysis of the mental system behind the illusion of conscious will and inspires neuroscientists to (...) reflect on its neural substrates. (shrink)

Samuel Lytler Metcalfe was an American chemist and physician who wrote a voluminous work, Caloric Its Mechanical Chemical and Vital Agencies in the Phenomena of Nature ; attempting to account for all natural phenomena in terms of caloric. The book came out at the time when the concept of caloric was being gradually discarded and the law of conservation of energy was about to appear. Metcalfe was convinced that caloric would be the key to unlock the secrets of nature; in (...) order to develop the practical implications of his views he made research trips twice to England , and there he completed Caloric. (shrink)

「もの」とは何か、自明の基礎語を徹底して批評・解明。「もの」と「自覚」の矛盾・対抗を基本軸にしながら、移動・推移する「情」を普遍的媒介者として新たに位置づけ、日本思想史についての核心的な断面を明らかに した。.

Reinforcement learning has recently received much attention as a learning method for complicated systems, e.g., robot systems. It does not need prior knowledge and has higher capability of reactive and adaptive behaviors. However increase in dimensionality of the action-state space makes it diffcult to accomplish learning. The applicability of the existing reinforcement learning algorithms are effective for simple tasks with relatively small action-state space. In this paper, we propose a new reinforcement learning algorithm: “Q-learning with Dynamic Structuring of Exploration Space (...) Based on Genetic Algorithm ”. The algorithm is applicable to systems with high dimensional action and interior state spaces, for example a robot with many redundant degrees of freedom. To demonstrate the effectiveness of the proposed algorithm simulations of obstacle avoidance by a 50 links manipulator have been carried out. It is shown that effective behavior can be learned by using the proposed algorithm. (shrink)

Reinforcement learning is very effective for robot learning. Because it does not need priori knowledge and has higher capability of reactive and adaptive behaviors. In our previous works, we proposed new reinforcement learning algorithm: “Q-learning with Dynamic Structuring of Exploration Space Based on Genetic Algorithm (QDSEGA)”. It is designed for complicated systems with large action-state space like a robot with many redundant degrees of freedom. And we applied it to 50 link manipulator and effective behavior is acquired. However optimality and (...) fault tolerance of the proposed algorithm were not considered and to demonstrate effectiveness of the proposed algorithm other applications are necessary. Acquiring of locomotion patterns by a multi-legged robot is a very interesting problem. As it has many redundant degrees of freedom, application of usual reinforcement learning is difficult and an optimal locomotion has not been acquired using previous reinforcement learning algorithm. And the redundancy of the robot is effective to the fault tolerance and various locomotion patterns can be acquired for adapting the faults of the legs. In this paper, we applied QDSEGA to acquiring of locomotion pattern by the multi-legged robot and considered the optimality and fault tolerance. Effective behavior has been obtained by using our proposed algorithm. (shrink)