We investigated the effects of everyday language exposure on the prediction of orthographic and phonological forms of a highly predictable word during listening comprehension. Native Japanese speakers in Tokyo (Experiment 1) and Berlin (Experiment 2) listened to sentences that contained a predictable word and viewed four objects. The critical object represented the target word (e.g., /sakana/;fish), an orthographic competitor (e.g., /tuno/;horn), a phonological competitor (e.g., /sakura/;cherry blossom), or an unrelated word (e.g., /hon/;book). The three other objects were distractors. The Tokyo (...) group fixated the target and the orthographic competitor over the unrelated objects before the target word was mentioned, suggesting that they pre-activated the orthographic form of the target word. The Berlin group showed a weaker bias toward the target than the Tokyo group, and they showed a tendency to fixate the orthographic competitor only when the orthographic similarity was very high. Thus, prediction effects were weaker in the Berlin group than in the Tokyo group. We found no evidence for the prediction of phonological information. The obtained group differences support probabilistic models of prediction, which regard the built-up language experience as a basis of prediction. (shrink)

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)

十九世紀後半における英国唯美主義を支えた日本趣味の実態はどのようなものだったのか。ロセッティ、スウィンバーン、ペイター、ワイルド、ホイッスラーらの藝術について、さらに英国唯美主義の素地としてのギリシア のエピクーロス的世界、西脇順三郎を初めとする日本の詩人達とワイルド受容、ロセッティやペイターの系譜に連なる唯美主義者としてのハーンなど、両国の藝術の関わりを悠遠な視座から読み解く比較藝術論。図版多数。 .

死後の魂のヴィジョンと永遠回帰の宇宙論を語るこの破天荒な構想力、九鬼周造とはいったい何者か。.

40億年に及ぶとされる、地球における生命の歴史。共通の祖先から進化した生きものたちは、多様でありながら共通点もあわせ持っています。いのちとは何か?いのちはどのようにして生まれたのか?どのように考えられ てきたのか?その歴史をひもときながら、私たちの来た道と、これから行く道を、探ってみましょう。.

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)