The theme ‘East versus west’ is an old topic for discussion, but one that is constantly being rehashed. Though it has many aspects, a number of elements remain vague. Do East and West each form a monolithic bloc? Where does the East begin in the West's eyes, and vice versa, the West for the East? What, if any, are the basic differences separating the two?Palaeontologists tell us that a dividing line indicating a divergence of traditions in stone tools can be (...) traced back to the Lower Palaeolithic era, that is, a million years ago, in the western region of India. To the east of that line, in other words in East and South-east Asia, the most common tools are choppers and chopping-tools, while to the west, in western Asia, Europe and Africa, the main tools are hand-axes. The former are suitable for working vegetable materials and making bamboo or wooden, therefore perishable, objects, which for this reason leave no trace in the form of archaeological clues. The hand-axes are capable of being worked into stylized shapes and are appropriate for dismembering large herbivores and cutting up their meat. (shrink)

本书的中心课题是儒家、佛教与道家之间的哲学对话,这一主题不仅有利于推动对儒学、佛学与道家思想的深入研究,也有利于不同学术领域之间的对话和交流,有着重要的实际的社会意义.

One most common problem in reinforcement learning systems (e.g. Q-learning) is to reduce the number of trials to converge to an optimal policy. As one of the solution to the problem, k-certainty exploration method was proposed. Miyazaki reported that this method could determine an optimal policy faster than Q-learning in Markov decision processes (MDPs). This method is very efficient learning method. But, we propose an improvement plan that makes this method more efficient. In k-certainty exploration method, in case there is (...) no k-uncertainty rule (rules which aren’t selected more than k times) in current state, an agent sometimes walks randomly until it finds a state where it can select a k-uncertainty rule. We think that this behavior is not efficient. To reduce this useless behavior, we propose combining k-certainty exploration method with Dynamic programming (DP). Miyazaki’s system uses DP after all rules are executed at least k times. But, our method uses k-certainty exploration method along with DP during learning. Our method, takes two pattern actions. In case an agent can select k-uncertainty rules, one of these rules is selected at random as k-certainty exploration method. In another case there is no k-uncertainty rule, behavior of agent is different from the behavior of k-certainty exploration method. In that case, our method uses DP to compute an optimal policy for moving from a current state to a state in which some k-uncertainty rules remain. The model for DP is constructed by using only known states. The outline will be described below. First, an agent makes a map constructed by using only known states. In the map, goals are states in which there are k-uncertainty rules and arbitrary state values are set in these states. Point to which attention should be paid is that the map is not given from outside. It is made from only experience. Next, each state values of states in which there are only k-certainty rules in the map are computed by DP (we used Policy Iteration). Finally, an agent continues to select greedy action until it arrives at a state in which it can select a k-uncertainty rule. By this improvement, we expect it can determine an optimal policy faster than k-certainty exploration method. And we have verified that our exploration method can determine an optimal policy faster than k-certainty exploration method by computer simulation. (shrink)