In this research, we aim to create a computer player that gives fun to the opponent. Research on game AI has spread widely in recent years, and many games are being studied. Some of those studies have made remarkable results. Game research is aimed at strengthening computer players. However, it is unknown whether a computer player who is too strong is good. There may also be opponents who think that a computer player is not interesting if it is too strong. Therefore, we thought whether we could create a computer player who entertains the opponent while maintaining a certain degree of strength. To realize this idea, we use the Monte Carlo Tree Search. We tried to create a computer player that gives fun to the opponent by improving the Monte Carlo Tree Search. As a result of some experiments, we succeeded in giving fun, although it was a first step. On the other hand, many problems were found through experiments. In future, it is necessary to solve these problems. Communications recruitment of experimenters and adjustment of algorithms are required. Furthermore, it is necessary to increase the number of experiments. After that, a detailed analysis is carried out and the computer player is evaluated.
In this paper, we provide a new approach to classify and recognize the acoustic events for multiple autonomous robots systems based on the deep learning mechanisms. For disaster response robotic systems, recognizing certain acoustic events in the noisy environment is very effective to perform a given operation. As a new approach, trained deep learning networks which are constructed by RBMs, classify the acoustic events from input waveform signals. From the experimental results, usefulness of our approach is discussed and verified.
This study proposes the concepts of “behavior simple” and “behavior composed.” Behavior simple means primitive behavior, and behavior composed is a combination of behaviors simple. An artificial creature first learns some behaviors simple. Then, it learns behavior composed as a combination of behaviors simple, responding to the change of environment. This concept is applied to an Artificial Flying Creature (AFC). The AFC learns two types of flight independently: flapping and gliding. After that, it learns a sophisticated behavior by alternatively selecting these behaviors. Simulation results prove that adequate flight occurs by alternatively using flapping and gliding.
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