Data Set Used
― This paper presents a new reinforcement learning method, called Temporal Difference Learning with Monte Carlo simulation (TDMC), which uses a combinationofTemporalDifferenceLearning(TD)and winning probability in each non-terminal position.
Introduction. According to the analysis of grandmaster-like strategies in Shogi [Iida and Uiterwijk 1993], it is important for a teacher, at the beginning stages of teaching, to intentionally lose an occasional game against a novice opponent, or to play less than optimally in order to give the novice some prospects of winning, without this being noticed by… (More)
We describe a corpus-based approach of natural language dialogue system. The characteristic is that all the system's behaviors, like processing and understanding dialogues and generating responses, depend on corpora. As a result, the system can handle any language and any topic. This paper aims to explain the whole architecture and individual technology… (More)
— Monte-Carlo method recently has produced good results in Go. Monte-Carlo Go uses a move which has the highest mean value of either winning percentage or final score. In a past research, winning percentage is superior to final score in Monte-Carlo Go. We investigated them in BlokusDuo, which is a relatively new game, and showed that Monte-Carlo using final… (More)
We describe an N-gram based syntactic analysis using a dependency grammar. Instead of generalizing syntactic rules, N-gram information of parts of speech is used to segment a sequence of words into two clauses. A special part of speech, called segmentation word, which corresponds to the beginning or end symbol of clauses is introduced to express a sentence… (More)