{'fraction_non_alphanumeric': 0.023608768971332208, 'fraction_numerical': 0.007869589657110737, 'mean_word_length': 4.798045602605863, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Temporal Model with Influence and Territory in Computer

In computer Go programming, utilizing knowledge from look-ahead intelligently is an area worthy of research. Look-ahead in the game of Go using brute-force search is not a feasible option [18]. Even when simple lookahead is possible, specialized Go game concepts only understood by skilled Go players makes programming computer Go difficult. The authors believe that look-ahead evaluation in computer Go programs should mimic the same behavior of skilled Go players a dynamic evaluation of influence and territory. In this paper, we describe a way to mimic the lookahead behavior of skilled Go players in computer Go programs. We achieve this by setting up a temporal model that utilizes future Go board influence data and territorial data to determine the effectiveness of current candidate moves. In this model, we apply an Influence Weighting Curve (IWC) and a Territory Weighting Curve (TWC) to the normalized value of these future influence and territory values. The result of our techniques shows that our approach can increase the winning percentage of our Go program by 48%. Our temporal model successfully mimics the basic thought process of a skilled Go player. Therefore the technique of using a Influence Weighting Curve (IWC) and a Territory Weighting Curve (TWC) should be applied to any Go programs that involve look-ahead. We applied our technique on SlugGo a modified version of GNU Go that uses parallel computing techniques. GNU Go is an open-source computer Go program that had won the crown of 2003 Computer Olympiad [9]. After we applied our temporal influence/territory model to SlugGo, we won over 90% of the games against the official release of GNU Go (version 3.4) after giving it 2 handicaps.