Data Mining in Adversarial Search - Players Movement Prediction in Connect 4 Games

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Ribeiro, AC (Author) Other; Rios, LM (Author) Other; Gomes, RM (Author) Other; Faria, BM (Author) Other; reis, lp (Author) REIT

Conference proceedings International

Title: 2017 12TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI)

12th Iberian Conference on Information Systems and Technologies (CISTI)

Lisbon, PORTUGAL, JUN 21-24, 2017

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Other information

Authenticus ID: P-00N-RR0

DOI: 10.23919/cisti.2017.7976065

Abstract (EN): Knowledge Discovery in Databases (KDD) is a major innovation in knowledge extraction. This knowledge can be extracted to recognize patterns or behaviors. Board games playing patterns are a concise experiment on testing data mining methods in order to find such patterns and behaviors. In this work a Connect-4 game is simulated with several distinct players with different characteristics. Most of these distinct players have intelligent game playing abilities, whereas others are simpler and play by very simple rules. The work uses three different data-mining algorithms in order to classify the players and their moves. Analyzing the results achieved we can conclude that General Linear Model leads to better results in terms of accuracy, class precision and class recall

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6