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

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

Ribeiro, AC (Autor) Outra; Rios, LM (Autor) Outra; Gomes, RM (Autor) Outra; Faria, BM (Autor) Outra; reis, lp (Autor) REIT

Ata de Conferência Internacional

Título: 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

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: 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

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6