Traditional Wisdom and Monte Carlo Tree Search Face-to-Face in the Card Game Scopone

We present the design of a competitive artificial intelligence for Scopone, a popular Italian card game. We compare rule-based players using the most established strategies (one for beginners and two for advanced players) against players using Monte Carlo Tree Search (MCTS) and Information Set Monte Carlo Tree Search (ISMCTS) with different reward functions and simulation strategies. MCTS requires complete information about the game state and thus implements a cheating player while ISMCTS can deal with incomplete information and thus implements a fair player. Our results show that, as expected, the cheating MCTS outperforms all the other strategies; ISMCTS is stronger than all the rule-based players implementing well-known and most advanced strategies and it also turns out to be a challenging opponent for human players.Comment: Preprint. Accepted for publication in the IEEE Transaction on Game

Adaptative play in texas hold'em poker

International audienceWe present a Texas Hold'em poker player for limit heads-up games. Our bot is designed to adapt automatically to the strategy of the opponent and is not based on Nash equilibrium computation. The main idea is to design a bot that builds beliefs on his opponent's hand. A forest of game trees is generated according to those beliefs and the solutions of the trees are combined to make the best decision. The beliefs are updated during the game according to several methods, each of which corresponding to a basic strategy. We then use an exploration-exploitation bandit algorithm, namely the UCB (Upper Confidence Bound), to select a strategy to follow. This results in a global play that takes into account the opponent's strategy, and which turns out to be rather unpredictable. Indeed, if a given strategy is exploited by an opponent, the UCB algorithm will detect it using change point detection, and will choose another one. The initial resulting program , called Brennus, participated to the AAAI'07 Computer Poker Competition in both online and equilibrium competition and ranked eight out of seventeen competitors

Review and analysis of research on video games and artificial intelligence: a look back and a step forward

This article shows the intimate relationship between Artificial Intelligence (AI) and video games research in 13 categories of analysis based on a bibliometric survey carried out in the Scopus database. We first briefly reviewed the relation between video games and AI. Then, we introduced the methodology of literature collection, presented and discussed the query, as well the flow of data treatment in the applications and plugins used. Since the article is concerned with a historical point of view of the relationship between digital games and AI the results were many and, therefore, we focused on the top 10 of each ranking, and discussed these results separately. Finally, we discuss the limitations of our review, proposing future research directions for scholars.info:eu-repo/semantics/publishedVersio

Artificial intelligence and machine learning

Within the last decade, the application of "artificial intelligence" and "machine learning" has become popular across multiple disciplines, especially in information systems. The two terms are still used inconsistently in academia and industry—sometimes as synonyms, sometimes with different meanings. With this work, we try to clarify the relationship between these concepts. We review the relevant literature and develop a conceptual framework to specify the role of machine learning in building (artificial) intelligent agents. Additionally, we propose a consistent typology for AI-based information systems. We contribute to a deeper understanding of the nature of both concepts and to more terminological clarity and guidance—as a starting point for interdisciplinary discussions and future research