Knowledge-Free and Learning-Based Methods in Intelligent Game Playing

A work point count system coupled with back-propagation for solving double dummy bridge problem

“…In bridge games, though the basic representation includes value of each card as (Ace (A), King (K), Queen (Q), Jack (J ), 10,9,8,7,6,5,4,3,2) for assignment of cards into particular hands and into public or hidden subsets, a uniform linear transformation in the range 0.10 through 0.90 where 0.10 is assigned to the smallest card value 2 with an increment of 0.067 to the next card value i.e., 3 and so on till 0.90 for the highest card value A is assigned as represented in Table 1. Though the ranking is for 'bidding' purposes only, with respect to 'play' all suits are considered equal, unless one suit has been named as 'trumps', then it beats all the other cards.…”

“…It is played with a standard deck of 52 playing cards, where one of the players deals all of the cards, 13 to each player, in clockwise rotation, beginning with the player to the left of the dealer. In bridge games, basic representation includes value of each card as (Ace (A), King (K), Queen (Q), Jack (J ), 10,9,8,7,6,5,4,3,2) and suit as (Ƅ (Spades), the highest, Ɔ (Hearts), Ƈ (Diamonds), ƅ(Clubs), the lowest) for assignment of cards into particular hands and into public or hidden subsets, depending on the game rules. The ranking is for 'bidding' purposes only and in 'play' all suits are equal, unless one suit has been named as 'trumps', then it beats all the other cards.…”

“…The randomness and imperfect information built-in the game is drawing attention of many of the researchers and Computational Intelligence (CI) methods are applied to focus mainly on the aspect of learning in the game playing systems [10]. The bridge is a partnership game requiring four players, each player sits opposite to his partner and it is traditional to refer to the players according to their position at the table as North, East, South and West, so North and South are partners playing against East and West.…”

Reinforcement Learning in Games

“…Luckily, there are many high-quality review papers covering these topics. For classical board and card games, Fürnkranz (2001);Fürnkranz (2007) focuses on machine learning techniques, Schaeffer (2000); van den Herik et al (2002) and the recent book of Mańdziuk (2010) surveys general AI approaches including machine learning (and within that, RL). Ghory (2004) gives a good overview of TD variants applied to classical board games; while Galway et al (2008) focuses on evolutionary methods and computer games.…”

Generic Heuristic Approach to General Game Playing

“…Given the game rules written in the so-called GDL (Game Description Language) [2] a playing agent takes various actions towards learning and mastering the game. This includes analysis of the game rules, application of various learning and searching mechanisms, logic-based reasoning methods, efficient knowledge representation and many other techniques [3][4][5]. Integration of all these elements formulates an interesting and challenging research task.…”