The game of Poker is an excellent test bed for studying opponent modeling methodologies applied to non-deterministic games with incomplete information. The most known Poker variant, Texas Hold'em Poker, combines simple rules with a huge amount of possible playing strategies. This paper is focused on developing algorithms for performing simple online opponent modeling in Texas Hold'em. The oppon...

In imperfect information problems, board game is a class of special problem that differs from card games like poker. Several characters make it a valuable test bed for opponent modeling, which is one of the most difficult problems in artificial intelligence decision systems. In card games, opponent modeling has proved its importance on improving agents’ strength. In this paper, a method of buil...

Opponent modeling is an essential approach for building competitive computer agents in imperfect information games. This paper presents a novel approach to develop opponent modeling techniques. The approach applies neural networks which are separately trained on different dataset to build Kmodel clustering opponent models. KullbackLeibler (KL) divergence is used to exploit a safety mode on oppo...

This work describes an automated negotiation agent called OMAC which was awarded the joint third place in the 2012 Automated Negotiating Agent Competition (ANAC 2012). OMAC, standing for “Opponent Modeling and Adaptive Concession”, combines efficient opponent modeling and adaptive concession making. Opponent modeling is achieved through standard wavelet decomposition and cubic smoothing spline;...

Stratego is a game of imperfect information, where observations of the opponent’s behaviour are crucial for determining the best move. This paper describes how one can model the opponent in the game of Stratego, using a Bayesian approach. By observing the moves of the opponent, a probability distribution can be derived to help determine the identity of unknown pieces of the opponent. Experiment...

Poker is an interesting test-bed for artificial intelligence research. It is a game of imperfect knowledge, where multiple competing agents must deal with risk management, agent modeling, unreliable information and deception, much like decision-making applications in the real world. Agent modeling is one of the most difficult problems in decision-making applications and in poker it is essential...

In recent years much progress has been made on computer gameplay in games of complete information such as chess and go. Computers have surpassed the ability of top chess players and are well on their way to doing so at Go. Games of incomplete information, on the other hand, are far less studied. Despite significant financial incentives, computerized poker players still perform at a level well b...

Opponent modeling is a technique in computer game-playing which attempts to create a model of an opponent’s strategy. This model can then be used to predict the opponent’s future actions. This paper attempts to apply opponent modeling to the commercial card game Machiavelli, a game containing imperfect information. Neural networks are used to build the models. These neural networks are trained ...

Computers have already eclipsed the level of human play in competitive Scrabble, but there remains room for improvement. In particular, there is much to be gained by incorporating information about the opponent’s tiles into the decision-making process. In this work, we quantify the value of knowing what letters the opponent has. We use observations from previous plays to predict what tiles our ...

Abstract Multiagent reinforcement learning (MARL) has been used extensively in the game environment. One of main challenges MARL is that environment agent system dynamic, and other agents are also updating their strategies. Therefore, modeling opponents’ process adopting specific strategies to shape an effective way obtain better training results. Previous studies such as DRON, LOLA SOS approxi...