Opponent Modeling is one of the most attractive and practical arenas in Multi Agent System (MAS) for predicting and identifying the future behaviors of opponent. This paper introduces a novel approach using rule based expert system towards opponent modeling in RoboCup Soccer Coach Simulation. In this scene, an autonomous coach agent is able to identify the patterns of the opponent by analyzing ...

Games have always been a natural topic for Artificial Intelligence researchers to study and poker has proven to be a game that is both interesting and challenging. Part of the challenge of poker comes from the fact that 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-maki...

An important aim in bilateral negotiations is to achieve a win-win solution for both parties; therefore, a critical aspect of a negotiating agent’s success is its ability to take the opponent’s preferences into account. Every year, new negotiation agents are introduced with better learning techniques to model the opponent. Our main goal in this work is to evaluate and compare the performance of...

Real-time strategy games present an environment in which game AI is expected to behave realistically. One feature of realistic behaviour in game AI is the ability to recognise the strategy of the opponent player. This is known as opponent modeling. In this paper, we propose an approach of opponent modeling based on hierarchically structured models. The top-level of the hierarchy can classify th...

We develop an algorithm for opponent modeling in large extensive-form games of imperfect information. It works by observing the opponent’s action frequencies and building an opponent model by combining information from a precomputed equilibrium strategy with the observations. It then computes and plays a best response to this opponent model; the opponent model and best response are both updated...

Negotiation is a process in which parties interact to settle a mutual concern to improve their status quo. Traditionally, negotiation is a necessary, but time-consuming and expensive activity. Therefore, in the last two decades, there has been a growing interest in the automation of negotiation. One of the key challenges for a successful negotiation is that usually only limited information is a...

The game of poker has many properties that make it an interesting topic for arti cial intelligence (AI). It is a game of imperfect information, which relates to one of the most fundamental problems in computer science: how to handle knowledge that may be erroneous or incomplete. Poker is also one of the few games to be studied where deriving an accurate understanding of each opponent's style is...

Opponent modeling is the ability to use prior knowledge and observations in order predict behavior of an opponent. This survey presents a comprehensive overview existing opponent techniques for adversarial domains, many which must address stochastic, continuous, or concurrent actions, sparse, partially observable payoff structures. We discuss all components systems, including feature extraction...

One approach to designing an intelligent agent capable of winning competitive games such as Texas hold’em poker is to use opponent modeling to learn about an opponent’s behavior, then exploit that knowledge to maximize long term winnings. However, opponent modeling can suffer from several problems, including slow convergence due to a lack of a priori knowledge, noisy or dynamic opponent behavio...