Automated negotiation agents capable of negotiating efficiently with people must deal with the fact that people are diverse in their behavior and each individual might negotiate in a different manner. Thus, automated agents must rely on a good opponent modeling component to model their counterpart and adapt their behavior to their partner. In this paper we present the KBAgent. The KBAgent is an...

The development of an autonomous agent that plays Poker at human level is a very difficult task since the agent has to deal with problems like the existence of hidden information, deception and risk management. To solve these problems, Poker agents use opponent modeling to predict the opponents next move and thereby determine its next action. In this paper are described several methods to measu...

Opponent modeling consists in modeling the strategy or preferences of an agent thanks to the data it provides. In the context of automated negotiation and with machine learning, it can result in an advantage so overwhelming that it may restrain some casual agents to be part of the bargaining. We qualify as “curious” an agent driven by the desire of negotiating in order to collect information an...

This paper focuses on an investigation of case-based opponent player modeling in the domain of simulated robotic soccer. While in previous and related work it has frequently been claimed that the prediction of low-level actions of an opponent agent in this application domain is infeasible, we show that – at least in certain settings – an online prediction of the opponent’s actions can be made w...

In this thesis, we present a computer agent for the game of no-limit Texas Hold'em Poker for two players. Poker is a partially observable, stochastic, multi-agent, sequential game. This combination of characteristics makes it a very challenging game to master for both human and computer players. We explore this problem from an opponent modeling perspective, using data mining to build a database...

Real Time strategy games offer an environment where game AI is known to conduct actuality. One feature of realistic behavior in game AI is the ability to recognize the strategy of the opponent player. This is known as opponent modeling. In this paper, a classification Rough-Neuro hybrid model of the RTS opponent player behavior process is proposed. As a mean to achieve better game performance, ...

When an opponent with a stationary and stochastic policy is encountered in a twoplayer competitive game, model-free Reinforcement Learning (RL) techniques such as Q-learning and Sarsa(λ) can be used to learn near-optimal counter strategies given enough time. When an agent has learned such counter strategies against multiple diverse opponents, it is not trivial to decide which one to use when a ...

This paper describes a method for cooperative play among 3 robots in order to score a goal in the RoboCup Small Size League. In RoboCup 2005 Osaka, our team introduced a new attacking play, where one robot kicks a ball and the other receives and immediately shoots the ball on goal. However, due to the relatively slow kicking speed of the robot, top opponent teams could prevent successful passin...

In automated bilateral multi issue negotiations, two intelligent automated agents negotiate on behalf of their owners over many issues in order to reach an agreement. Modeling the opponent can excessively boost the performance of the agents and increase the quality of the negotiation outcome. State of the art models accomplish this by considering some assumptions about the opponent which restri...