Opponent Provocation and Behavior Classification: A Machine Learning Approach

Fathzadeh, Ramin; Mokhtari, Vahid; Kangavari, Mohammad Reza

doi:10.1007/978-3-540-68847-1_58

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…In another line of work, the champion of the RoboCup Soccer Coach Simulation Competition at 2004 and 2006 used a rule based expert system for modeling the opponent team [3]. Fathzadeh et al defined the model of the opponent as a collection of multiple identified patterns.…”

Section: Game Playmentioning

confidence: 99%

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An Overview on Opponent Modeling in RoboCup Soccer Simulation 2D

Pourmehr

Dadkhah

2012

Lecture Notes in Computer Science

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This paper reviews the proposed opponent modeling algorithms within the soccer simulation domain. RoboCup soccer simulation 2D is a rich multi agent environment where opponent modeling plays a crucial role. In multi agent systems with adversarial and cooperative agents, team agents should be adapted to the current environment and opponent in order to propose appropriate and effective counteractions. Predicting the opponent's future behaviors during competition allows for more informed decisions. We divide opponent modeling into two categories of individual agent behaviors and team behaviors. Individual behaviors concern modeling the low-level behaviors of individual opponent agents, however in team behaviors, the high-level strategy of the entire team like formation, offensive and defensive system, is recognized. Several methods have been proposed to create different models of opponents to improve the performance of teams in an essential aspect. In this paper, we review the approaches to the problem of opponent modeling published from 2000 to 2010.

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Section: Game Playmentioning

confidence: 99%

“…An opponent is an agent that has private strategies and has goals that are conflicting with your own [2]. Opponent modeling predicts and identifies the future behaviors of opponent and proposes an appropriate counteraction [3].…”

Section: Introductionmentioning

confidence: 99%

An Overview on Opponent Modeling in RoboCup Soccer Simulation 2D

Pourmehr

Dadkhah

2012

Lecture Notes in Computer Science

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“…In the next phase, they placed a dummy player in the field for the purpose of recording opponent actions and finally they predicted actions of opponent based on the training dataset. For the coaching competition, many simulated coaches have been presented; Fathzadeh et al [8], Agapito et al [9], Peter Stone at al [10][11] being some of them. The focus of these coaches is to learn normal base patterns of team plays and then predict the strategy with which the team is playing.…”

Section: Related Workmentioning

confidence: 99%

Rule-Based Behavior Prediction of Opponent Agents Using Robocup 3D Soccer Simulation League Logfiles

Larik

Haider

2012

IFIP Advances in Information and Communication Technology

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Abstract. Opponent modeling in games deals with analyzing opponents' behavior and devising a winning strategy. In this paper we present an approach to model low level behavior of individual agents using Robocup Soccer Simulation 3D environment. In 2D League, the primitive actions of agents such as Kick, Turn and Dash are known and high level behaviors are derived using these low level behaviors. In 3D League, however, the problem is complex as actions are to be inferred by observing the game. Our approach, thus, serves as a middle tier in which we learn agent behavior by means of manual data tagging by an expert and then use the rules generated by the PART algorithm to predict opponent behavior. A parser has been written for extracting data from 3D logfiles, thus making our approach generalized. Experimental results on around 6000 records of 3D league matches show very promising results.

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The Winning Advantage: Using Opponent Models in Robot Soccer

Iglesias

Fernández

Villena

et al. 2009

Intelligent Data Engineering and Automated Learning - IDEAL 2009

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Abstract. Opponent modeling is a skill in multi-agent systems (MAS) which attempts to create a model of the behavior of the opponent. This model can be used to predict the future actions of the opponent and generate appropriate strategies to play against it. Several researches present different methods to create an opponent model in the RoboCup environment. However, how these models can impact the performance of teams is an essential aspect. This paper introduces a novel approach to use efficiently opponent models in order to improve our own team behavior. The basis of this approach is the research done by CAOS Coach Team for modeling and recognizing behaviors evaluated in the RoboCup Coach Competition 2006.For using these models, it is necessary a special agent (coach) which can model the observed opponent team (based on the previous research) and communicate a counter-strategy to the coached players (using the approach proposed in this paper). The evaluation of this approach is a hard problem, but we have conducted several experiments that can help us to know if we are going in a promising direction.

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Opponent Provocation and Behavior Classification: A Machine Learning Approach

Cited by 6 publications

References 2 publications

An Overview on Opponent Modeling in RoboCup Soccer Simulation 2D

An Overview on Opponent Modeling in RoboCup Soccer Simulation 2D

Rule-Based Behavior Prediction of Opponent Agents Using Robocup 3D Soccer Simulation League Logfiles

The Winning Advantage: Using Opponent Models in Robot Soccer

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