An Overview on Opponent Modeling in RoboCup Soccer Simulation 2D

Pourmehr, Shokoofeh; Dadkhah, Chitra

doi:10.1007/978-3-642-32060-6_34

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…Bakkes et al (2012) and Karpinskyj et al (2014) survey methods for player modelling in commercial video games, where the purpose of modelling is to improve the playing strength of game AI as well as player satisfaction. Pourmehr and Dadkhah (2012) provide an overview of modelling methods used in 2D simulated robot soccer, in which two teams of agents compete in a soccer match. Rubin and Watson (2011) survey research in Poker playing agents and dedicate a section to opponent modelling methods.…”

Section: Related Surveysmentioning

confidence: 99%

Autonomous agents modelling other agents: A comprehensive survey and open problems

Albrecht

Stone

2018

Artificial Intelligence

300

208

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Much research in artificial intelligence is concerned with the development of autonomous agents that can interact effectively with other agents. An important aspect of such agents is the ability to reason about the behaviours of other agents, by constructing models which make predictions about various properties of interest (such as actions, goals, beliefs) of the modelled agents. A variety of modelling approaches now exist which vary widely in their methodology and underlying assumptions, catering to the needs of the different sub-communities within which they were developed and reflecting the different practical uses for which they are intended. The purpose of the present article is to provide a comprehensive survey of the salient modelling methods which can be found in the literature. The article concludes with a discussion of open problems which may form the basis for fruitful future research.

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Section: Related Surveysmentioning

confidence: 99%

Autonomous agents modelling other agents: A comprehensive survey and open problems

Albrecht

Stone

2018

Artificial Intelligence

300

208

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“…Pourmehr and C. Dadkhah [9] have written a comprehensive literature review of various techniques applied in the domain of 2D. L. Agapito et al [10] present OMBO, an opponent modeling approach based on observations, which purely focuses on the skills of goal keeper and attacker.…”

Section: Related Workmentioning

confidence: 99%

Opponent Classification in Robot Soccer

Larik

Haider

2015

Current Approaches in Applied Artificial Intelligence

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Abstract. The paper presents an approach to perform post-hoc analysis of RoboCup Soccer Simulation 3D teams via log files of their matches and to learn a model to classify them not only as being strong, medium or weak but also through their game playing styles such as frequent kickers, frequent dribblers, heavy/lean attackers, etc. The learned model can then be used to further cluster teams to predict game style of similar opponents. We have applied the presented approach to 22 teams from RoboCup 2011 in a fully automated fashion and the results show the validity of our approach. The synergetic effect of these individual actions would emerge as a team strategy and would reveal the quality of the team. This paper presents a novel framework that analyzes key agent behaviors in RoboCup Soccer Simulation 3D[6] using a set of pre-defined rules and classifies a team based upon these extracted team quality features. Team quality features were identified by a group of experts and extraction of these features is automated via a parser application. These features include team players positioning, frequency of players in attacking region, ball possession, average velocity of team players, frequency of kicks executed by teams, shots attempted towards goal etc. Once a team is classified upon its strength parameters then grouping of teams that play similar is done via clustering that would aid in counter strategy formulation against similar opponents. The major

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“…Unsurprisingly, these properties have attracted the attention of many researchers studying opponent modeling, and the RoboCup simulation league in particular has supported a variety of opponent modeling research (Pourmehr & Dadkhah, 2011). From here on we will illustrate concepts using RoboCup-based examples, and because of this grounding we will refer to the set of variables relevant to decision making and prediction, both fully and partially observable, as the 'game state', Y .…”

Section: Introductionmentioning

confidence: 99%

A Survey of Opponent Modeling in Adversarial Domains

Nashed

Zilberstein

2022

jair

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Opponent modeling is the ability to use prior knowledge and observations in order to predict the behavior of an opponent. This survey presents a comprehensive overview of existing opponent modeling techniques for adversarial domains, many of which must address stochastic, continuous, or concurrent actions, and sparse, partially observable payoff structures. We discuss all the components of opponent modeling systems, including feature extraction, learning algorithms, and strategy abstractions. These discussions lead us to propose a new form of analysis for describing and predicting the evolution of game states over time. We then introduce a new framework that facilitates method comparison, analyze a representative selection of techniques using the proposed framework, and highlight common trends among recently proposed methods. Finally, we list several open problems and discuss future research directions inspired by AI research on opponent modeling and related research in other disciplines.

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An Overview on Opponent Modeling in RoboCup Soccer Simulation 2D

Cited by 8 publications

References 24 publications

Autonomous agents modelling other agents: A comprehensive survey and open problems

Autonomous agents modelling other agents: A comprehensive survey and open problems

Opponent Classification in Robot Soccer

A Survey of Opponent Modeling in Adversarial Domains

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