Behavior Recognition and Opponent Modeling for Adaptive Table Soccer Playing

Weigel, Thilo; Rechert, Klaus; Nebel, Bernhard

doi:10.1007/11551263_27

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…The latter approach is sometimes called 'opponent modelling' and used to learn an optimal strategy against the modelled type of agent [1,8,38,39]. Although plan recognition was first introduced in the field of logical inference by [25], the current state of the art is mainly based on probabilistic methods like Bayesian Networks [6,7,9] and Markov Models [6,4,21,10,46,26]. For example in [39], simulated robot soccer players adapt their positions strategically in adaptive response to the opponent's behaviour throughout the game.…”

Section: Related Researchmentioning

confidence: 99%

A Formalization of the Coach Problem

Schropp

Meyer

Ramamoorthy

2015

RoboCup 2014: Robot World Cup XVIII

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Abstract. Coordination is an important aspect of multi-agent teamwork. In the context of robot soccer in the RoboCup Standard Platform League, our focus is on the coach as an external observer of the team, aiming to provide his teammates with effective tactical advice during matches. The coach problem can be approached from different angles: in order to adapt the behaviour of his teammates, he should at first be able to perform plan recognition on their observable actions. Furthermore, in providing them with appropriate advice, he should still adhere to the norms and regulations of the match to prevent penalties for his team. Also, when teammates' profiles and attributes are unknown or the system is only partially observable, coordination should be more 'ad hoc' to ensure robustness of the Multi-Agent System (MAS). In this work, we present a formalization of the problem of designing a coach in robot soccer, employing a temporal deontic logical framework. The framework is based on agent organizations [14], in which social coordination and norms play an important part.

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Section: Related Researchmentioning

confidence: 99%

A Formalization of the Coach Problem

Schropp

Meyer

Ramamoorthy

2015

RoboCup 2014: Robot World Cup XVIII

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show abstract

“…The latter approach is sometimes called 'opponent modelling' and used to learn an optimal strategy against the modelled type of agent [5,31]. Although plan recognition was first introduced in the field of logical inference by [20], the current state of the art is mainly based on probabilistic methods like Bayesian Networks [3,4,6] and Markov Models [3,38]. For example in [31], simulated robot soccer players adapt their positions strategically in adaptive response to the opponent's behaviour throughout the game.…”

Section: Related Researchmentioning

confidence: 99%

RoboCup 2014: Robot World Cup XVIII

Bianchi¹,

Akın²,

Ramamoorthy³

et al. 2015

Lecture Notes in Computer Science

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Abstract. In this paper, we present the details of our team WrightEagle@Home's approaches. Our KeJia robot won the RoboCup@Home competition 2014 and accomplished two tests which have never been fully solved before. Our work covers research issues ranging from hardware, perception and high-level cognitive functions. All these techniques and the whole robot system have been exhaustively tested in the competition and have shown good robustness.

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“…Games such as soccer [1], table soccer [13], and table tennis have all been the subject of robotic gaming research, the common objective being not only to play, but to compete successfully against humans.…”

Section: Introductionmentioning

confidence: 99%

Determining Shot Accuracy of a Robotic Pool System

Lam

Long

Roth

et al.

The 3rd Canadian Conference on Computer and Robot Vision (CRV'06)

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A process is described to determine the shot accuracy of an automatic robotic pool playing system. The system comprises a ceiling-mounted gantry robot, a special purpose cue end-effector, a ceiling-mounted camera, and a standard bar pool table.Two methods are compared for extracting the homography between the camera and the table plane. A challenge was the relatively large area of the table surface, which required a similarly large chessboard pattern to determine the point homography. In contrast, the Dual Conic method required only a set of orthogonal lines as a calibration target, which was more convenient to manipulate, and could be calculated from the integration of multiple images with multiple target locations. The Dual Conic method was shown experimentally to recover the homography with a similar, and sometimes greater accuracy than the Chessboard method.An experimental procedure was devised to measure the accuracy of an automatic shot using a sequence of images acquired from the overhead camera. For a set of 10 shots, the average absolute angular error in placing a shot was determined to be 0.74 0 , with a standard deviation of 0.96 0 .

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Behavior Recognition and Opponent Modeling for Adaptive Table Soccer Playing

Cited by 8 publications

References 10 publications

A Formalization of the Coach Problem

A Formalization of the Coach Problem

RoboCup 2014: Robot World Cup XVIII

Determining Shot Accuracy of a Robotic Pool System

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