Player Agents Research Articles

1P1-F27 ロボカップサッカーにおける協調的パスプレイを考慮したパサー動作のモデル化

This paper describes a model of passer's kick behavior considering cooperative pass play for a RoboCup middle size soccer player agent. In the RoboCup middle size league, realization of advanced cooperative pass play with improvement of players' autonomy is expected. However, such advanced pass play couldn't be seen yet in a present RoboCup game. Generally, it is necessary to control a landing position of kicked ball by a passer for an effective pass in soccer game. Therefore, a kick behavior of passer was modeled to realize advanced cooperative pass play in this paper. A passer's behavior was modeled based on mechanical features of a kick device installed on a player robot. Experimental results showed that a ball can be controlled for pass to an arbitrary position by using a proposed model.

JoMP: a mobile music player agent for joggers based on user interest and pace

Jogging is a common and cheap form of exercise which improves health and can reduce the risk of illness. Many people wear mobile devices to listen to music during this activity. However, the playlist in mobile devices are predefined and independent of the jogger's pace. Whenever the user wants to change the music that is playing, they have to stop exercising and push buttons on the device. In this paper, we introduce a novel service that provides the jogger with a smart music player agent to reduce the effort required to control the mobile device. This system involves music filtration and pace prediction technologies. An artificial neural networks that can filter out undesired music is used as the kernel of the music filter. Pace prediction is based on hidden Markov models (HMM), and similar pattern searching techniques are used to select music that has a suitable tempo. A series of experiments were carried out to demonstrate the performance of this system. The results show that this unique service is attractive to joggers.

The use of celebrity athletes as endorsers: views of the New Zealand general public

Matching celebrity athletes with potential endorsement opportunities is often difficult. Yet there are easy-touse survey-based methods available. Based on a survey of the general public in New Zealand, this study uses both Ohanian's source-credibility scale and a constant-sum scale to help brand managers, player agents and advertising practitioners select good celebrity athlete-product fit. Four New Zealand athletes (two males and two females) and several products were included in the test. Results show that the female celebrity athletes outperformed their male counterparts as potential endorsers. Use of Ohanian's multi-attribute scale yields a level of richness and insight, prompting us to advocate the use of both scales in the pursuit of endorser-product congruences.

Preventing bots from playing online games

As multiplayer online gaming gains in economic and social importance, an increasingly large number of players is beginning to rely on bots (automated player agents) to gain unfair advantages in games. In this article we study the problem of restricting participation in online games to human players so they can enjoy the game without interference from the bots. We propose two broad approaches to prevent bots from playing online games. The first consists of seamlessly integrating software-based tests (known as reverse Turing tests or CAPTCHA tests) into online games to tell humans and computers apart. Our second contribution is to propose hardware instantiations of CAPTCHA tests. Our techniques are applicable in a wide variety of online games, from poker to "shoot'em ups." They are cost-effective, immune to cheating, and preserve the human players' enjoyment of each game. We conclude with a discussion of how approaches to deter the use of bots may complement our techniques to detect bots.

Analysis of Symbol Generation and Integration in a Unified Model Based on a Neural Network

The computational (numerical information) and symbolic (knowledge-based) processing used in intelligent processing has advantages and disadvantages. A simple model integrating symbols into a neural network was proposed as a first step toward fusing computational and symbolic processing. To verify the effectiveness of this model, we first analyze the trained neural network and generate symbols manually. Then we discuss generation methods that are able to discover effective symbols during training of the neural network. We evaluated these through simulations of reinforcement learning in simple football games. Results indicate that the integration of symbols into the neural network improved the performance of player agents.

A Two-Phase Mechanism for Agent's Action Selection in Soccer Simulation

Soccer simulation is an effort to motivate researchers and practitioners to do artificial and robotic intelligence research; and at the same time put into practice and test the results. Many researchers and practitioners throughout the world are continuously working to polish their ideas and improve their implemented systems. At the same time, new groups are forming and they bring bright new thoughts to the field. The research includes designing and executing robotic soccer simulation algorithms. In our research, a soccer simulation player is considered to be an intelligent agent that is capable of receiving information from the environment, analyze it and to choose the best action from a set of possible ones, for its next move. We concentrate on developing a two-phase method for the soccer player agent to choose its best next move. The method is then implemented into our software system called Nexus simulation team of Ferdowsi University. This system is based on TsinghuAeolus[1] team that was the champion of the world RoboCup soccer simulation contest in 2001 and 2002. Keywords—RoboCup, Soccer simulation, multi-agent environment, intelligent soccer agent, ball controller agent.

Industry Factors and the Changing Dynamics of the Player–Agent Relationship in Professional Ice Hockey

Player agents are now long-established and influential stakeholders in the business of professional ice hockey in North America. With the help of agents, average National Hockey League (NHL) player salaries have grown from US$24,000 in 1972 to an average of US$1.49 million annually in the 2000/2001 season. Since the arrival of agents in the mid-1960s, the professional hockey industry has experienced significant and turbulent growth. This paper identifies and reviews the industry changes that have taken place in hockey in recent decades and the manner in which these factors have affected the relationship between player and agent. Principal–agent theory is used to facilitate an analysis of the contracts and relationships between professional hockey players and sports agents and to understand the effects of changing environmental pressures upon these relationships.

Evaluating Monitoring Mechanisms as a Solution to Opportunism by Professional Hockey Agents

Focusing on player agents in professional ice hockey, this paper utilizes the theoretical construct of agency theory as a means of evaluating attempts by several stakeholder groups to find solutions to opportunistic agent behavior. As proposed by agency theorists, this would include the creation and implementation of monitoring mechanisms by industry stakeholders in order to regulate agent activities. Stakeholder groups involved include state and federal governments, the agents themselves, the National Collegiate Athletic Association (NCAA), and Players' Associations, which, at various times, all have adopted forms of certification programs in attempts to monitor player agents. Documentation on these programs and interviews with industry stakeholders are employed to develop criteria by which such programs can be assessed in terms of their ability to reduce traditional agency problems. In doing so, it is argued that the agency model can be used to provide additional insight into problems associated with these programs and to improve program effectiveness in monitoring hockey agent behavior.

LogMonitor: Analyzing Good Plays to Train Player Agents

Teaching (training, programming, and learning) player agents is the challenging research in the RoboCup simulation games. LOGMONITOR is a tool for analyzing games from game records. LOGMONITORs can be used not only to improve the collaboration among agent players but also to evaluate them.

Pro-sports medicine from the inside.

Pro-sports medicine from the inside.

Agents and Agency: a Sports Agent's View

Editor's Note—Attorney and Player Agent Leigh Steinberg has long been recognized as a leader in defining the ethos of the sports representation field. He represents a number of NFL players, as well as other professional athletes. This commentary on the Uniform Code proposal for agents and Staudohar's analysis of the McNeil decision was received in the editorial office January 11, 1993, five days after an agreement between the NFL owners and players had been announced.