C ue sports have been captivating humankind for thousands of years, with written references dating to the first century CE. They evolved as a branch of modern croquet and golf, as a kind of indoor table version, and much of the modern nomenclature can be traced back to that common root. Cue sports today are vastly popular, and comprise variations such as pool, billiards, carom, snooker, and many other local flavors. In a 2005 U.S. survey, pool ranked as the eighth most popular participation sport in that country, with more than 35 million people playing that year. Leagues and tournaments exist in nearly every country worldwide, with strong appeal to both experienced and casual players alike.A number of robotic cue-players have been developed over the years. The first such system was the Snooker Machine from University of Bristol, United Kingdom, in the late 1980s (Chang 1994). This system comprised an articulated manipulator inverted over a 1/4-sized snooker table. A single monochrome camera was used to analyze the ball positions, and custom control and strategy software was developed to plan and execute shots. The system was reported to perform moderately well, and could pot simple shots. Since then, there have been a number of other attempts at automating pool, including Alian et al. (2004) and Lin, Yang, and Yang (2004).Developing a complete robotic system that can be competitive against an accomplished human player is a significant challenge. The most recent, and likely most complete system to date, is Deep Green from Queen's University, Canada (Greenspan et al. 2008), shown in figure 1. This system uses an industrial gantry robot ceiling-mounted over a full-sized pool table. High-resolution FireWire cameras are mounted on both the ceiling to identify and localize the balls, and on the robotic wrist to correct for accumulated error and fine-tune the cue position prior to a shot. This system has been integrated with the physics simulator used in the computational pool tournaments and the strategy software developed to plan shots. Complex
Dr. Mohammadi-Aragh investigates the use of digital systems to measure and support engineering education, specifically through learning analytics and the pedagogical uses of digital systems. She also investigates fundamental questions critical to improving undergraduate engineering degree pathways.. She earned her Ph.D. in Engineering Education from Virginia Tech. In 2013, Dr. Mohammadi-Aragh was honored as a promising new engineering education researcher when she was selected as an ASEE Educational Research and Methods Division Apprentice Faculty.
Collusion is the practice of two or more parties deliberately cooperating to the detriment of others. While such behavior may be desirable in certain circumstances, in many it is considered dishonest and unfair. If agents otherwise hold strictly to the established rules, though, collusion can be challenging to police. In this paper, we introduce an automatic method for collusion detection in sequential games. We achieve this through a novel object, called a collusion table, that captures the effects of collusive behavior, i.e., advantage to the colluding parties, without assuming any particular pattern of behavior. We show the effectiveness of this method in the domain of poker, a popular game where collusion is prohibited.
The performance of agents in many domains with continuous action spaces depends not only on their ability to select good actions to execute, but also on their ability to execute planned actions precisely. This ability, which has been called an agent’s execution skill, is an important characteristic of an agent which can have a significant impact on their success. In this paper, we address the problem of estimating the execution skill of an agent given observations of that agent acting in a domain. Each observation includes the executed action and a description of the state in which the action was executed and the reward received, but notably excludes the action that the agent intended to execute. We previously introduced this problem and demonstrated that estimating an agent’s execution skill is possible under certain conditions. Our previous method focused entirely on the reward that the agent received from executed actions and assumed that the agent was able to select the optimal action for each state. This paper addresses the execution skill estimation problem from an entirely different perspective, focusing instead on the action that was executed. We present a Bayesian framework for reasoning about action observations and show that it is able to outperform previous methods under the same conditions. We also show that the flexibility of this framework allows it to be applied in settings where the previous limiting assumptions are not met. The success of the proposed method is demonstrated experimentally in a toy domain as well as the domain of computational billiards.
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