An Empirical Study of Artificial Participants: A Factor Affecting Game Length in Chess

An Empirical Study of Artificial Participants: A Factor Affecting Game Length in Chess

William Bart (University of Minnesota, USA), Jacob Ritter (University of Minnesota, USA) and Nathan Ritter (University of Minnesota, USA)
Copyright: © 2021 |Pages: 11
DOI: 10.4018/JTA.20210101.oa2
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Abstract

This study is an investigation of artificial participants operating in their appropriate environment. The artificial participants in this study are artificial chess players and their appropriate environment is a chess game. This study is an empirical investigation testing the hypothesis that the length of a chess game is inversely related to the difference in the chess skill levels of the artificial chess players. Five series of chess games of 18 games in each series were instituted between five pairings of web-based chess engines. The chess engine, chess.com Level 10, was a player in each series and won all 18 games in each series. The opposing players came from the Play Magnus app at five different levels of chess skill. This study provided an investigation of 90 chess games involving artificial chess players. The hypothesis for the study was confirmed. Game length was significantly inversely related to the disparity in chess proficiency between artificial chess players. This is one of the first scientific studies of artificial participants.
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Introduction

With new electronic technologies such as those dealing with mobile communications and Robotics comes the emergence of artificial participants that are software-directed entities that can engage in tasks that are typically done by humans and that can serve as participants in empirical research. In certain forms of psychological research, one could thus have human participants as well as artificial participants. With the proliferation of artificial participants, one can inquire how they perform in their appropriate environments and what factors affect that performance.

There are many forms of artificial participants such as robotic waiters programmed to wait on customers in restaurants, social service robots programmed to provide information to customers in shopping malls, security robots programmed to detect and prevent crime in shopping malls, and chess engines programmed to play chess at a high level. All of those entities can serve as participants in empirical research.

One form of artificial participant that has appeal in the restaurant business is the robotic waiter that is intended to serve food to customers in a restaurant. Zhang (2018) reported that robotic waiters have become popular in a host of restaurants in China. The robotic waiter takes the form of a transparent container the size of a microwave oven that is placed on a cart that rolls around the restaurant. When the robotic waiter arrives at a table, it raises its glass lid to reveal the ordered food and then tells the customer to enjoy the meal.

Mishra, Goyal, and Sharma, (2018) reported that robotic waiters are already being used in countries such as China and Japan and that the restaurant businesses in other countries such as India are developing interests in them. However, further improvements in that technology are warranted. For example, there is the issue of navigation in room service or serving at a table. Robotic waiters at present typically follow a track in a restaurant. This approach is termed the line follower technique. One positive aspect to the line follower technique is that robotic waiters following this technique can move about smoothly in a restaurant in a balanced manner. One problem is that the robotic waiter following the line follower technique typically needs to be stopped by a customer who then takes the food from the robotic waiter and places the food onto a table being used by the customer. Robotic waiters are not advanced enough to serve food on a table as well as a human.

In a subsequent publication on the same topic, Mishra, Goyal, Sharma, and Gupta (2021) indicated that the task of a robotic waiter is to identify and stop at the correct table of a restaurant customer with the food order. But robotic waiters do not always behave the way intended. They at times stop at some distance from the desired table or even stop at the wrong table which indicates a detection problem in the robotic waiter technology.

A possible solution to the detection problem is the use of RFID technology (Ahuja and Potti, 2010; Finkenzeller, 2010), which refers to Radio Frequency Identification Technology. RFID is a technology used to scan products as in grocery stores. It is superior to reading barcodes in that it permits the scanning of objects at a distance. Robotic waiters in restaurants equipped with RFID tags and RFID tag readers allow accuracy in the detection by a robotic waiter of the correct table in the serving of food. But the problem of how to place the food on the customer table correctly remains.

Although some of the behaviors of robotic waiters have improved, robotic behaviors still do not behave completely as intended. More research is required to address the problem of correct food placement. Robotic waiters are examples of artificial participants that behave in specific ways within the environment of restaurant. They have been programmed to do their job fairly well, but further improvements are warranted.

One shortcoming to the research on robotic waiters is the lack of use of the scientific method in the research. Rarely is a hypothesis cited and then tested empirically with the collection of relevant data and the subsequent analysis of the data with the use of appropriate statistical methods. Research on robotic methods would likely benefit from the use of rigorous scientific analysis.

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