Formalizing Opponent Modeling with the Rock, Paper, Scissors Game

In simple dyadic games such as rock, paper, scissors (RPS), people exhibit peculiar sequential dependencies across repeated interactions with a stable opponent. These regularities seem to arise from a mutually adversarial process of trying to outwit their opponent. What underlies this process, and what are its limits? Here, we offer a novel framework for formally describing and quantifying human adversarial reasoning in the rock, paper, scissors game. We first show that this framework enables a precise characterization of the complexity of patterned behaviors that people exhibit themselves, and appear to exploit in others. This combination allows for a quantitative understanding of human opponent modeling abilities. We apply these tools to an experiment in which people played 300 rounds of RPS in stable dyads. We find that although people exhibit very complex move dependencies, they cannot exploit these dependencies in their opponents, indicating a fundamental limitation in people’s capacity for adversarial reasoning. Taken together, the results presented here show how the rock, paper, scissors game allows for precise formalization of human adaptive reasoning abilities.

A Computer-Based Method for the Investigation of Human Behavior in the Iterative Chicken Game

The present study develops an artificial agent that plays the iterative chicken game based on a computational model that describes human behavior in competitive social interactions in terms of fairness. The computational model we adopted in this study, named as the self-concept fairness model, decides the agent’s action according to the evaluation of fairness of both opponent and self. We implemented the artificial agent in a computer program with a set of parameters adjustable by researchers. These parameters allow researchers to determine the extent to which the agent behaves aggressively or cooperatively. To demonstrate the use of the proposed method for the investigation of human behavior, we performed an experiment in which human participants played the iterative chicken game against the artificial agent. Participants were divided into two groups, each being informed to play with either a person or the computer. The behavioral analysis results showed that the proposed method can induce changes in the behavioral pattern of human players by changing the agent’s behavioral pattern. Also, we found that participants tended to be more sensitive to fairness when they played with a human opponent than with a computer opponent. These results support that the artificial agent developed in this study will be useful to investigate human behavior in competitive social interactions.

Design and Construction of Zana Robot for Modeling Human Player in Rock-paper-scissors Game using Multilayer Perceptron, Radial basis Functions and Markov Algorithms

In this paper, the implementation of artificial neural networks (multilayer perceptron [MLP] and radial base functions [RBF]) and the upgraded Markov chain model have been studied and performed to identify the human behavior patterns during rock, paper, and scissors game. The main motivation of this research is the design and construction of an intelligent robot with the ability to defeat a human opponent. MATLAB software has been used to implement intelligent algorithms. After implementing the algorithms, their effectiveness in detecting human behavior pattern has been investigated. To ensure the ideal performance of the implemented model, each player played with the desired algorithms in three different stages. The results showed that the percentage of winning computer with MLP and RBF neural networks and upgraded Markov model, on average in men and women is 59%, 76.66%, and 75%, respectively. Obtained results clearly indicate a very good performance of the RBF neural network and the upgraded Markov model in the mental modeling of the human opponent in the game of rock, paper, and scissors. In the end, the designed game has been employed in both hardware and software which include the Zana intelligent robot and a digital version with a graphical user interface design on the stand. To the best knowledge of the authors, the precision of novel presented method for determining human behavior patterns was the highest precision among all of the previous studies.

Preferential Engagement and What Can We Learn from Online Chess?

An online game of chess against a human opponent appears to be indistinguishable from a game against a machine: both happen on the screen. Yet, people prefer to play chess against other people despite the fact that machines surpass people in skill. When the philosophers of 1970’s and 1980’s argued that computers will never surpass us in chess, perhaps their intuitions were rather saying “Computers will never be favored as opponents”? In this paper we analyse through the introduced concepts of psychological affordances and psychological interplay, what are the mechanisms that make a human-human (HH) interaction more meaningful than a human-computer (HC) interaction. We claim that an HH chess game consists of two intertwined, but independent simultaneous games—only one of which is retained in the HC game. To help with the analysis we introduce the thought experiment of a Preferential Engagement Test (PET) which is inspired by, but non-equivalent to, the Standard Turing Test. We also explore how the PET can illuminate, and be illuminated by, various philosophies of mind reading: Theory Theory, Simulation Theory and Mind Minding. We propose that our analysis along with the concept of PET could illuminate in a new way the conditions and challenges a machine (or its designers) must face before it can replace humans in a given occupation.

Review of Kalah Game Research and the Proposition of a Novel Heuristic–Deterministic Algorithm Compared to Tree-Search Solutions and Human Decision-Making

The Kalah game represents the most popular version of probably the oldest board game ever—the Mancala game. From this viewpoint, the art of playing Kalah can contribute to cultural heritage. This paper primarily focuses on a review of Kalah history and on a survey of research made so far for solving and analyzing the Kalah game (and some other related Mancala games). This review concludes that even if strong in-depth tree-search solutions for some types of the game were already published, it is still reasonable to develop less time-consumptive and computationally-demanding playing algorithms and their strategies Therefore, the paper also presents an original heuristic algorithm based on particular deterministic strategies arising from the analysis of the game rules. Standard and modified mini–max tree-search algorithms are introduced as well. A simple C++ application with Qt framework is developed to perform the algorithm verification and comparative experiments. Two sets of benchmark tests are made; namely, a tournament where a mid–experienced amateur human player competes with the three algorithms is introduced first. Then, a round-robin tournament of all the algorithms is presented. It can be deduced that the proposed heuristic algorithm has comparable success to the human player and to low-depth tree-search solutions. Moreover, multiple-case experiments proved that the opening move has a decisive impact on winning or losing. Namely, if the computer plays first, the human opponent cannot beat it. Contrariwise, if it starts to play second, using the heuristic algorithm, it nearly always loses.

Design and Construction of a Cost-Effective Didactic Robotic Arm for Playing Chess, Using an Artificial Vision System

This paper presents the design and construction of a robotic arm that plays chess against a human opponent, based on an artificial vision system. The mechanical design was an adaptation of the robotic arm proposed by the rapid prototyping laboratory FabLab RUC (Fabrication Laboratory of the University of Roskilde). Using the software Solidworks, a gripper with 4 joints was designed. An artificial vision system was developed for detecting the corners of the squares on a chessboard and performing image segmentation. Then, an image recognition model was trained using convolutional neural networks to detect the movements of pieces on the board. An image-based visual servoing system was designed using the Kanade–Lucas–Tomasi method, in order to locate the manipulator. Additionally, an Arduino development board was programmed to control and receive information from the robotic arm using Gcode commands. Results show that with the Stockfish chess game engine, the system is able to make game decisions and manipulate the pieces on the board. In this way, it was possible to implement a didactic robotic arm as a relevant application in data processing and decision-making for programmable automatons.

Dissociable roles of left and right temporoparietal junction in strategic competitive interaction

Although many studies have shown that the temporoparietal junction (TPJ) is involved in inferring others’ beliefs, neural correlates of ‘second-order’ inferences (inferring another’s inference about one’s own belief) are still elusive. Here we report a functional magnetic resonance imaging experiment to examine the involvement of TPJ for second-order inferences. Participants played an economic game with three types of opponents: a human opponent outside the scanner, an artificial agent that followed a fixed probabilistic strategy according to a game-theoretic solution (FIX) and an artificial agent that adjusted its choices through a machine-learning algorithm (LRN). Participants’ choice behaviors against the human opponent and LRN were similar but remarkably different from those against FIX. The activation of the left TPJ (LTPJ) was correlated with choice behavior against the human opponent and LRN but not against FIX. The overall activity pattern of the LTPJ for the human opponent was also similar to that for LRN but not for FIX. In contrast, the right TPJ (RTPJ) showed higher activation for the human opponent than FIX and LRN. These results suggest that, while the RTPJ is associated with the perception of human agency, the LTPJ is involved in second-order inferences in strategic decision making.

Playing Well With Others: The Role of Opponent and Intergroup Anxiety in the Reduction of Prejudice Through Collaborative Video Game Play

Recent work on the social effects of video gaming has moved away from the view they are detrimental, and has instead demonstrated how they may be a force for good. One example is how collaborative intergroup play can reduce prejudice between groups. However, this literature is at a nascent stage, and many of the intricacies of such a mechanism are unknown. Previous work has predominantly used attitude scales and ignored other measures. Factors such as the role of the opponent in games and what may be the mechanism behind any effects has likewise received little attention. In this laboratory study, participants played collaborative games with an outgroup member, or alone. Their opponent was also reported to be computer-controlled, or controlled by another person. Following play, intergroup anxiety was reported, and participants wrote a short passage of prose regarding the outgroup as well as rating on attitude scales. Analysis demonstrated that playing with outgroup members was indeed an effective method of increase the positivity towards outgroup members, reflected in both scale and prose measures. Anxiety was also found to be a significant mediator; however it was less clear whether a human opponent moderated any effects. Further ideas of how these findings could be developed are then discussed.

Reconstruction of Human Skills by Using PCA and Transferring them to a Robot

This paper discusses human skills enabling rapid adaptation to a changing environment, e.g., when a human table tennis player hits an incoming ball, and describes how to transfer these skills to a robot. Human skills are classified into motor and cognitive. Motor skills are functions involving precise limb movement with the intent to perform a specific act, i.e., hitting a ball. Cognitive skills are functions involving meaningful responses to external stimuli. We extract these skills from observing human movement using principal component analysis and generalize these skills as a schema for a generalized motor program. We also describe table tennis matches between a human opponent and a robot to which these skills have been transferred.