Opponent's Behavior Research Articles

Toward Adaptive Virtual Reality Systems: Understanding Emotional and Physiological Responses in VR Game

Virtual reality (VR) affective computing is expanding to include systems that can identify and respond to human emotions. This study explores emotional responses in VR using the game Pong to monitor these reactions under different gameplay conditions. It investigates how game pace and opponent behavior affect emotions, assessed through physiological metrics like electrocardiogram, galvanic skin response, electromyogram, and player behaviors. Results from three game variations—slow-paced, fast-paced, and lag-induced—showed distinct emotional and physiological responses. Significant differences were found in arousal and valence levels, with varying heart rate and skin conductance among the game conditions. These findings suggest that VR game design can significantly influence emotional states, providing insights that could help develop more engaging and responsive VR systems. Thus, this study demonstrates the potential of VR to evoke and measure specific emotional responses, contributing to the fields of human-computer interaction, and affective computing.

Modeling Rationality: Toward Better Performance Against Unknown Agents in Sequential Games.

Opponent modeling is necessary for autonomous agents to capture the intents of others during strategic interactions. Most previous works assume that they can access enough interaction history to build the model. However, it may not be realistic. To solve this problem, we present a novel rationality-consistent opponent modeling (ROM) method for games with imperfect information. In our approach, a game-theoretical concept of consistence about rationality is proposed to take advantage of the characteristic of imperfect information sequential games that rational behavior at disjoint information sets is correlated through anticipated opponent's behavior. With the correlation between different information sets, agents could infer the opponents' strategies at information sets correlated to observed behavior. To exploit the correlation, ROM attempts to conduct reasoning from the opponent's perspective and rationalize its past behavior. In this way, ROM acquires the ability to better adapt to different opponents and achieves a more accurate opponent model with insufficient observation history, which is verified by experiments in different settings. A heuristic adaptation approach is also applied in ROM, which updates the opponent model in an online manner and significantly reduces the computation cost. We evaluate ROM in both a grid world game and a poker game. Compared with other opponent modeling methods, ROM shows better performance and has more accurate predictions in both games against different types of opponents with limited action interactions. Experimental results also show that ROM's time cost is significantly reduced through heuristic adaptation.

Evolution of spite versus evolution of altruism through a disbandment mechanism

Altruism and spite are costly to the actor, making their evolution unlikely without specific mechanisms. Nonetheless, both altruistic and spiteful behaviors are present in individuals, which suggests the existence of an underlying mechanism that drives their evolution. If altruistic individuals are more likely to be recipients of altruism than non-altruistic individuals, then altruism can be favored by natural selection. Similarly, if spiteful individuals are less likely to be recipients of spite than non-spiteful individuals, then spite can be favored by natural selection. Spite is altruism's evil twin, ugly sister of altruism, or a shady relative of altruism. In some mechanisms, such as repeated interactions, if altruism is favored by natural selection, then spite is also favored by natural selection. However, there has been limited investigation into whether both behaviors evolve to the same extent. In this study, we focus on the mechanism by which individuals choose to keep or stop the interaction according to the opponent's behavior. Using the evolutionary game theory, we investigate the evolution of altruism and spite under this mechanism. Our model revealed that the evolution of spite is less likely than the evolution of altruism.

Algorithms for detecting the structure of labyrinths and studying the efficiency of their application

The article considers labyrinths in the context of the task of their creation and completion for use in the gaming IT industry. Artificial intelligence is currently used to simulate human behavior in computer games, which, depending on the algorithms, more or less imitates the intelligent behavior of a computer opponent. The peculiarity of a computer player is that at the software level, computer opponents have access to all objects of the game situation: the level map, the game board, etc. For a better human imitation of the behavior of the agent, the data coming to it is suppressed in such a way that it coincides with the data that a person in the same situation can receive using the senses. Therefore, it is important to apply appropriate algorithms for finding a way out of it, as well as recognizing their structure. The study of path-finding algorithms and the structure of the labyrinth for the detection of "human-like" approaches, and the evaluation of the effectiveness of such algorithms, allows us to identify better algorithms on the basis of which it is possible to develop games with a more interesting plot. Thus, this work is devoted to the research of algorithms for finding the structure of a labyrinth. Achieving the final goal involves the practical development of software in the Java language for visualizing their structure and obtaining specific values of the performance parameters of the selected algorithms. The work provides basic information about labyrinths, their main varieties and structures, and provides a mathematical model of an ideal labyrinth. Questions related to the selection of parameters for evaluating the effectiveness of their work, the direct analysis of algorithms, the design of a test application in Java and the analysis of the obtained results are investigated and resolved. During the performance evaluation, several structure research algorithms were analyzed in terms of speed of operation, and recommendations were given to choose the most optimal of the ones presented.

Особливості врегулювання крос-культурних конфліктів в процесі здійснення зовнішньоекономічної діяльності

Any violations or problems in the establishment of cross-cultural communication in the organiza-tion can become the causes of potential conflicts. Therefore, for their prevention or faster solving, it is important to understand the possible causes of their occurrence. Problems related to the establishment of cross-cultural communication can be: the use of cultural filters when forming an opinion about foreign counterparties; insufficient level of tolerance towards part-ners or clients; incorrect use of verbal and non-verbal means of communication; the establishment of new connections may depend on the degree of implementation of the principles of social responsibility, as this indicates the reliability of the business entity. Conventionally, all problems of cross-cultural interaction can be divided into two groups: conducting negotiations with international business partners and commu-nication within the organization. Usually, cross-cultural conflict during business negotiations arises for two main reasons. First, when dealing with cultural differences, partners quite often rely on stereotypes. Stereotypes are often de-rogatory and can lead to distorted expectations about the opponent's behavior and potentially misinterpre-tations. The second most common cause of intercultural misunderstandings is the tendency to interpret the behavior, values, and beliefs of others through the lens of one's own culture. To overcome this tendency, it is important to learn as much as possible about the culture of the foreign partner's side. The classification of business cultures by Richard Lewis can help to better navigate the character-istics of different cultures, comparing one with another and identifying common and distinctive features. This method is useful for planning cooperation with new counterparties in order to minimize the risk of potential conflicts due to unfamiliarity with the foreign partner. Before starting each negotiation, especially if they take place with new counterparties, it is neces-sary to process an array of information about cross-cultural differences. It is thanks to this measure that the probability of cross-cultural conflict can be minimized. And when a dispute arises, the company's team still needs to review its training, because in most cases, knowing cultural characteristics and being able to use them correctly is the key to solving the problem. For the effective resolution of conflicts, it is worth using the recommended strategy for resolving cross-cultural conflicts in the company in the process of foreign economic activity.

The analogical foundations of cooperation

We offer an approach to cooperation in repeated games of private monitoring in which players construct models of their opponents' behavior by observing the frequencies of play in a record of past plays of the game in which actions but not signals are recorded. Players construct models of their opponent's behavior by grouping the histories in the record into a relatively small number of analogy classes for which they estimate probabilities of cooperation. The incomplete record and the limited number of analogy classes lead to misspecified models that provide the incentives to cooperate. We provide conditions for the existence of equilibria supporting cooperation and equilibria supporting high payoffs for some nontrivial analogy partitions.

Multi-agent actor-critic with time dynamical opponent model

In multi-agent reinforcement learning, multiple agents learn simultaneously while interacting with a common environment and each other. Since the agents adapt their policies during learning, not only the behavior of a single agent becomes non-stationary, but also the environment as perceived by the agent. This renders it particularly challenging to perform policy improvement. In this paper, we propose to exploit the fact that the agents seek to improve their expected cumulative reward and introduce a novel Time Dynamical Opponent Model (TDOM) to encode the knowledge that the opponent policies tend to improve over time. We motivate TDOM theoretically by deriving a lower bound of the log objective of an individual agent and further propose Multi-Agent Actor-Critic with Time Dynamical Opponent Model (TDOM-AC). We evaluate the proposed TDOM-AC on a differential game and the Multi-agent Particle Environment. We show empirically that TDOM achieves superior opponent behavior prediction during test time. The proposed TDOM-AC methodology outperforms state-of-the-art Actor-Critic methods on the performed tasks in cooperative and especially in mixed cooperative-competitive environments. TDOM-AC results in a more stable training and a faster convergence. Our code is available at https://github.com/Yuantian013/TDOM-AC.

Behavior Reasoning for Opponent Agents in Multi-Agent Learning Systems

One important component of developing autonomous agents lies in the accurate prediction of their opponents’ behaviors when the agents interact with others in an uncertain environment. Most recent study focuses on first constructing predictive types (or models) of the opponents, considering their various properties of interest, and subsequently using these models to predict their behaviors accordingly. However, as the possible type space can be rather large, it is time-consuming, and sometimes even infeasible, to predict the actual behaviors of opponents with all candidate types. Thus, in this paper a tractable opponent behavior reasoning approach is proposed that facilitates ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$a$</tex-math></inline-formula> ) extraction of a small yet representative summary of all candidates using sub-modular-type maximization, and accordingly, ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$b$</tex-math></inline-formula> ) identification of the most appropriate type for real-time behavior prediction based on multi-armed bandits. In addition, we propose a knowledge-transfer scheme through demonstration learning to synchronize subject agents’ knowledge about their opponents’ behaviors. This further reduces the burden of reasoning with all models of their opponents from the perspective of individual subject agents. We integrate the new behavior prediction and reasoning method into a state-of-the-art evolutionary multi-agent framework, namely a memetic multi-agent system (MeMAS), and demonstrate empirical performance in two problem domains.

The angle of attack: rapping technique predicts skill in hermit crab contests

Skill, the ability to perform a challenging behaviour well, has been shown to be an important determinant of success in a variety of contexts, including human sports, animal courtship and, most recently, animal contests. Because skilful movement requires precise motor control, skill is assumed to be underpinned by traits that determine these abilities. However, while these traits determine an individual's potential to perform effective movements (known as technique), this potential may not translate into skilful fighting due to interference from the opponent. Here, we investigated the relationship between technique and skill using the European hermit crab, Pagurus bernhardus . By examining the spatial distribution of shell raps, we found that, on average, technique exhibited during a ‘training’ fight predicted the level of skill displayed in a real contest. However, our results also demonstrated substantial among-individual variation in the direction of change across the two fights, with some individuals exhibiting better technique than skill and others showing the opposite pattern. Finally, we found that winners, but not losers, progressively adapted their targeting of strikes when faced with a fully functional opponent. Our results indicate that skill is a combination of innate technique and the ability to adapt to an opponent's behaviour. • Skill has recently been shown to be a key determinant of fighting success. • Skill is assumed to rely on innate capacities for motor control (technique). • But opponent interference may impede an individual's ability to perform skilfully. • Here we show that innate technique is indicative of the ability to fight skilfully. • We also find that skilful fighting relies on the ability to adapt to your opponent.

MPD: Introduction of a single player prisoner's dilemma application for medical experiments

BackgroundPrisoner's dilemma is one of the most popular concepts among scientific literature. In medical literature the majority of prisoner's dilemma experiments with human participants implement computerized means. Despite this, there is no shared validated tool for prisoner's dilemma tasks. MethodsThe application is developed in Javascript programming language and makes use of the pixijs library for WebGL rendering. To create a custom trial, a set of variables have to be set. These refer to the linguistics, user controls, available choices, computer strategy, interaction flow (simultaneous or sequential), opponent's choice prediction requirement, noise induction, human opponent behavior simulation, the way resulted data will be handled and more. Results are in JSON format and include time interval data. ResultsWe have developed an application which, given the various parameters experimenter can modify, is able to simulate a large number of single player prisoner's dilemma versions. It is open source with no installation requirement, executable by any modern internet browser remotely or locally with the ability to post data results either locally or remotely. Experimenter only has to modify certain starting values in order to create his design of choice. Two examples are included, with initial settings and results, to demonstrate the use and validity of the application. ConclusionsOur aim is to assist future researchers in their methodological designs. In this scope, our application, has the minimum requirements, can be served either locally or remotely, has a wide range of modifiable parameters and takes care of the resulted data. In the long term, a shared and validated tool would contribute to increasing methodologies’ credibility and mitigating cross-validation discrepancies.

The Relationship between Support for Autonomy of Youth Taekwondo Instructors, Burnout and Sports Moral Behavior of Athletes

The purpose of this study was to investigate the The relationship between support for autonomy of youth Taekwondo instructors, burnout and sports moral behavior of athletes. To this end, Taekwondo athletes registered by the Korea Taekwondo Association selected as a population, a survey was conducted on 280 youth Taekwondo athletes living in Gyeonggi, Daejeon, Jeonnam, and Gwangju for about six months from September 2020 to March 2021. Among them, 277 data were used for the final analysis after excluding the data of 3 athletes whose response information was omitted. As a result of this study, first, autonomy support had a negative effect (-) on burnout. Second, autonomy support had a positive effect (+) on team prosocial behavior, opponent prosocial behavior, and opponent antisocial behavior. Third, burnout had a negative effect (-) on opponent prosocial behavior, while had a positive effect (+) on team antisocial behavior and opponent antisocial behavior. Based on the above results, this study identified the relationship of perceived autonomy support, burnout, and Sport moral behavior.

Modeling and prediction of human negotiation behavior in human-computer negotiation

• This study proposes a segmented linear regression Strategy (SLR) model, which can predict the opponent's future offer patterns. • Segmented linear regression method can model complex concession behavior dynamically. • A theoretical framework is built upon the hope-based and game-based negotiation theories. • The model is validated empirically through computer-to-computer and human-to-computer negotiation experiments based on a prototype system. With increasing e-commerce activities, the human–computer negotiation mechanism for online transactions emerges. This study proposes a segmented linear regression strategy (SLR) model, which can predict the opponent's future offer patterns and dynamically adjust the model parameters according to the opponent's behavior. We use the hope-based (behavior-oriented) and game-based (technical-oriented) negotiation theories as the theory ground to explicate the logic of the agent's negotiation strategy. Upon which we propose a theoretical model of bilateral negotiation as the framework for designing the SLR model. We then propose the testable hypotheses to evaluate the utility of the proposed model against the benchmark results. We build a prototype system and empirically test the hypotheses using computer-to-computer (CtC) and human-to-computer (HtC) negotiation experiments. The empirical results show that the proposed system outperforms the benchmark system in both CtC and HtC negotiations regarding deal price and utility. The proposed model provides an automated negotiation solution for online platforms that can improve the efficiency and effectiveness of their negotiation capability.

The Prophet’s Da’wah to Jews &amp; their Opposition (A research-based study on its causes and reasons)

Before the migration of the Holy Prophet ﷺ, Madinah was inhabited by Jews. They possessed some of the most fertile lands in Madinah and carried on several crafts, especially as goldsmiths. The Jews acted as moneylenders to the Arabs. They had many strongholds in and around Madinah. Thus, the Jews had a firm position in Madinah. The Holy Prophet ﷺ, irrespective of Jewish attitude, tried his level best to maintain a friendly relationship with the Jews. They were granted political, social and religious freedom. However, the Jews became increasingly hostile to the Muslims despite these rights and concessions as they wanted to dominate Arabia politically and religiously. Thus, they expressed their bitterness and hostility in many ways, which worsened their relations with the Muslims. This research is aimed at highlighting the history, socio-political status and conspiracies of Jews. The researcher presented detailed accounts on the opponent and rigid behaviour of Jews along with their character. The distinct element of the article is the references and a discourse on the Jewish tribes who opposed Islam openly. The researcher also worked out the causes why a call to Islam for Jews was indispensable. Likewise, he also mentioned the reasons and the causes of opposition from the then Jews. Jews, out of jealousy did not accept Islam. However, they kept demanding multiple things as previous. The article also mentions them. This piece of research is highly recommended for the young generation who are unaware of the history and attitude of the Jews that continued since the times of the Prophets.

Increasing Replayability with Deliberative and Reactive Planning

Opponent behavior in today's computer games is often the result of a static set of Artificial Intelligence (AI) behaviors or a fixed AI script. While this ensures that the behavior is reasonably intelligent, it also results in very predictable behavior. This can have an impact on the replayability of entertainment-based games and the educational value of training-based games. This paper proposes a move away from static, scripted AI by using a combination of deliberative and reactive planning. The deliberative planning (or Strategic AI) system creates a novel strategy for the AI opponent before each gaming session. The reactive planning (or Tactical AI) system executes this strategy in real-time and adapts to the player and the environment. These two systems, in conjunction with a future automated director module, form the Adaptive Opponent Architecture. This paper describes the architecture and the details of the deliberative and reactive planning components.

Cooperate or aggress? An opponent's tendency to cooperate modulates the neural dynamics of interpersonal cooperation

Humans are social animals and need to cooperate to survive. However, individuals are not cooperative in every social interaction, and their cooperation may depend on social context. The present study used a social dilemma game to investigate whether an opponent's tendency to be cooperative over time influenced a player's behavior and neural response to outcomes in the game. University students (“players”) thought they were playing against other students (“opponents”) in the Chicken Game but were actually playing against a programmed computer. Participants were randomly assigned to play with an opponent who tended to be competitive (cooperative 20% of the time) or who tended to be cooperative (cooperative 80% of the time). The results showed that early in the game, participants in both groups adopted a “tit-for-tat” strategy. However, as the game progressed and the opponent's behavioral tendency became more noticeable, players in the competitive-opponent group became generally more cooperative to limit their losses. ERPs analyses indicated that players had a higher P300 and larger theta power in response to the opponent's aggression but not to the opponent's cooperation when their opponent showed a tendency to be cooperative vs. competitive. The results suggest that people adjust their cooperative behavior based on their opponent's behavior in social interaction, and aggression captures more attention than cooperation in this process.

COACH’S EFFECTIVENESS MEDIATE LONGITUDINAL EFFECTS OF TRANSFORMATIONAL LEADERSHIP BEHAVIOUR ON ATHLETE OUTCOMES

The present study aimed to investigate whether athlete perceptions of coaching effectiveness mediate the longitudinal relationships between perceptions of transformational leadership (TL) and athlete outcomes. 174 team-sport athletes (Rugby = 35, Basketball = 46, Football = 93) participated by completing psychometric measures of the study variables at two time points (retention rate = 93.5%). Data analyses revealed athletes’ perceptions of their coach’s character-building effectiveness at Time 1 did not mediate a negative predictive effect of perceptions of coach appropriate role model behaviour at Time 1 on athlete antisocial opponent behaviour at Time 2. However, such perceptions did mediate a negative effect of perceptions of coach appropriate role model behaviour at Time 1 on athlete antisocial teammate behaviour at Time 2. Further, athletes’ perceptions of their coach’s motivation effectiveness at Time 1 mediated a positive effect of coach individual consideration behaviour at Time 1 on trust at Time 2. The findings of this study suggest athletes’ perceptions of their coach’s transformational leadership behaviour may at times influence athlete outcomes through changes in athletes’ perceptions of their coach’s effectiveness.

Opponent portrait for multiagent reinforcement learning in competitive environment

Existing investigations of opponent modeling and intention inferencing cannot make clear descriptions and practical explanations of the opponent's behaviors and intentions, which may inevitably limit the applicability of them. In this work, we propose a novel approach for opponent's policy explanation and intention inference based on the behavioral portrait of opponent. Specifically, we use the multiagent deep deterministic policy gradients (MADDPG) algorithm to train the agent and opponent in the competitive environment, and collect the behavioral data of opponent based on agent's observations. Then we perform pattern segmentation and extract the opponent's behavior events via Toeplitz inverse covariance-based clustering (TICC) algorithm; hence the opponent's behavior data can be encoded into a knowledge graph, named opponent's behavior knowledge graph (OKG). Based on this, we built a question-answer system (QA system) to query and match opponent historical information in OKG, so that the agent can obtain additional experience and gradually infer the intention of opponent with the episodes of iteration. We evaluate the proposed method on the competitive scenario in multiagent particle environment (MPE). Simulation results show that the agents are able to learn better policies with opponent portrait in competitive settings.

Auto-encoder neural network based prediction of Texas poker opponent's behavior

Imperfect information game is the difficulty in the game field. In the study of imperfect information games, an accurate and effective opponent modeling method is essential. Texas Hold 'em is a typical game with imperfect information. The main form of opponent modeling is to predict the opponent's behavior. This article takes Texas Hold 'em as the specific research object and proposes an opponent's behavior prediction method based on an auto-encoding neural network. We use the International Computer Poker Tournament historical data as experimental data to model Texas Hold 'em poker players' behavior and train an auto-encoding neural network model that can be applied to different stages of the game. In the article, the auto-encoding network model's structure and parameters are experimentally studied, and the quantized conjugate gradient convergence algorithm (SCG) and softmax classifier are used. Experimental test results show that this method can accurately predict the opponent's behavior information in a short time, which will provide precious help for the decision-making of the Texas Hold 'em agent.

Nine typical injury patterns in German professional male football (soccer): a systematic visual video analysis of 345 match injuries

AimWe aimed to systematically analyse the videos of acute injuries in professional men’s football and describe typical injury patterns.MethodsInjuries were registered with the German statutory accident insurance for professional athletes...

From Riemannian Trichromacy to Quantum Color Opponency via Hyperbolicity

We propose a mathematical description of human color perception that relies on a hyperbolic structure of the space \({\mathcal {P}}\) of perceived colors. We show that hyperbolicity allows us to reconcile both trichromaticity, from a Riemannian point of view, and color opponency, from a quantum viewpoint. In particular, we will underline how the opponent behavior can be represented by a rebit, a real analog of a qubit, whose state space is endowed with the Hilbert metric.