Статті в журналах: "Opponent model"

1

Davies, Ian, Zheng Tian, and Jun Wang. "Learning to Model Opponent Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (April 3, 2020): 13771–72. http://dx.doi.org/10.1609/aaai.v34i10.7157.

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Multi-Agent Reinforcement Learning (MARL) considers settings in which a set of coexisting agents interact with one another and their environment. The adaptation and learning of other agents induces non-stationarity in the environment dynamics. This poses a great challenge for value function-based algorithms whose convergence usually relies on the assumption of a stationary environment. Policy search algorithms also struggle in multi-agent settings as the partial observability resulting from an opponent's actions not being known introduces high variance to policy training. Modelling an agent's opponent(s) is often pursued as a means of resolving the issues arising from the coexistence of learning opponents. An opponent model provides an agent with some ability to reason about other agents to aid its own decision making. Most prior works learn an opponent model by assuming the opponent is employing a stationary policy or switching between a set of stationary policies. Such an approach can reduce the variance of training signals for policy search algorithms. However, in the multi-agent setting, agents have an incentive to continually adapt and learn. This means that the assumptions concerning opponent stationarity are unrealistic. In this work, we develop a novel approach to modelling an opponent's learning dynamics which we term Learning to Model Opponent Learning (LeMOL). We show our structured opponent model is more accurate and stable than naive behaviour cloning baselines. We further show that opponent modelling can improve the performance of algorithmic agents in multi-agent settings.

2

Shen, Macheng, and Jonathan P. How. "Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 578–87. http://dx.doi.org/10.1609/icaps.v31i1.16006.

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This paper studies decision-making in two-player scenarios where the type (e.g. adversary, neutral, or teammate) of the other agent (opponent) is uncertain to the decision-making agent (protagonist), which is an abstraction of security-domain applications. In these settings, the reward for the protagonist agent depends on the type of the opponent, but this is private information known only to the opponent itself, and thus hidden from the protagonist. In contrast, as is often the case, the type of the protagonist agent is assumed to be known to the opponent, and this information-asymmetry significantly complicates the protagonist's decision-making. In particular, to determine the best actions to take, the protagonist agent must infer the opponent type from the observations and agent modeling. To address this problem, this paper presents an opponent-type deduction module based on Bayes' rule. This inference module takes as input the imagined opponent's decision-making rule (opponent model) as well as the observed opponent's history of actions and states, and outputs a belief over the opponent's hidden type. A multiagent reinforcement learning approach is used to develop this game-theoretic opponent model through self-play, which avoids the expensive data collection step that requires interaction with a real opponent. Besides, this multiagent approach also captures the strategy interaction and reasoning between agents. In addition, we apply ensemble training to avoid over-fitting to a single opponent model during the training. As a result, the learned protagonist policy is also effective against unseen opponents. Experimental results show that the proposed game-theoretic modeling, explicit opponent type inference and the ensemble training significantly improves the decision-making performance over baseline approaches, and generalizes well against adversaries that have not been seen during the training.

3

Li, Junkang, Bruno Zanuttini, and Véronique Ventos. "Opponent-Model Search in Games with Incomplete Information." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 9 (March 24, 2024): 9840–47. http://dx.doi.org/10.1609/aaai.v38i9.28844.

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Games with incomplete information are games that model situations where players do not have common knowledge about the game they play, e.g. card games such as poker or bridge. Opponent models can be of crucial importance for decision-making in such games. We propose algorithms for computing optimal and/or robust strategies in games with incomplete information, given various types of knowledge about opponent models. As an application, we describe a framework for reasoning about an opponent's reasoning in such games, where opponent models arise naturally.

4

Otto, Jacob, and William Spaniel. "Doubling Down: The Danger of Disclosing Secret Action." International Studies Quarterly 65, no. 2 (November 19, 2020): 500–511. http://dx.doi.org/10.1093/isq/sqaa081.

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Abstract When an actor catches a state taking an objectionable secret action, it faces a dilemma. Exposing the action could force unresolved states to terminate the behavior to save face. However, it could also provoke resolved states to double down on the activity now that others are aware of the infraction. We develop a model that captures this fundamental trade-off. Three main results emerge. First, the state and its opponent may engage in a form of collusion—opponents do not expose resolved states despite their distaste for the behavior. Second, when faced with uncertainty, the opponent may mistakenly expose a resolved type and induce escalation, leading the opponent to have ex post regret. Finally, as the strength of secret action increases, states may engage in it less often. This counterintuitive result is a consequence of the opponent's greater willingness to expose, which deters less resolved types.

5

Wang, Yu, Ke Fu, Hao Chen, Quan Liu, Jian Huang, and Zhongjie Zhang. "Efficiently Detecting Non-Stationary Opponents: A Bayesian Policy Reuse Approach under Partial Observability." Applied Sciences 12, no. 14 (July 8, 2022): 6953. http://dx.doi.org/10.3390/app12146953.

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In multi-agent domains, dealing with non-stationary opponents that change behaviors (policies) consistently over time is still a challenging problem, where an agent usually requires the ability to detect the opponent’s policy accurately and adopt the optimal response policy accordingly. Previous works commonly assume that the opponent’s observations and actions during online interactions are known, which can significantly limit their applications, especially in partially observable environments. This paper focuses on efficient policy detecting and reusing techniques against non-stationary opponents without their local information. We propose an algorithm called Bayesian policy reuse with LocAl oBservations (Bayes-Lab) by incorporating variational autoencoders (VAE) into the Bayesian policy reuse (BPR) framework. Following the centralized training with decentralized execution (CTDE) paradigm, we train VAE as an opponent model during the offline phase to extract the latent relationship between the agent’s local observations and the opponent’s local observations. During online execution, the trained opponent models are used to reconstruct the opponent’s local observations, which can be combined with episodic rewards to update the belief about the opponent’s policy. Finally, the agent reuses the best response policy based on the updated belief to improve online performance. We demonstrate that Bayes-Lab outperforms existing state-of-the-art methods in terms of detection accuracy, accumulative rewards, and episodic rewards in a predator–prey scenario. In this experimental environment, Bayes-Lab can achieve about 80% detection accuracy and the highest accumulative rewards, and its performance is less affected by the opponent policy switching interval. When the switching interval is less than 10, its detection accuracy is at least 10% higher than other algorithms.

6

Liu, Chanjuan, Jinmiao Cong, Tianhao Zhao, and Enqiang Zhu. "Improving Agent Decision Payoffs via a New Framework of Opponent Modeling." Mathematics 11, no. 14 (July 11, 2023): 3062. http://dx.doi.org/10.3390/math11143062.

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The payoff of an agent depends on both the environment and the actions of other agents. Thus, the ability to model and predict the strategies and behaviors of other agents in an interactive decision-making scenario is one of the core functionalities in intelligent systems. State-of-the-art methods for opponent modeling mainly use an explicit model of opponents’ actions, preferences, targets, etc., that the primary agent uses to make decisions. It is more important for an agent to increase its payoff than to accurately predict opponents’ behavior. Therefore, we propose a framework synchronizing the opponent modeling and decision making of the primary agent by incorporating opponent modeling into reinforcement learning. For interactive decisions, the payoff depends not only on the behavioral characteristics of the opponent but also the current state. However, confounding the two obscures the effects of state and action, which then cannot be accurately encoded. To this end, state evaluation is separated from action evaluation in our model. The experimental results from two game environments, a simulated soccer game and a real game called quiz bowl, show that the introduction of opponent modeling can effectively improve decision payoffs. In addition, the proposed framework for opponent modeling outperforms benchmark models.

7

Donkers, H. "Probabilistic opponent-model search." Information Sciences 135, no. 3-4 (July 2001): 123–49. http://dx.doi.org/10.1016/s0020-0255(01)00133-5.

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8

Redden, Ralph S., Greg A. Gagliardi, Chad C. Williams, Cameron D. Hassall, and Olave E. Krigolson. "Champ versus Chump: Viewing an Opponent’s Face Engages Attention but Not Reward Systems." Games 12, no. 3 (July 31, 2021): 62. http://dx.doi.org/10.3390/g12030062.

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When we play competitive games, the opponents that we face act as predictors of the outcome of the game. For instance, if you are an average chess player and you face a Grandmaster, you anticipate a loss. Framed in a reinforcement learning perspective, our opponents can be thought of as predictors of rewards and punishments. The present study investigates whether facing an opponent would be processed as a reward or punishment depending on the level of difficulty the opponent poses. Participants played Rock, Paper, Scissors against three computer opponents while electroencephalographic (EEG) data was recorded. In a key manipulation, one opponent (HARD) was programmed to win most often, another (EASY) was made to lose most often, and the third (AVERAGE) had equiprobable outcomes of wins, losses, and ties. Through practice, participants learned to anticipate the relative challenge of a game based on the opponent they were facing that round. An analysis of our EEG data revealed that winning outcomes elicited a reward positivity relative to losing outcomes. Interestingly, our analysis of the predictive cues (i.e., the opponents’ faces) demonstrated that attentional engagement (P3a) was contextually sensitive to anticipated game difficulty. As such, our results for the predictive cue are contrary to what one might expect for a reinforcement model associated with predicted reward, but rather demonstrate that the neural response to the predictive cue was encoding the level of engagement with the opponent as opposed to value relative to the anticipated outcome.

9

Donkers, H. "Admissibility in opponent-model search." Information Sciences 154, no. 3-4 (September 2003): 119–40. http://dx.doi.org/10.1016/s0020-0255(03)00046-x.

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10

Park, Hyunsoo, and Kyung-Joong Kim. "Active Player Modeling in the Iterated Prisoner’s Dilemma." Computational Intelligence and Neuroscience 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/7420984.

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The iterated prisoner’s dilemma (IPD) is well known within the domain of game theory. Although it is relatively simple, it can also elucidate important problems related to cooperation and trust. Generally, players can predict their opponents’ actions when they are able to build a precise model of their behavior based on their game playing experience. However, it is difficult to make such predictions based on a limited number of games. The creation of a precise model requires the use of not only an appropriate learning algorithm and framework but also a good dataset. Active learning approaches have recently been introduced to machine learning communities. The approach can usually produce informative datasets with relatively little effort. Therefore, we have proposed an active modeling technique to predict the behavior of IPD players. The proposed method can model the opponent player’s behavior while taking advantage of interactive game environments. This experiment used twelve representative types of players as opponents, and an observer used an active modeling algorithm to model these opponents. This observer actively collected data and modeled the opponent’s behavior online. Most of our data showed that the observer was able to build, through direct actions, a more accurate model of an opponent’s behavior than when the data were collected through random actions.

11

Iida, Hiroyuki, Jos W. H. M. Uiterwijk, H. J. van den Herik, and I. S. Herschberg. "Potential Applications of Opponent-Model Search1." ICGA Journal 16, no. 4 (December 1, 1993): 201–8. http://dx.doi.org/10.3233/icg-1993-16403.

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Iida, Hiroyuki, Jos W. H. M. Uiterwijk, H. J. van den Herik, and I. S. Herschberg. "Potential Applications of Opponent-Model Search1." ICGA Journal 17, no. 1 (March 1, 1994): 10–14. http://dx.doi.org/10.3233/icg-1994-17103.

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13

Koob, George F., S. Barak Caine, Loren Parsons, Athina Markou, and Friedbert Weiss. "Opponent Process Model and Psychostimulant Addiction." Pharmacology Biochemistry and Behavior 57, no. 3 (July 1997): 513–21. http://dx.doi.org/10.1016/s0091-3057(96)00438-8.

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14

Luo, Junren, Wanpeng Zhang, Wei Gao, Zhiyong Liao, Xiang Ji, and Xueqiang Gu. "Opponent-Aware Planning with Admissible Privacy Preserving for UGV Security Patrol under Contested Environment." Electronics 9, no. 1 (December 18, 2019): 5. http://dx.doi.org/10.3390/electronics9010005.

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Unmanned ground vehicles (UGVs) have been widely used in security patrol. The existence of two potential opponents, the malicious teammate (cooperative) and the hostile observer (adversarial), highlights the importance of privacy-preserving planning under contested environments. In a cooperative setting, the disclosure of private information can be restricted to the malicious teammates. In adversarial setting, obfuscation can be added to control the observability of the adversarial observer. In this paper, we attempt to generate opponent-aware privacy-preserving plans, mainly focusing on two questions: what is opponent-aware privacy-preserving planning, and, how can we generate opponent-aware privacy-preserving plans? We first define the opponent-aware privacy-preserving planning problem, where the generated plans preserve admissible privacy. Then, we demonstrate how to generate opponent-aware privacy-preserving plans. The search-based planning algorithms were restricted to public information shared among the cooperators. The observation of the adversarial observer could be purposefully controlled by exploiting decoy goals and diverse paths. Finally, we model the security patrol problem, where the UGV restricts information sharing and attempts to obfuscate the goal. The simulation experiments with privacy leakage analysis and an indoor robot demonstration show the applicability of our proposed approaches.

15

Kovach, Nicholas S., Alan S. Gibson, and Gary B. Lamont. "Hypergame Theory: A Model for Conflict, Misperception, and Deception." Game Theory 2015 (August 19, 2015): 1–20. http://dx.doi.org/10.1155/2015/570639.

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When dealing with conflicts, game theory and decision theory can be used to model the interactions of the decision-makers. To date, game theory and decision theory have received considerable modeling focus, while hypergame theory has not. A metagame, known as a hypergame, occurs when one player does not know or fully understand all the strategies of a game. Hypergame theory extends the advantages of game theory by allowing a player to outmaneuver an opponent and obtaining a more preferred outcome with a higher utility. The ability to outmaneuver an opponent occurs in the hypergame because the different views (perception or deception) of opponents are captured in the model, through the incorporation of information unknown to other players (misperception or intentional deception). The hypergame model more accurately provides solutions for complex theoretic modeling of conflicts than those modeled by game theory and excels where perception or information differences exist between players. This paper explores the current research in hypergame theory and presents a broad overview of the historical literature on hypergame theory.

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Borghetti, Brett J. "The Environment Value of an Opponent Model." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 40, no. 3 (June 2010): 623–33. http://dx.doi.org/10.1109/tsmcb.2009.2033703.

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17

Donkers, H. H. L. M., H. J. van den Herik, and J. W. H. M. Uiterwijk. "Selecting evaluation functions in Opponent-Model search." Theoretical Computer Science 349, no. 2 (December 2005): 245–67. http://dx.doi.org/10.1016/j.tcs.2005.09.049.

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Dewan, Torun, and Rafael Hortala-Vallve. "Electoral Competition, Control and Learning." British Journal of Political Science 49, no. 3 (May 24, 2017): 923–39. http://dx.doi.org/10.1017/s0007123416000764.

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This article explores an agency model in which voters learn about both an incumbent and an opponent. They observe the incumbent’s policy record and update their beliefs about his opponent via a campaign. Although the former is relatively more informative, it can be costly for the voter to learn about the incumbent from her policy record. This is because policy reforms, which allow a voter to learn an incumbent’s ability, are risky and can leave the voter worse off. Then the voter may prefer the incumbent to take safer actions. The efficient level of reform – the one preferred by the voter – balances the value of learning with the expected policy costs/benefits. In a world where the opponent’s campaign is uninformative, reform can be too low due to the incumbent’s fear of failure. Or it can be too high: the incumbent may gamble on success. This article shows that the presence of an opponent who can reveal information via a campaign exacerbates these inefficiencies. An incumbent who anticipates the effect of an opponent’s campaign on voter beliefs is more likely to make inefficient policy choices. Further, such campaigns can lead to an overall welfare loss when they reveal little about the opponent’s ability and yet have an impact on the incumbent’s policy choice.

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Tian, Xin, Yubei Huang, Lu Cai, and Hai Fang. "E-Commerce Decision Model Based on Auto-Learning." Journal of Electronic Commerce in Organizations 15, no. 4 (October 2017): 57–71. http://dx.doi.org/10.4018/jeco.2017100105.

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The proposed model utilizes the information implied in the history of E-commerce negotiation to automatically mark the data to form the training samples, and apply the clues binary decision tree to automatically learn the samples to obtain the estimate of the opponent difference function. Then, an incremental decision-making problem is constituted through the combination of its own and the opponent's difference functions; and the dispersion algorithm is adopted to solve the optimization problem. The experimental results show that, the model still demonstrates relatively high efficiency and effectiveness under the condition of information confidentiality and no priori knowledge.

20

Lv, Yongliang, Yan Zheng, and Jianye Hao. "Opponent modeling with trajectory representation clustering." Intelligence & Robotics 2, no. 2 (2022): 168–79. http://dx.doi.org/10.20517/ir.2022.09.

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For a non-stationary opponent in a multi-agent environment, traditional methods model the opponent through its complex information to learn one or more optimal response policies. However, the response policy learned earlier is prone to catastrophic forgetting due to data imbalance in the online-updated replay buffer for non-stationary changes of opponent policies. This paper focuses on how to learn new response policies without forgetting old policies that have been learned when the opponent policy is constantly changing. We extract the representation of opponent policies and make explicit clustering distinctions through the contrastive learning autoencoder. With the idea of balancing the replay buffer, we maintain continuous learning of the trajectory data of various opponent policies that have appeared to avoid policy forgetting. Finally, we demonstrate the effectiveness of the method under a classical opponent modeling environment (soccer) and show the clustering effect of different opponent policies.

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Vial, Shayne, James L. Croft, Ryan T. Schroeder, Anthony J. Blazevich, and Jodie Cochrane Wilkie. "Does the presence of an opponent affect object projection accuracy in elite athletes? A study of the landing location of the short serve in elite badminton players." International Journal of Sports Science & Coaching 15, no. 3 (March 29, 2020): 412–17. http://dx.doi.org/10.1177/1747954120915670.

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The ability to accurately project (e.g. throw, kick, hit) an object at high speed is a uniquely human skill, and this ability has become a critical feature of many competitive sports. Nonetheless, in some sports, the target or end-point for a projected object is often not reached because an opponent intercepts or returns the object; thus, a player cannot use object landing location information to inform accuracy outcome. By comparing the landing location of serves performed without an opponent by elite badminton players to predicted landing points of serves delivered with an opponent, we aimed to determine whether object projection accuracy is affected by the presence of an opponent. Landing locations of serves to an opponent were predicted using a model developed through analysis of serves without an opponent present. The model predicted that 69% of serves to an opponent would have landed on or short (i.e. outside the permitted area) of the service line. Thus, serve trajectory in elite badminton players was considerably altered by the presence of an opponent, despite their aim to serve to a specific point on the court.

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Hein, Anthony, May Jiang, Vydhourie Thiyageswaran, and Michael Guerzhoy. "Random Forests for Opponent Hand Estimation in Gin Rummy." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (May 18, 2021): 15545–50. http://dx.doi.org/10.1609/aaai.v35i17.17830.

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We demonstrate an AI agent for the card game of Gin Rummy. The agent uses simple heuristics in conjunction with a model that predicts the probability of each card's being in the opponent's hand. To estimate the probabilities for cards' being in the opponent's hand, we generate a dataset of Gin Rummy games using self-play, and train a random forest on the game information states. We explore the random forest classifier we trained and study the correspondence between its outputs and intuitively correct outputs. Our agent wins 61% of games against a baseline heuristic agent that does not use opponent hand estimation.

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Gawne, Timothy J., and Thomas T. Norton. "An opponent dual-detector spectral drive model of emmetropization." Vision Research 173 (August 2020): 7–20. http://dx.doi.org/10.1016/j.visres.2020.03.011.

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Tian, Yuan, Klaus-Rudolf Kladny, Qin Wang, Zhiwu Huang, and Olga Fink. "Multi-agent actor-critic with time dynamical opponent model." Neurocomputing 517 (January 2023): 165–72. http://dx.doi.org/10.1016/j.neucom.2022.10.045.

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Joyce, D. W., B. B. Averbeck, C. D. Frith, and S. S. Shergill. "Examining belief and confidence in schizophrenia." Psychological Medicine 43, no. 11 (March 22, 2013): 2327–38. http://dx.doi.org/10.1017/s0033291713000263.

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BackgroundPeople with psychoses often report fixed, delusional beliefs that are sustained even in the presence of unequivocal contrary evidence. Such delusional beliefs are the result of integrating new and old evidence inappropriately in forming a cognitive model. We propose and test a cognitive model of belief formation using experimental data from an interactive ‘Rock Paper Scissors’ (RPS) game.MethodParticipants (33 controls and 27 people with schizophrenia) played a competitive, time-pressured interactive two-player game (RPS). Participants' behavior was modeled by a generative computational model using leaky integrator and temporal difference methods. This model describes how new and old evidence is integrated to form a playing strategy to beat the opponent and to provide a mechanism for reporting confidence in one's playing strategy to win against the opponent.ResultsPeople with schizophrenia fail to appropriately model their opponent's play despite consistent (rather than random) patterns that can be exploited in the simulated opponent's play. This is manifest as a failure to weigh existing evidence appropriately against new evidence. Furthermore, participants with schizophrenia show a ‘jumping to conclusions’ (JTC) bias, reporting successful discovery of a winning strategy with insufficient evidence.ConclusionsThe model presented suggests two tentative mechanisms in delusional belief formation: (i) one for modeling patterns in other's behavior, where people with schizophrenia fail to use old evidence appropriately, and (ii) a metacognitive mechanism for ‘confidence’ in such beliefs, where people with schizophrenia overweight recent reward history in deciding on the value of beliefs about the opponent.

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Angilletta, Michael J., Gregory Kubitz, and Robbie S. Wilson. "Self-deception in nonhuman animals: weak crayfish escalated aggression as if they were strong." Behavioral Ecology 30, no. 5 (July 13, 2019): 1469–76. http://dx.doi.org/10.1093/beheco/arz103.

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Abstract Humans routinely deceive themselves when communicating to others, but no one knows whether other animals do the same. We ask whether dishonest signaling between crayfish meets a condition required for self-deception: dishonest individuals and honest individuals escalate aggression according to their signals of strength rather than actual strength. Using game theory, we predicted how an animal’s knowledge of its strength should affect its decision to escalate aggression. At the evolutionary equilibrium, an animal that knows its strength should escalate aggression according to its strength, relative to the expected strength of its opponent. By contrast, an animal that knows only its size should escalate aggression according to its size, relative to the size of its opponent. We tested these predictions by staging encounters between male crayfish (Cherax dispar) of known sizes and strengths. Consistent with a model of self-deception, crayfish escalated aggression based on the sizes of their claws relative to those of their opponents, despite the fact that size poorly predicts strength. Males who were weak for their size escalated disputes less often, but their aggression far exceeded the level predicted by a model of self-awareness, suggesting these crayfish were largely ignorant of their deception. Animals that fail to recognize their own dishonest signals may win disputes with stronger opponents without engaging in costly combat. Our game-theoretical approach can be used to identify potential cases of self-deception in nonhuman animals, enabling comparative studies of this behavior.

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Patel, Saumil S., Bai-Chuan Jiang, and Haluk Ogmen. "Vergence Dynamics Predict Fixation Disparity." Neural Computation 13, no. 7 (July 1, 2001): 1495–525. http://dx.doi.org/10.1162/089976601750264983.

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The neural origin of the steady-state vergence eye movement error, called binocular fixation disparity, is not well understood. Further, there has been no study that quantitatively relates the dynamics of the vergence system to its steady-state behavior, a critical test for the understanding of any oculomotor system. We investigate whether fixation disparity can be related to the dynamics of opponent convergence and divergence neural pathways. Using binocular eye movement recordings, we first show that opponent vergence pathways exhibit asymmetric angle-dependent gains. We then present a neural model that combines physiological properties of disparity-tuned cells and vergence premotor cells with the asymmetric gain properties of the opponent pathways. Quantitative comparison of the model predictions with our experimental data suggests that fixation disparity can arise when asymmetric opponent vergence pathways are driven by a distributed disparity code.

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NING, Hong-yun, Jin-lan LIU, and De-gan ZHANG. "Intelligent order online negotiation model with incomplete information of opponent." Journal of Computer Applications 29, no. 1 (June 25, 2009): 221–23. http://dx.doi.org/10.3724/sp.j.1087.2009.00221.

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Perevozchikov, A. G., V. Yu Reshetov, and I. E. Yanochkin. "A Discrete Multilevel Attack-Defense Model with Nonhomogeneous Opponent Resources." Computational Mathematics and Modeling 29, no. 2 (February 24, 2018): 134–45. http://dx.doi.org/10.1007/s10598-018-9396-3.

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LEBEDEV, D. S., and D. W. MARSHAK. "Amacrine cell contributions to red-green color opponency in central primate retina: A model study." Visual Neuroscience 24, no. 4 (July 2007): 535–47. http://dx.doi.org/10.1017/s0952523807070502.

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To investigate the contributions of amacrine cells to red-green opponency, a linear computational model of the central macaque retina was developed based on a published cone mosaic. In the model, amacrine cells of ON and OFF types received input from all neighboring midget bipolar cells of the same polarity, but OFF amacrine cells had a bias toward bipolar cells whose center responses were mediated by middle wavelength sensitive cones. This bias might arise due to activity dependent plasticity because there are midget bipolar cells driven by short wavelength sensitive cones in the OFF pathway. The model midget ganglion cells received inputs from neighboring amacrine cells of both types. As in physiological experiments, the model ganglion cells showed spatially opponent responses to achromatic stimuli, but they responded to cone isolating stimuli as though center and surround were each driven by a single cone type. Without amacrine cell input, long and middle wavelength sensitive cones contributed to both the centers and surrounds of model ganglion cell receptive fields. According to the model, the summed amacrine cell input was red-green opponent even though inputs to individual amacrine cells were unselective. A key prediction is that GABA and glycine depolarize two of the four types of central midget ganglion cells; this may reflect lower levels of the potassium chloride co-transporter in their dendrites.

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Verbrugge, Rineke, Ben Meijering, Stefan Wierda, Hedderik van Rijn, and Niels Taatgen. "Stepwise training supports strategic second-order theory of mind in turn-taking games." Judgment and Decision Making 13, no. 1 (January 2018): 79–98. http://dx.doi.org/10.1017/s1930297500008846.

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AbstractPeople model other people’s mental states in order to understand and predict their behavior. Sometimes they model what others think about them as well: “He thinks that I intend to stop.” Such second-order theory of mind is needed to navigate some social situations, for example, to make optimal decisions in turn-taking games. Adults sometimes find this very difficult. Sometimes they make decisions that do not fit their predictions about the other player. However, the main bottleneck for decision makers is to take a second-order perspective required to make a correct opponent model. We report a methodical investigation into supporting factors that help adults do better. We presented subjects with two-player, three-turn games in which optimal decisions required second-order theory of mind (Hedden and Zhang, 2002). We applied three “scaffolds” that, theoretically, should facilitate second-order perspective-taking: 1) stepwise training, from simple one-person games to games requiring second-order theory of mind; 2) prompting subjects to predict the opponent’s next decision before making their own decision; and 3) a realistic visual task representation. The performance of subjects in the eight resulting combinations shows that stepwise training, but not the other two scaffolds, improves subjects’ second-order opponent models and thereby their own decisions.

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Weber, Ben, Michael Mateas, and Arnav Jhala. "A Particle Model for State Estimation in Real-Time Strategy Games." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 7, no. 1 (October 9, 2011): 103–8. http://dx.doi.org/10.1609/aiide.v7i1.12424.

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A big challenge for creating human-level game AI is building agents capable of operating in imperfect information environments. In real-time strategy games the technological progress of an opponent and locations of enemy units are partially observable. To overcome this limitation, we explore a particle-based approach for estimating the location of enemy units that have been encountered. We represent state estimation as an optimization problem, and automatically learn parameters for the particle model by mining a corpus of expert StarCraft replays. The particle model tracks opponent units and provides conditions for activating tactical behaviors in our StarCraft bot. Our results show that incorporating a learned particle model improves the performance of EISBot by 10% over baseline approaches.

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Gao, Xinbo, Hiroyuki Iida, Jos W. H. M. Uiterwijk, and H. Jaap van den Herik. "Strategies anticipating a difference in search depth using opponent-model search." Theoretical Computer Science 252, no. 1-2 (February 2001): 83–104. http://dx.doi.org/10.1016/s0304-3975(00)00077-3.

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34

Sally, Almanasra, Suwais Khaled, and Rafie Arshad Muhammad. "Adaptive automata model for learning opponent behavior based on genetic algorithms." Scientific Research and Essays 7, no. 42 (October 31, 2012): 3609–20. http://dx.doi.org/10.5897/sre11.1860.

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Morgan, M. J., and D. Regan. "Opponent model for line interval discrimination: Interval and vernier performance compared." Vision Research 27, no. 1 (January 1987): 107–18. http://dx.doi.org/10.1016/0042-6989(87)90147-7.

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36

Li, Cheng-Yu, Hans A. Hofmann, Melissa L. Harris, and Ryan L. Earley. "Real or fake? Natural and artificial social stimuli elicit divergent behavioural and neural responses in mangrove rivulus, Kryptolebias marmoratus." Proceedings of the Royal Society B: Biological Sciences 285, no. 1891 (November 14, 2018): 20181610. http://dx.doi.org/10.1098/rspb.2018.1610.

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Understanding how the brain processes social information and generates adaptive behavioural responses is a major goal in neuroscience. We examined behaviour and neural activity patterns in socially relevant brain nuclei of hermaphroditic mangrove rivulus fish ( Kryptolebias marmoratus ) provided with different types of social stimuli: stationary model opponent, regular mirror, non-reversing mirror and live opponent. We found that: (i) individuals faced with a regular mirror were less willing to interact with, delivered fewer attacks towards and switched their orientation relative to the opponent more frequently than fish exposed to a non-reversing mirror image or live opponent; (ii) fighting with a regular mirror image caused higher expression of immediate-early genes (IEGs: egr-1 and c-Fos ) in the teleost homologues of the basolateral amygdala and hippocampus, but lower IEG expression in the preoptic area, than fighting with a non-reversing mirror image or live opponent; (iii) stationary models elicited the least behavioural and IEG responses among the four stimuli; and (iv) the non-reversing mirror image and live opponent drove similar behavioural and neurobiological responses. These results suggest that the various stimuli provide different types of information related to conspecific recognition in the context of aggressive contests, which ultimately drive different neurobiological responses.

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LEU, SOU-SEN, PHAM VU HONG SON, P. E. JUI-SHENG CHOU, and PHAM THI HONG NHUNG. "DEVELOPING FUZZY BAYESIAN GAME MODEL FOR OPTIMIZING NEGOTIATION PRICE." International Journal of Computational Intelligence and Applications 13, no. 04 (December 2014): 1450022. http://dx.doi.org/10.1142/s1469026814500229.

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Construction procurement is a key business where price negotiation is commonly required to reach final contractual agreement. However, even simple negotiations often result in infeasible agreements. The uncertain and limited supplier information as well as complex correlations among various factors affecting supplier behaviors make the contractor difficult to decide the appropriate offer price (OP) and vice versa. This study proposes a novel Fuzzy Bayesian Game Model (FBGM) for improving the prediction effectiveness of negotiation behaviors. The performance of the proposed FBGM was evaluated in the case where an agent uses the counter-OP of an opponent to learn the negotiation strategy of the opponent. The validation analysis shows that the sequential updating process of FBGM significantly improves the estimation ability of negotiators. The proposed model also gives a comprehensive view of negotiation scenarios by considering all possible negotiation cases. Using FBGM, negotiators can apply flexible strategies to optimize their own profit with a reasonable negotiation time.

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RUSINOWSKA, AGNIESZKA. "REFINEMENTS OF NASH EQUILIBRIA IN VIEW OF JEALOUS OR FRIENDLY BEHAVIOR OF PLAYERS." International Game Theory Review 04, no. 03 (September 2002): 281–99. http://dx.doi.org/10.1142/s0219198902000707.

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In this paper, several bargaining models, differing in some assumptions from each other, are analyzed. We consider a discrete case and a continuous case. In the former model, players bargain over a division of n objects. In the latter, parties divide one unit of infinitely divisible good. We start with an analysis of the one-round model, and then we consider a model in which players can continue to bargain. For each model, simultaneous moves as well as alternating offers of players are considered. The assumption that each player receives no more than his/her opponent proposes giving to him/her is the common assumption for all cases analyzed. Moreover, we adopt some assumptions concerning players' attitudes towards their opponents' payments, assuming that players can be either jealous or friendly. In view of the jealousy or friendliness of players, Nash equilibrium and subgame perfect equilibrium are described.

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Slantchev, Branislav L. "Feigning Weakness." International Organization 64, no. 3 (July 2010): 357–88. http://dx.doi.org/10.1017/s002081831000010x.

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AbstractIn typical crisis bargaining models, strong actors must convince the opponent that they are not bluffing and the only way to do so is through costly signaling. However, in a war, strong actors can benefit from tactical surprise when their opponent mistakenly believes that they are weak. This creates contradictory incentives during the pre-war crisis: actors want to persuade the opponent of their strength to gain a better deal but, should war break out, they would rather have the opponent believe they are weak. I present an ultimatum crisis bargaining model that incorporates this dilemma and show that a strong actor may feign weakness during the bargaining phase. This implies that (1) absence of a costly signal is not an unambiguous revelation of weakness, (2) the problem of uncertainty is worse because the only actor with incentives to overcome it may be unwilling to do so, and (3) because of the difficulty with concealing resolve, democracies might be seriously disadvantaged in a crisis.

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Schleiner, Winfried. "Early Modern Controversies about the One-Sex Model." Renaissance Quarterly 53, no. 1 (2000): 180–91. http://dx.doi.org/10.2307/2901536.

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This essay traces the opposition to the Galenic notion of a homology between male and female genitalia (the “one-sex model”) and identifies the French physician André Dulaurens as the first outspoken opponent. After Dulaurens, the German physician Johann Peter Lotichius makes the opposition to that model more clearly an argument that may be called “feminist.”

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Sato, Reo, Kanji Shimomura, and Kenji Morita. "Opponent learning with different representations in the cortico-basal ganglia pathways can develop obsession-compulsion cycle." PLOS Computational Biology 19, no. 6 (June 15, 2023): e1011206. http://dx.doi.org/10.1371/journal.pcbi.1011206.

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Obsessive-compulsive disorder (OCD) has been suggested to be associated with impairment of model-based behavioral control. Meanwhile, recent work suggested shorter memory trace for negative than positive prediction errors (PEs) in OCD. We explored relations between these two suggestions through computational modeling. Based on the properties of cortico-basal ganglia pathways, we modeled human as an agent having a combination of successor representation (SR)-based system that enables model-based-like control and individual representation (IR)-based system that only hosts model-free control, with the two systems potentially learning from positive and negative PEs in different rates. We simulated the agent’s behavior in the environmental model used in the recent work that describes potential development of obsession-compulsion cycle. We found that the dual-system agent could develop enhanced obsession-compulsion cycle, similarly to the agent having memory trace imbalance in the recent work, if the SR- and IR-based systems learned mainly from positive and negative PEs, respectively. We then simulated the behavior of such an opponent SR+IR agent in the two-stage decision task, in comparison with the agent having only SR-based control. Fitting of the agents’ behavior by the model weighing model-based and model-free control developed in the original two-stage task study resulted in smaller weights of model-based control for the opponent SR+IR agent than for the SR-only agent. These results reconcile the previous suggestions about OCD, i.e., impaired model-based control and memory trace imbalance, raising a novel possibility that opponent learning in model(SR)-based and model-free controllers underlies obsession-compulsion. Our model cannot explain the behavior of OCD patients in punishment, rather than reward, contexts, but it could be resolved if opponent SR+IR learning operates also in the recently revealed non-canonical cortico-basal ganglia-dopamine circuit for threat/aversiveness, rather than reward, reinforcement learning, and the aversive SR + appetitive IR agent could actually develop obsession-compulsion if the environment is modeled differently.

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KAMERMANS, M., D. A. KRAAIJ, and H. SPEKREIJSE. "The cone/horizontal cell network: A possible site for color constancy." Visual Neuroscience 15, no. 5 (May 1998): 787–97. http://dx.doi.org/10.1017/s0952523898154172.

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Color vision is spectrally opponent, suggesting that spectrally opponent neurons, such as the horizontal cells in fish and turtle retinae, play a prominent role in color discrimination. In the accompanying paper (Kraaij et al., 1998), it was shown that the output signal of the horizontal cell system to the cones is not at all spectrally opponent. Therefore, a role for the spectrally opponent horizontal cells in color discrimination seems unlikely. In this paper, we propose that the horizontal cells play a prominent role in color constancy and simultaneous color contrast instead of in color discrimination. We have formulated a model of the cone/horizontal cell network based on measurements of the action spectra of the cones and of the feedback signal of the horizontal cell system to the various cone types. The key feature of the model is (1) that feedback is spectrally and spatially very broad and (2) that the gain of the cone synapse strongly depends on the feedback strength. This makes the synaptic gain of the cones strongly dependent on the spectral composition of the surround. Our model, which incorporates many physiological details of the outer retina, displays a behavior that can be interpreted as color constancy and simultaneous color contrast. We propose that the horizontal cell network modulates the cone synaptic gains such that the ratios of the cone outputs become almost invariant with the spectral composition of the global illumination. Therefore, color constancy appears to be coded in the retina.

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Villacorta, Pablo J., and David A. Pelta. "A repeated imitation model with dependence between stages: Decision strategies and rewards." International Journal of Applied Mathematics and Computer Science 25, no. 3 (September 1, 2015): 617–30. http://dx.doi.org/10.1515/amcs-2015-0045.

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Abstract Adversarial decision making is aimed at determining strategies to anticipate the behavior of an opponent trying to learn from our actions. One defense is to make decisions intended to confuse the opponent, although our rewards can be diminished. This idea has already been captured in an adversarial model introduced in a previous work, in which two agents separately issue responses to an unknown sequence of external inputs. Each agent’s reward depends on the current input and the responses of both agents. In this contribution, (a) we extend the original model by establishing stochastic dependence between an agent’s responses and the next input of the sequence, and (b) we study the design of time varying decision strategies for the extended model. The strategies obtained are compared against static strategies from theoretical and empirical points of view. The results show that time varying strategies outperform static ones

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Kusunoki, Makoto, Natasha Sigala, Hamed Nili, David Gaffan, and John Duncan. "Target Detection by Opponent Coding in Monkey Prefrontal Cortex." Journal of Cognitive Neuroscience 22, no. 4 (April 2010): 751–60. http://dx.doi.org/10.1162/jocn.2009.21216.

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The pFC plays a key role in flexible, context-specific decision making. One proposal [Machens, C. K., Romo, R., & Brody, C. D. Flexible control of mutual inhibition: A neural model of two-interval discrimination. Science, 307, 1121–1124, 2005] is that prefrontal cells may be dynamically organized into opponent coding circuits, with competitive groups of cells coding opposite behavioral decisions. Here, we show evidence for extensive, temporally evolving opponent organization in the monkey pFC during a cued target detection task. More than a half of all randomly selected cells discriminated stimulus category in this task. The largest set showed target-positive activity, with the strongest responses to the current target, intermediate activity for a nontarget that was a target on other trials, and lowest activity for nontargets never associated with the target category. Second most frequent was a reverse, antitarget pattern. In the ventrolateral frontal cortex, opponent organization was strongly established in phasic responses at stimulus onset; later, such activity was widely spread across dorsolateral and ventrolateral sites. Task-specific organization into opponent cell groups may be a general feature of prefrontal decision making.

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Boda, Márton Attila. "Avoiding Revenge Using Optimal Opponent Ranking Strategy in the Board Game Catan." International Journal of Gaming and Computer-Mediated Simulations 10, no. 2 (April 2018): 47–70. http://dx.doi.org/10.4018/ijgcms.2018040103.

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The study analyses the attitude of players in a board game called Catan. In Catan, we are basically handling the players as opponents, but this does not rule out the possibility of cooperation. In a game with three players, in order to increase the chances of winning, it is worth acting together against the lead player. Cooperation has several possible modalities. In the article, the focus is on blocking situations which can lead to revenge. The primary objectives of this study were to examine how different types of thinking can cause revenge situations and which are the successful strategies among players. Strategies (as a mathematical solution to a decision problem) are examined in the study via computer modeling. To help the model, some kind of behavior of human Catan players was studied which enables profiling gaming styles used in the model. The winning chances of the player who was not involved in revenge have improved considerably, by 43%. To avoid being involved in revenge situations, the best solution is to accept other players' opponent ranking methods.

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Chuan Lin, Hao-Jun Zhao, and Yi-Jun Cao. "Improved Color Opponent Contour Detection Model Based on Dark and Light Adaptation." Automatic Control and Computer Sciences 53, no. 6 (November 2019): 560–71. http://dx.doi.org/10.3103/s0146411619060075.

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47

Sasithradevi, A., and S. Mohamed Mansoor Roomi. "A new pyramidal opponent color-shape model based video shot boundary detection." Journal of Visual Communication and Image Representation 67 (February 2020): 102754. http://dx.doi.org/10.1016/j.jvcir.2020.102754.

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48

Hansen, Thorsten, and Heiko Neumann. "A simple cell model with dominating opponent inhibition for robust image processing." Neural Networks 17, no. 5-6 (June 2004): 647–62. http://dx.doi.org/10.1016/j.neunet.2004.04.002.

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49

Usui, Shiro, Shigeki Nakauchi, and Sei Miyake. "Acquisition of color opponent representation by a three-layered neural network model." Biological Cybernetics 72, no. 1 (November 1994): 35–41. http://dx.doi.org/10.1007/bf00206236.

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Hansen, Thorsten, Gregory Baratoff, and Heiko Neumann. "A simple cell model with dominating opponent inhibition for robust contrast detection." Kognitionswissenschaft 9, no. 2 (June 2000): 93–100. http://dx.doi.org/10.1007/bf03354941.

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