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Journal articles on the topic 'Opponent model'

Author: Grafiati

Published: 7 July 2024

Last updated: 31 July 2025

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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 (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

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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 signi

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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 (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.

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Jing, Yuheng, Kai Li, Bingyun Liu, et al. "An Open-Ended Learning Framework for Opponent Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 22 (2025): 23222–30. https://doi.org/10.1609/aaai.v39i22.34488.

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Opponent Modeling (OM) aims to enhance decision-making by modeling other agents in multi-agent environments. Existing works typically learn opponent models against a pre-designated fixed set of opponents during training. However, this will cause poor generalization when facing unknown opponents during testing, as previously unseen opponents can exhibit out-of-distribution (OOD) behaviors that the learned opponent models cannot handle. To tackle this problem, we introduce a novel Open-Ended Opponent Modeling (OEOM) framework, which continuously generates opponents with diverse strengths and sty

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Otto, Jacob, and William Spaniel. "Doubling Down: The Danger of Disclosing Secret Action." International Studies Quarterly 65, no. 2 (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 ma

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Liu, Chanjuan, Jinmiao Cong, Tianhao Zhao, and Enqiang Zhu. "Improving Agent Decision Payoffs via a New Framework of Opponent Modeling." Mathematics 11, no. 14 (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

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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 (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 oppo

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

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9

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 (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) da

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

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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

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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 16, no. 4 (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 (1994): 10–14. http://dx.doi.org/10.3233/icg-1994-17103.

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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 (1997): 513–21. http://dx.doi.org/10.1016/s0091-3057(96)00438-8.

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15

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 (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 q

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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

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17

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

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18

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 (2005): 245–67. http://dx.doi.org/10.1016/j.tcs.2005.09.049.

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19

Dewan, Torun, and Rafael Hortala-Vallve. "Electoral Competition, Control and Learning." British Journal of Political Science 49, no. 3 (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 – ba

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20

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 (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 eff

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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 clu

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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 (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

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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 (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 oppo

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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 (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 computatio

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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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Patel, Saumil S., Bai-Chuan Jiang, and Haluk Ogmen. "Vergence Dynamics Predict Fixation Disparity." Neural Computation 13, no. 7 (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 neura

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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 (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 stren

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Zhang, Ya, Jinghua Wu, and Ruiyang Cao. "Optimizing Automated Negotiation: Integrating Opponent Modeling with Reinforcement Learning for Strategy Enhancement." Mathematics 13, no. 4 (2025): 679. https://doi.org/10.3390/math13040679.

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Agent-based automated negotiation aims to enhance decision-making processes by predefining negotiation rules, strategies, and objectives to achieve mutually acceptable agreements. However, most existing research primarily focuses on modeling the formal negotiation phase, while neglecting the critical role of opponent analysis during the pre-negotiation stage. Additionally, the impact of opponent selection and classification on strategy formulation is often overlooked. To address these gaps, we propose a novel automated negotiation framework that enables the agent to use reinforcement learning,

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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 (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.

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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 (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 (2018): 134–45. http://dx.doi.org/10.1007/s10598-018-9396-3.

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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 (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 r

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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 (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 p

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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 (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 oppone

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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 (2001): 83–104. http://dx.doi.org/10.1016/s0304-3975(00)00077-3.

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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 (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 (1987): 107–18. http://dx.doi.org/10.1016/0042-6989(87)90147-7.

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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 (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 i

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Slantchev, Branislav L. "Feigning Weakness." International Organization 64, no. 3 (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 dilemm

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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 (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

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Epp-Stobbe, Amarah, Ming-Chang Tsai, and Marc Klimstra. "A Comparison of the Application of Load Monitoring Metrics for Key Match Characteristics in Women’s Rugby Sevens." Applied Sciences 15, no. 5 (2025): 2344. https://doi.org/10.3390/app15052344.

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In rugby sevens, multiple high-speed matches in quick succession make effective workload monitoring essential to support decision-making around athlete preparedness and competition strategy. Match characteristics like score differential, player’s competition experience, match type, and opponent may influence workload. The purpose of this investigation was to examine the relationships between match and player characteristics and three workload measures, session rating of perceived exertion (sRPE), mechanical work, and an alternative speed–deceleration–contact (SDC) model. Twenty-two female rugb

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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 (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, wit

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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 (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 dis

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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 (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

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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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Suvechcha Sengupta and Rakesh Verma. "Compromised payoffs in the presence of incomplete information for supply chain applications." World Journal of Advanced Research and Reviews 25, no. 2 (2025): 434–47. https://doi.org/10.30574/wjarr.2025.25.2.0318.

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People in situation of conflict are often found to engage based on their own understanding of expected utility from the said conflict. Should people behave completely rational, they would cease to engage the moment there is visibility on one’s earnings from the conflict. However, in reality the observable patterns and trends are different given that not always are people engaging rationally. What makes this complex is the fact that many a times a person does not have the required information to be able to engage in a fair play. Distortion in communication and presence of irrationality contribu

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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 (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

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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 (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

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Scott-Samuel, N. E., and M. A. Georgeson. "Motion Contrast: A New Metric for Direction Discrimination." Perception 26, no. 1_suppl (1997): 348. http://dx.doi.org/10.1068/v970204.

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The Adelson - Bergen energy model (1985 Journal of the Optical Society of America A2 284 – 299) is a standard framework for understanding 1st-order motion processing. Its output, the opponent energy for a given input, is calculated by subtracting one directional energy measure ( R) from its opposite ( L), and its sign indicates the direction of motion of the input. Our observers viewed a dynamic sequence of gratings (1 cycle deg−1) equivalent to the sum of two gratings moving in opposite directions with different contrasts, and had to report the dominant direction of motion. The ratio of contr

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