Готові списки джерел за темами / Opponent model

Добірка наукової літератури з теми "Opponent model"

Автор: Grafiati
Опубліковано: 7 липня 2024

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Статті в журналах з теми "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.
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Статті в журналах Книги

Дисертації з теми "Opponent model":

1

Lau, Hoi Ying. "Neural inspired color constancy model based on double opponent neurons /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?ECED%202008%20LAU.

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2

Koerper, Jason A. "A new colour quality model for ultra-high efficiency light sources with discontinuous spectra." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/112359/1/Jason_Koerper_Thesis.pdf.

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This thesis demonstrates a novel approach to assessing the colour rendition of objects by high efficacy light sources. It establishes a relationship between a light source's output and the visual system, and develops a procedure to evaluate the colour quality of a light source based its fundamental spectral properties. Key wavelengths that link colour rendering and the human colour vision system are identified within. Furthermore, a predictive colour quality model based on the fundamental properties of a light source is presented, which is significantly different to existing, reference-based colour quality measures.
3

Mojalefa, M. J. (Mawatle Jeremiah) 1948. "Tshekatsheko ya Sebilwane bjalo ka thetokanegelo (Sepedi)." Diss., University of Pretoria, 1993. http://hdl.handle.net/2263/24295.

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In this thesis, Sebilwane is the subject of a narratological investigation. The point of departure of this study is based on the fact that a narratological text consists of three levels: the history, the composition, the usage of words which are recognisable in the style of the author. The epic-poem is not the subject of a verse-technical investigation and description. The narratological model is adapted to the aim of this study. The historical level regarded in principle as the original level prior to the material's exposure to a viewpoint and it is interpreted. The four narrative elements that are investigated are: the events, the characters/actors, time and place. In Sebilwane there are main events identified by the criteria of: (a) change, (b) cause, and (c) result. The characters/actors have been described and classified according to: (a) aim, (b) supporter, (c) patron, (d)helper and patron,and (e) opponent. In as far as historical time is concerned, it has been concluded that the events occurred: (a) in the remote past, and (b) stretched it to a 24 hour period. The actors/persons find themselves in a rural area which can be comparable to Botlokwa which is lying within the borders of Lebowa. The composition of the information which is given in the historical level, gives the shape of the author's aim. Here, what is important, are the functions which are described by the elements themselves. Then the idea of the theme comes clearly in this part and it is therefore identified as the main - and sub-theme. The third level concerns the usage of words; the information now gets a personal or subjective selection. Therefore, only a short passage is to be selected for stylistic analysis. The analytic model which is effected here is Kerkhoff's. AFRIKAANS : In hierdie verhandeling word Sebilwane aan 'n narratologiese ondersoek onderwerp. Die uitgangspunt van hierdie studie is dat 'n narratologiese teks uit drie lae bestaan: die geskiedenis, die samestelling, die verwoording wat in die styl van die outeur kenbaar is. Hierdie epiese gedig word nie verstegnies ondersoek en beskryf nie. Die narratologiese model is vir die doel van hierdie studie aangepas. Die geskiedenislaag word in beginsel as die oorspronklike laag beskou voordat die gegewens vanuit 'n bepaalde gesigspunt bekyk en weergegee word. Die vier vertelelemente wat ondersoek word, is die gebeurtenisse, die karakters/akteurs, tyd en plek. In Sebilwane is die kerngebeurtenisse geïdentifiseer deur die kriteria van (a) verandering, (b) oorsaak en (c) afloop. Die karakters/akteurs is beskryf en geklassifiseer volgens (a) doelstelling, (b) begunstigde, (c) begunstiger, (d) helper en (e) teëstaander. Wat die tyd betref, speel die gebeure (a) histories in die verre verlede af, en (b) strek dit oor 'n 24 uur tydperk. Die akteurs/mense bevind hulle in 'n landelike gebied wat waarskynlik Botlokwa is wat binne Lebowa geleë is. Die samestelling van die gegewens wat in die geskiedenislaag gegee is, gee aan die doestelling van die outeur gestalte. Daarvan gaan dit hier om die funksies wat aan die elemente toegesê word. Die begrip van die tema staan in hierdie gedeelte voorop, en daar word 'n hoof - en 'n subtema geïdentifiseer.
Dissertation (MA)--University of Pretoria, 1993.
African Languages
unrestricted
4

Sailer, Zbyněk. "Vyhledání podobných obrázků pomocí popisu barevným histogramem." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236514.

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This thesis deals with description of existing methods of image retrieval. It contains set of methods for image description, coding of global and local descriptor (SIFT, etc.) and describes method of effective searching in multidimensional space (LSH). It continues with proposal and testing of three global descriptors using color histograms, histogram of gradients and the combination of both. The last part deals with similar image retrieval using proposed descriptors and the indexing method LSH and compares the results with the existing method. Product of this work is an experimental application which demonstrates the proposed solution.
5

Li, Junkang. "Games with incomplete information : complexity, algorithmics, reasoning." Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMC270.

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Dans cette thèse, on étudie les jeux à information incomplète. On commence par établir un paysage complet de la complexité du calcul des stratégies pures optimales pour différentes sous-catégories de jeux, lorsque les jeux sont explicitement donnés en entrée. On étudie ensuite la complexité lorsque les jeux sont représentés de façon compacte (par leurs règles de jeu, par exemple). Pour ce faire, on conçoit deux formalismes pour ces représentations compactes. Dans la suite, on se consacre aux jeux à information incomplète, en proposant d'abord un nouveau formalisme, nommé jeu combinatoire à information incomplète, qui regroupe les jeux sans hasard (sauf un tirage aléatoire initial) et avec uniquement des actions publiques. Pour de tels jeux, ce nouveau formalisme capture la notion de l'information et de la connaissance des joueurs mieux que la forme extensive. Puis, on étudie des algorithmes et leurs optimisations pour résoudre les jeux combinatoires à information incomplète ; certains algorithmes que l'on propose sont applicables au-delà de ces jeux. Dans la dernière partie, on présente un travail en cours, qui consiste à modéliser les raisonnements récursifs et différents types de connaissances sur le comportement des adversaires dans les jeux à information incomplète
In this dissertation, we study games with incomplete information. We begin by establishing a complete landscape of the complexity of computing optimal pure strategies for different subclasses of games, when games are given explicitly as input. We then study the complexity when games are represented compactly (e.g.\ by their game rules). For this, we design two formalisms for such compact representations. Then we concentrate on games with incomplete information, by first proposing a new formalism called combinatorial game with incomplete information, which encompasses games of no chance (apart from a random initial drawing) and with only public actions. For such games, this new formalism captures the notion of information and knowledge of the players in a game better than extensive form. Next, we study algorithms and their optimisations for solving combinatorial games with incomplete information; some of these algorithms are applicable beyond these games. In the last part, we present a work in progress that concerns the modelling of recursive reasoning and different types of knowledge about the behaviour of the opponents in games with incomplete information
6

Hladky, Stephen Michael. "Predicting opponent locations in first-person shooter video games." Master's thesis, 2009. http://hdl.handle.net/10048/600.

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Thesis (M. Sc.)--University of Alberta, 2009.
Title from PDF file main screen (viewed on Oct. 2, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Department of Computing Science, University of Alberta." Includes bibliographical references.

Книги з теми "Opponent model":

1

K, Davis Paul. Thinking about opponent behavior in crisis and conflict: A generic model for analysis and group discussion. Santa Monica, CA: Rand, 1991.

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2

Graziano, William G., and Renée M. Tobin. Agreeableness and the Five Factor Model. Edited by Thomas A. Widiger. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199352487.013.17.

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Agreeableness is a summary label for individual differences in the motivation to maintain positive relations with others. Agreeableness is one of the major dimensions in the Big Five structural model of personality. It is also a major domain in the Five Factor Model of personality. This chapter provides an overview of the considerable body of research concerning the conceptualization, assessment, and etiology of Agreeableness with a focus on its six facets. It concludes with a discussion of alternative theoretical explanations for Agreeableness. In particular, an opponent process model that involves two competing motive systems is applied to the processes underlying Agreeableness.
3

Laver, Michael, and Ernest Sergenti. Endogenous Parties, Interaction of Different Decision Rules. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.003.0006.

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This chapter attempts to develop more realistic and interesting models in which the set of competing parties is a completely endogenous output of the process of party competition. It also seeks to model party competition when different party leaders use different decision rules in the same setting by building on an approach pioneered in a different context by Robert Axelrod. This involves long-running computer “tournaments” that allow investigation of the performance and “robustness” of decision rules in an environment where any politician using any rule may encounter an opponent using either the same decision rule or some quite different rule. The chapter is most interested in how a decision rule performs against anything the competitive environment might throw against it, including agents using decision rules that are difficult to anticipate and/or comprehend.
4

Hoppe, Sherry, and Bruce W. Speck, eds. Service-Learning. Praeger, 2004. http://dx.doi.org/10.5040/9798216013013.

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Although service-learning programs can have diverse theoretical roots, faculty who engage their students in service-learning may not be be cognizant of alternatives to the one they adopt. This book presents not only a historical perspective, but it also debates the theories and issues surrounding the conflicts inherent in those theories. One theory, based on a philanthropic model, engages students in a commitment to serve others from a sense of gratitude for their own good fortunes or from a desire to give back to communities from which they have benefited. Typically, service-learning programs based on the philanthropic or communitarian models deal with the overt needs of community members. In contrast, the civic model requires deeper analysis of the various political and social issues that may be the cause of social conditions that require the help of the more fortunate. Opponents of the civic theory fear that proponents see the classroom as a forum for advancing particular political agendas, conceivably indoctrinating students to a particular view of social injustices. This book presents the theories and critiques their merits and liabilities, providing insight into the widely divergent curricular applications. It also examines the reasons professors should consider service-learning components in their classes and provides resources for further investigation of both theory and practice.
5

Thompson, Douglas I. Montaigne and the Tolerance of Politics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190679934.001.0001.

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Montaigne and the Tolerance of Politics provides a new interpretation of Michel de Montaigne’s Essais in the context of his activity as a political negotiator between combatant parties during the French Wars of Religion. At the heart of the Essais lies a political conception of tolerance that is rarely considered today. Tolerance is usually conceived as an individual ethical disposition or a moral principle of public law. For Montaigne, tolerance is instead a political capacity: the power and ability to negotiate relationships of basic trust and civil peace with one’s opponents in political conflict. Contemporary thinkers often argue that what matters most for tolerance is how one talks to one’s political opponents: with respect, reasonableness, and civility. For Montaigne, what matters most is not how, but rather that opponents talk to each other across lines of disagreement. Using his own experience negotiating between Catholic and Huguenot parties as a model, Montaigne investigates and prescribes a set of skills and capacities that might help his readers become the kinds of people who can initiate and sustain dialogue with the “other side” to achieve public goods—even when respect, reasonableness, and civility are not yet assured. Montaigne and the Tolerance of Politics argues that this dimension of tolerance is worth recovering and reconsidering in contemporary democratic societies, in which partisan “sorting” and multidimensional polarization have evidently rendered political leaders and ordinary citizens less and less able to talk to each other to resolve political conflicts and to work for shared public goods.
6

Thompson, Douglas I. The Power of Uncivil Conversation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190679934.003.0004.

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This chapter investigates Michel de Montaigne’s engagement with the Italian humanist conception of “civil conversation” as an exercise for training effective political counselors and ambassadors. Using himself as a model, Montaigne prescribes a more confrontational, uncivil form of conversation as a means to train his readers into a high tolerance for political negotiation with the widest possible range of interlocutors and opinions. Following the conventions of the humanist literature on political education, Montaigne argues that the best way to practice political negotiation and other forms of conversation with political opponents is to go out and do it. The chapter then compares this theme of the Essais with Rainer Forst’s conception of tolerance as a form of civil public reason.
7

Laver, Michael, and Ernest Sergenti. Party Competition. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.001.0001.

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Party competition for votes in free and fair elections involves complex interactions by multiple actors in political landscapes that are continuously evolving, yet classical theoretical approaches to the subject leave many important questions unanswered. This book offers the first comprehensive treatment of party competition using the computational techniques of agent-based modeling. This exciting new technology enables researchers to model competition between several different political parties for the support of voters with widely varying preferences on many different issues. The book models party competition as a true dynamic process in which political parties rise and fall, a process where different politicians attack the same political problem in very different ways, and where today's political actors, lacking perfect information about the potential consequences of their choices, must constantly adapt their behavior to yesterday's political outcomes. This book shows how agent-based modeling can be used to accurately reflect how political systems really work. It demonstrates that politicians who are satisfied with relatively modest vote shares often do better at winning votes than rivals who search ceaselessly for higher shares of the vote. It reveals that politicians who pay close attention to their personal preferences when setting party policy often have more success than opponents who focus solely on the preferences of voters, that some politicians have idiosyncratic “valence” advantages that enhance their electability—and much more.
8

Garloff, Katja. Mixed Feelings. Cornell University Press, 2017. http://dx.doi.org/10.7591/cornell/9781501704963.001.0001.

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Since the late eighteenth century, writers and thinkers have used the idea of love—often unrequited or impossible love—to comment on the changing cultural, social, and political position of Jews in the German-speaking countries. This book asks what it means for literature (and philosophy) to use love between individuals as a metaphor for group relations. This question is of renewed interest today, when theorists of multiculturalism turn toward love in their search for new models of particularity and universality. The book is structured around two transformative moments in German Jewish culture and history that produced particularly rich clusters of interfaith love stories. Around 1800, literature promoted the rise of the Romantic love ideal and the shift from prearranged to love-based marriages. In the German-speaking countries, this change in the theory and practice of love coincided with the beginnings of Jewish emancipation, and both its supporters and opponents linked their arguments to tropes of love. The book explores the generative powers of such tropes in Moses Mendelssohn, G. E. Lessing, Friedrich Schlegel, Dorothea Veit, and Achim von Arnim. Around 1900, the rise of racial antisemitism had called into question the promises of emancipation and led to a crisis of German Jewish identity. At the same time, Jewish-Christian intermarriage prompted public debates that were tied up with racial discourses and concerns about procreation, heredity, and the mutability and immutability of the Jewish body. The text shows how modern German Jewish writers such as Arthur Schnitzler, Else Lasker-Schüler, and Franz Rosenzweig wrestle with this idea of love away from biologist thought and reinstate it as a model of sociopolitical relations. It concludes by tracing the relevance of this model in post-Holocaust works by Gershom Scholem, Hannah Arendt, and Barbara Honigmann.
9

Markwica, Robin. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198794349.003.0001.

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Why do states frequently reject coercive threats from more powerful opponents? This introductory chapter begins by outlining the explanations in the existing literature for failures of coercive diplomacy. It suggests that these accounts generally share a cognitivist perspective that neglects the role of emotion in target leaders’ decision-making. To capture the social, physiological, and dynamic nature of emotion, it is necessary to introduce an additional action model besides the traditional rationalist and constructivist paradigms. The chapter provides a summary of this logic of affect, or emotional choice theory, which includes a series of propositions specifying the emotional conditions under which target leaders are likely to accept or reject a coercer’s demands. Next, it justifies the selection of the case studies and the book’s focus on political leaders. The chapter ends with a brief outline of the rest of the study.
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Pick, Daniel. 1. Introduction. Oxford University Press, 2015. http://dx.doi.org/10.1093/actrade/9780199226818.003.0001.

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The ‘Introduction’ provides an overview of psychoanalysis, its history, and its development. Psychoanalysis is an original method of therapy that is a form of inquiry, a theory of mind, and a mode of treatment concerned, above all, with the unconscious mind. Founded by Sigmund Freud (1856–1939), it became a movement and set of institutions, inspiring many, but also galvanizing numerous opponents. Freud’s method of free association involves allowing the patient to discuss anything that comes into their mind. The analyst is tasked with attending to possible unconscious meaning in what the patient brings. Critique of psychoanalysis has taken many forms. Sometimes disagreements spurred new ideas and modified techniques within the mainstream tradition.
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Частини книг з теми "Opponent model":

1

Donkers, Jeroen, Jaap van den Herik, and Jos Uiterwijk. "Probabilistic Opponent-Model Search in Bao." In Entertainment Computing – ICEC 2004, 409–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28643-1_53.

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2

Chang, Hung-Jui, Cheng Yueh, Gang-Yu Fan, Ting-Yu Lin, and Tsan-sheng Hsu. "Opponent Model Selection Using Deep Learning." In Lecture Notes in Computer Science, 176–86. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11488-5_16.

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3

van der Zwet, Koen, Ana Isabel Barros, Tom M. van Engers, and Bob van der Vecht. "An Agent-Based Model for Emergent Opponent Behavior." In Lecture Notes in Computer Science, 290–303. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22741-8_21.

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4

van Galen Last, Niels. "Agent Smith: Opponent Model Estimation in Bilateral Multi-issue Negotiation." In New Trends in Agent-Based Complex Automated Negotiations, 167–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24696-8_12.

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5

Donkers, H. H. L. M., H. J. Herik, and J. W. H. M. Uiterwijk. "Opponent-Model Search in Bao: Conditions for a Successful Application." In Advances in Computer Games, 309–24. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-0-387-35706-5_20.

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6

Salam, Khan Md Mahbubush, and Kazuyuki Ikko Takahashi. "Mathematical model of conflict and cooperation with non-annihilating multi-opponent." In Unifying Themes in Complex Systems, 299–306. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-85081-6_38.

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7

Black, Elizabeth, and Anthony Hunter. "Reasons and Options for Updating an Opponent Model in Persuasion Dialogues." In Theory and Applications of Formal Argumentation, 21–39. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28460-6_2.

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8

Bullock, Daniel, José L. Contreras-Vidal, and Stephen Grossberg. "Equilibria and Dynamics of a Neural Network Model for Opponent Muscle Control." In Neural Networks in Robotics, 439–57. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3180-7_25.

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9

Zhang, Yicheng, Jiannan Zhao, Mu Hua, Hao Luan, Mei Liu, Fang Lei, Heriberto Cuayahuitl, and Shigang Yue. "O-LGMD: An Opponent Colour LGMD-Based Model for Collision Detection with Thermal Images at Night." In Lecture Notes in Computer Science, 249–60. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15934-3_21.

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10

Mudgal, Chhaya, and Julita Vassileva. "Bilateral Negotiation with Incomplete and Uncertain Information: A Decision-Theoretic Approach Using a Model of the Opponent." In Cooperative Information Agents IV - The Future of Information Agents in Cyberspace, 107–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-540-45012-2_11.

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Тези доповідей конференцій з теми "Opponent model":

1

Nakano, Yasuhisa. "New model for brightness perception." In Advances in Color Vision. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/acv.1992.fd5.

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Brightness-luminance discrepancy is the well known issue in color vision. To overcome this discrepancy, several models of brightness perception are proposed1),2) based on color vision model. Common idea of these models is that the brightness-luminance discrepancy yield from contributions of red-green and yellow-blue color opponent channels to the brightness perception. Recently, however, Nakano, Ikeda and Kaiser (1988)3) proposed another type of model. They explained the brightness perception using L-M and M-L type opponent mechanisms in stead of luminance and color opponent channels. They also showed that individual difference of brightness matching was explained by adjusting opponency of these two types of opponent mechanisms. In this paper, I modified their model so that it could explain individual data of spectral luminous efficiency function obtained by heterochromatic brightness matching. The data of twelve subjects were used in this analysis. The new model explains brightness matching data of composite lights as well as spectral data.
2

Hernandez, Daniel, Hendrik Baier, and Michael Kaisers. "BRExIt: On Opponent Modelling in Expert Iteration." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/422.

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Finding a best response policy is a central objective in game theory and multi-agent learning, with modern population-based training approaches employing reinforcement learning algorithms as best-response oracles to improve play against candidate opponents (typically previously learnt policies). We propose Best Response Expert Iteration (BRExIt), which accelerates learning in games by incorporating opponent models into the state-of-the-art learning algorithm Expert Iteration (ExIt). BRExIt aims to (1) improve feature shaping in the apprentice, with a policy head predicting opponent policies as an auxiliary task, and (2) bias opponent moves in planning towards the given or learnt opponent model, to generate apprentice targets that better approximate a best response. In an empirical ablation on BRExIt's algorithmic variants against a set of fixed test agents, we provide statistical evidence that BRExIt learns better performing policies than ExIt. Code available at: https://github.com/Danielhp95/on-opponent-modelling-in-expert-iteration-code. Supplementary material available at https://arxiv.org/abs/2206.00113.
3

Ahumada, Albert J. "Learning a red–green opponent system from LGN inputs." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.wr5.

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Ahumada and Mulligan1 proposed a network model for constructing a red–green opponent system from LGN outputs without specific long versus middle wavelength cone labeling. They constructed model LGN cells having long and middle cone inputs and fed the LGN outputs into units, presumed cortical, which were trained by a network learning process to compute the principal component of the LGN units in its receptive field. These units turn out approximately doubly opponent and have less luminance sensitivity than their LGN input cells. Their outputs were then calibrated by translation invariance. Derrington, Krauskopf, and Lennie2 and Young3 have published measurements of relative long, middle, and short wavelength cone weightings for actual LGN cells. These weights are sufficient to compute LGN outputs for training the model opponent cells. Model cortical cells were constructed by taking a random sample of seven LGN cells from a data set and computing their principal component weights. The resulting cells are highly red-green opponent (less luminance sensitivity and smaller short wavelength cone weightings). The strong opponency of the LGN cells and the infrequency of large short wavelength weights allows the construction of red-green opponent cells without knowledge of their input sources.
4

Gershon, Ron, and John K. Tsotsos. "Experiments with a spatiochromatic model of early vision." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.wd2.

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We claim that color vision provides important information about properties of surfaces. To process the chromatic information in images, one has to use a model of color vision and determine its responses to different types of input. Our model consists of different operators which resemble in their characteristics the different layers in the biological chromatic visual pathways. In particular, we look at color-opponent and double-opponent cells with center-surround spatial organization. We characterize the mathematical properties of these units and test their responses with respect to changes in wavelength intensity and space. Our results1 show that the opponent operators give information about the chromatic content of the input in terms of two mutually exclusive color pairs. Moreover, these operators distinguish areas which are isoluminant from areas which have different luminance levels. The double-opponent units detect color borders, no matter the luminance levels. They have bandpass properties, similar to the difference-of-Gaussians (DOG) operator,2 with the advantage of responding to some stimuli which the DOG will not respond to. In addition, the double-opponent operators seem to disregard certain types of shadow, which leads to speculation about their inability to discriminate between the lit and shadow areas. This information, in coordination with responses from opponent operators, can be useful in detecting changes in materials.
5

Zhang, Weinan, Xihuai Wang, Jian Shen, and Ming Zhou. "Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/466.

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This paper investigates the model-based methods in multi-agent reinforcement learning (MARL). We specify the dynamics sample complexity and the opponent sample complexity in MARL, and conduct a theoretic analysis of return discrepancy upper bound. To reduce the upper bound with the intention of low sample complexity during the whole learning process, we propose a novel decentralized model-based MARL method, named Adaptive Opponent-wise Rollout Policy Optimization (AORPO). In AORPO, each agent builds its multi-agent environment model, consisting of a dynamics model and multiple opponent models, and trains its policy with the adaptive opponent-wise rollout. We further prove the theoretic convergence of AORPO under reasonable assumptions. Empirical experiments on competitive and cooperative tasks demonstrate that AORPO can achieve improved sample efficiency with comparable asymptotic performance over the compared MARL methods.
6

Tian, Zheng, Ying Wen, Zhichen Gong, Faiz Punakkath, Shihao Zou, and Jun Wang. "A Regularized Opponent Model with Maximum Entropy Objective." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/85.

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In a single-agent setting, reinforcement learning (RL) tasks can be cast into an inference problem by introducing a binary random variable o, which stands for the "optimality". In this paper, we redefine the binary random variable o in multi-agent setting and formalize multi-agent reinforcement learning (MARL) as probabilistic inference. We derive a variational lower bound of the likelihood of achieving the optimality and name it as Regularized Opponent Model with Maximum Entropy Objective (ROMMEO). From ROMMEO, we present a novel perspective on opponent modeling and show how it can improve the performance of training agents theoretically and empirically in cooperative games. To optimize ROMMEO, we first introduce a tabular Q-iteration method ROMMEO-Q with proof of convergence. We extend the exact algorithm to complex environments by proposing an approximate version, ROMMEO-AC. We evaluate these two algorithms on the challenging iterated matrix game and differential game respectively and show that they can outperform strong MARL baselines.
7

Howett, Gerald L. "Linear opponent-colors model optimized for brightness prediction." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.wu3.

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Formal multivariate optimization techniques were applied in an attempt to determine how well a linear, opponent-colors model of color vision could account for specific brightness-matching data. The data fitted were from the Sanders-Wyszecki experiment that matched an adjustable white light in brightness to each of a set of lights of ninety-six different chromaticities and constant luminance. A generalized, linear, opponent-colors model was formulated, which includes the linear models of Guth (and co-workers), Ingling (and co-workers), and Thornton as special cases. The model contained ten parameters, including nine determining the spectral responses of the three opponent-level channels, and one determining the rule for combining the outputs of the three channels to obtain an estimate of equivalent luminance (the luminance of an equally bright white light). Despite difficulties with the optimization procedure, a model was found that correlates better than 0.98 with the fitted data. The predictions of this model for various other color vision functions were explored. These predictions are less than perfect but surprisingly good considering that the model was optimized entirely on brightness data (the only restriction being that the luminance channel should have no negative values).
8

Riley, Patrick, and Manuela Veloso. "Coaching a simulated soccer team by opponent model recognition." In the fifth international conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/375735.376034.

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9

Zafari, Farhad, and Faria Nassiri-Mofakham. "POPPONENT: Highly accurate, individually and socially efficient opponent preference model in bilateral multi issue negotiations (Extended Abstract)." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/730.

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In automated bilateral multi issue negotiations, two intelligent automated agents negotiate on behalf of their owners over many issues in order to reach an agreement. Modeling the opponent can excessively boost the performance of the agents and increase the quality of the negotiation outcome. State of the art models accomplish this by considering some assumptions about the opponent which restricts their applicability in real scenarios. In this paper, a less restricted technique where perceptron units (POPPONENT) are applied in modelling the preferences of the opponent is proposed. This model adopts a Multi Bipartite version of the Standard Gradient Descent search algorithm (MBGD) to find the best hypothesis, which is the best preference profile. In order to evaluate the accuracy and performance of this proposed opponent model, it is compared with the state of the art models available in the Genius repository. This results in the devised setting which approves the higher accuracy of POPPONENT compared to the most accurate state of the art model. Evaluating the model in the real world negotiation scenarios in the Genius framework also confirms its high accuracy in relation to the state of the art models in estimating the utility of offers. The findings here indicate that the proposed model is individually and socially efficient. This proposed MBGD method could also be adopted in similar practical areas of Artificial Intelligence.
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Mathibela, Bonolo, Ingmar Posner, and Paul Newman. "A roadwork scene signature based on the opponent colour model." In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013). IEEE, 2013. http://dx.doi.org/10.1109/iros.2013.6696987.

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Звіти організацій з теми "Opponent model":

1

Howett, Gerald L. Linear opponent-colors model optimized for brightness prediction. Gaithersburg, MD: National Bureau of Standards, 1986. http://dx.doi.org/10.6028/nbs.ir.85-3202.

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2

Bobashev, Georgiy, John Holloway, Eric Solano, and Boris Gutkin. A Control Theory Model of Smoking. RTI Press, June 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0040.1706.

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We present a heuristic control theory model that describes smoking under restricted and unrestricted access to cigarettes. The model is based on the allostasis theory and uses a formal representation of a multiscale opponent process. The model simulates smoking behavior of an individual and produces both short-term (“loading up” after not smoking for a while) and long-term smoking patterns (e.g., gradual transition from a few cigarettes to one pack a day). By introducing a formal representation of withdrawal- and craving-like processes, the model produces gradual increases over time in withdrawal- and craving-like signals associated with abstinence and shows that after 3 months of abstinence, craving disappears. The model was programmed as a computer application allowing users to select simulation scenarios. The application links images of brain regions that are activated during the binge/intoxication, withdrawal, or craving with corresponding simulated states. The model was calibrated to represent smoking patterns described in peer-reviewed literature; however, it is generic enough to be adapted to other drugs, including cocaine and opioids. Although the model does not mechanistically describe specific neurobiological processes, it can be useful in prevention and treatment practices as an illustration of drug-using behaviors and expected dynamics of withdrawal and craving during abstinence.
3

Millán, Jaime. The Second Generation of Power Exchanges: Lessons for Latin America. Inter-American Development Bank, December 1999. http://dx.doi.org/10.18235/0006812.

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Almost two decades after the beginning of the Chilean and English experiments in power sector reform and privatization, many other countries have adopted or are in the process of adopting a model that promotes competition in the wholesale power market that is based partly on the pioneering efforts of those two countries. Some countries which adopted the English model but whose systems are dominated by hydroelectric power found themselves constrained by a structure that did not apply to their particular situations. And now, England and Chile are themselves radically revising their power trading arrangements. This paper attempts to answer the questions: Does this mean that their systems failed and that the countries that adopted them should go on the alert and adjust their models?; Or does it mean that the experiment failed and that the opponents of reform and those who maintained that it was impossible to mount a competitive model in the wholesale electricity market were right? This paper looks at the structure of the power markets (first-generation and second-generation reforms) in Chile, England/Wales, Argentina, Norway, Colombia, Australia, the United States and Spain.

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