Opponent's Preferences Research Articles

Emotional agents enabled bilateral negotiation: Persuasion strategies generated by agents' affect infusion and preference

Agents with emotional intelligence can enhance negotiation outcomes by improving interaction and better understanding of human opponents in human–machine negotiation. This paper proposes a novel method to improve agents' emotional intelligence level by modeling emotion and preferences in negotiation. Emotion is modeled to reflect an agent’s enduring emotional state and its dynamically instantaneous response to the opponent’s proposals. Emotion-driven rules are subsequently constructed to generate persuasion strategies. Then, a two-step issue updating model which integrates persuasion strategies, attribute preference, and altruistic preference is established to generate optimal issue values using the simulated annealing algorithm. After that, the proposed model is validated by agent-agent experiments and human-agent experiments. The experimental results show that (1) the introduction of emotion and persuasion strategies enhances negotiation success, with joint utility rising by 11%, utility difference decreasing by 28%, and negotiation speed rising by 24%.; (2) the inclusion of preference reduce the utility difference and improve the negotiation fairness, with attribute preference and altruistic preference reducing utility difference by 12% and 18%, respectively; (3) the proposed model outperforms competing models in negotiation speed and joint utility, and ensures larger joint utility and minimizing differences even when opponent's preferences are unknown. The results imply that the proposed model is conducive to achieving a win–win human-agent negotiation outcome.

The effect of task load, information reliability and interdependency on anticipation performance

In sport, coaches often explicitly provide athletes with stable contextual information related to opponent action preferences to enhance anticipation performance. This information can be dependent on, or independent of, dynamic contextual information that only emerges during the sequence of play (e.g. opponent positioning). The interdependency between contextual information sources, and the associated cognitive demands of integrating information sources during anticipation, has not yet been systematically examined. We used a temporal occlusion paradigm to alter the reliability of contextual and kinematic information during the early, mid- and final phases of a two-versus-two soccer anticipation task. A dual-task paradigm was incorporated to investigate the impact of task load on skilled soccer players’ ability to integrate information and update their judgements in each phase. Across conditions, participants received no contextual information (control) or stable contextual information (opponent preferences) that was dependent on, or independent of, dynamic contextual information (opponent positioning). As predicted, participants used reliable contextual and kinematic information to enhance anticipation. Further exploratory analysis suggested that increased task load detrimentally affected anticipation accuracy but only when both reliable contextual and kinematic information were available for integration in the final phase. This effect was observed irrespective of whether the stable contextual information was dependent on, or independent of, dynamic contextual information. Findings suggest that updating anticipatory judgements in the final phase of a sequence of play based on the integration of reliable contextual and kinematic information requires cognitive resources.

An opponent model for agent-based shared decision-making via a genetic algorithm.

Shared decision-making (SDM) has received a great deal of attention as an effective way to achieve patient-centered medical care. SDM aims to bring doctors and patients together to develop treatment plans through negotiation. However, time pressure and subjective factors such as medical illiteracy and inadequate communication skills prevent doctors and patients from accurately expressing and obtaining their opponent's preferences. This problem leads to SDM being in an incomplete information environment, which significantly reduces the efficiency of the negotiation and even leads to failure. In this study, we integrated a negotiation strategy that predicts opponent preference using a genetic algorithm with an SDM auto-negotiation model constructed based on fuzzy constraints, thereby enhancing the effectiveness of SDM by addressing the problems posed by incomplete information environments and rapidly generating treatment plans with high mutual satisfaction. A variety of negotiation scenarios are simulated in experiments and the proposed model is compared with other excellent negotiation models. The results indicated that the proposed model better adapts to multivariate scenarios and maintains higher mutual satisfaction. The agent negotiation framework supports SDM participants in accessing treatment plans that fit individual preferences, thereby increasing treatment satisfaction. Adding GA opponent preference prediction to the SDM negotiation framework can effectively improve negotiation performance in incomplete information environments.

Opponent's Preference Estimation Considering Their Offer Transition in Multi-Issue Closed Negotiations

Opponent's Preference Estimation Considering Their Offer Transition in Multi-Issue Closed Negotiations

The Evolution of 'Theory of Mind': Theory and Experiments

This paper investigates the evolutionary foundation for our capacity to attribute preferences to others. This ability is intrinsic to game theory, and is a key component of Theory of Mind'' (ToM), perhaps the capstone of social cognition. We argue here that this component of theory of mind allows organisms to efficiently modify their behavior in strategic environments with a persistent element of novelty. In particular, we consider an evolutionary environment in which players interact with one another while the set of games that they might face becomes larger and larger with time. We then compare two types of agents --- a naive type that adapts to each particular game through repeated exposure to it --- as in reinforcement learning --- and a ToM type that knows his opponents have preferences and can infer these from observed behavior. We show that ToM yields a sharp and unambiguous advantage over naivete when novel games are introduced at an intermediate rate. The edge to ToM arises because a ToM type can acquire opponent preferences by observing behavior in previous games and can then use this knowledge to make the correct choice in novel circumstances, while the naive type requires direct exposure to each new game. In related experiments, we demonstrate that there is a highly significant tendency for subjects to learn preferences of opponents, rather than to learn the game. That is, we provide strong evidence for the presence of ToM in the sense of our model. Moreover, scores on standard measures of autism-spectrum behaviors are significant determinants of individual speed of learning, indicating that our notion of ToM is correlated with ToM as it is understood in psychology.

COGNITIVE EMPATHY IN CONFLICT SITUATIONS

AbstractTwo individuals are involved in a conflict situation in which preferences are ex ante uncertain. Although they eventually learn their own preferences, they have to pay a small cost if they want to secretly learn their opponent's preferences. We show that there is an interval with an upper bound less than 1 and lower bound greater than zero such that, for sufficiently small positive costs of information acquisition, in any Bayesian Nash equilibrium of the resulting game of incomplete information the probability of acquiring information about the opponent's preferences is within this interval.

On the Influence of Action Preference on Female Players' Gaze Behavior During Defense of Volleyball Attacks.

Knowledge of an opponent's action preference may affect visual anticipation of their action outcome. Specifically, if an opponent acts according to their purported preference, anticipation may be facilitated. Conversely, if an opponent does not act according to their purported preference, anticipation may be unaffected or even harmed. The underlying perceptual-cognitive mechanisms of that effect, however, remain unclear. Here we tested the hypothesis that players might change their gaze behavior once provided with preference information. To this end, 27 female volleyball players anticipated the direction of attacks in two test blocks with 40 videos each. Videos were shown on a large screen and stopped 240 ms prior to hand-ball-contact. Participants simulated defensive reaction while their gaze was recorded using a mobile eye-tracker. One female attacker directed 75% of shots diagonally (25% longline), while another female attacker distributed shots equally to both directions. After block one, half of the participants were informed that either both attackers preferred diagonal shots in 75% of occasions (group preferred) or that both attackers distributed shots equally across directions (group non-preferred). Analysis of decision behavior (i.e., proportion of diagonal decisions), but not prediction accuracy (i.e., proportion of correct predictions), revealed that those instructions led both groups decide differently according to the purported preferences from block 1 to block 2. Analysis of gaze behavior did not reveal group-specific effects across blocks or attackers with/-out action preference. Findings underline the influence of contextual information on anticipation, but they leave open whether the availability of contextual information similarly affects gaze behavior.

Efficient agent-based negotiation by predicting opponent preferences using AHP

Negotiation is a process essential for a wide range of applications. The complex decision making involved in negotiation makes its automation difficult. The complexity is further increased as negotiators hide their individual preferences from each other to avoid exploitation by the opponent. Even though sharing of private preference information leads to better agreement for both sides, it is never done in the absence of trust. In this work, we learn opponent’s preference information from the offers given by the opponent using Analytic Hierarchy Process (AHP). We apply our approach to the negotiation of Quality-of-Service (QoS) parameters for the establishment of Service Level Agreements (SLA) between a provider and a consumer. Experiments show that using AHP, the negotiations are faster and the agreements are on or nearer to the pareto-optimal line.

Electoral alliance with sworn opponent political party: factors impacting voting intention

ABSTRACTAlliance with sworn political opponents is becoming a growing phenomenon within the realm of political alliances. Here, two or more competitive political parties join hands to defeat a particular common opponent. During election, alliance partners face election together and distribute electoral seats mutually to avoid vote share. However, ensuring vote in favour of opponent alliance candidate is only possible when voters prefer to cast vote for the alliance candidate. Thus, this is crucial to know influencing factors that shape voters’ voting intention in favour of opponent alliance candidate. This study is an attempt to empirically investigate factors that have significant influence to shape voters’ voting intention for opponent alliance. The result shows that opponent alliance perceived fit impacts opponent alliance voting intention positively. Opponent alliance brand trust has a positive impact on opponent alliance voting intention. In addition, opponent alliance preference impacts opponent alliance voting intention positively.

A New Approach to the Analysis of Cooperation Under the Shadow of the Future: Theory and Experimental Evidence

The theory of infinitely repeated games lacks predictive power due to equilibrium multiplicity and its insensitivity to, for example, changes in some game parameters, the timing of players' moves and communication possibilities. We propose a new approach to mitigate the shortcomings of the theory. Specifically, we study a standard infinitely repeated prisoner's dilemma game and its variants with (i) heterogeneous preferences for cooperation and defection, and (ii) strategic risk arising from incomplete information about the opponent's preferences. Our model generates a rich set of comparative static predictions in a variety of settings that go beyond the standard prisoner's dilemma game. We show that, unlike the standard theory and other existing models, our approach organizes the findings of a host of experiments including our novel experiments.

Efficient agent-based negotiation by predicting opponent preferences using AHP

Negotiation is a process essential for a wide range of applications. The complex decision making involved in negotiation makes its automation difficult. The complexity is further increased as negotiators hide their individual preferences from each other to avoid exploitation by the opponent. Even though sharing of private preference information leads to better agreement for both sides, it is never done in the absence of trust. In this work, we learn opponent’s preference information from the offers given by the opponent using Analytic Hierarchy Process (AHP). We apply our approach to the negotiation of Quality-of-Service (QoS) parameters for the establishment of Service Level Agreements (SLA) between a provider and a consumer. Experiments show that using AHP, the negotiations are faster and the agreements are on or nearer to the pareto-optimal line.

POPPONENT: Highly accurate, individually and socially efficient opponent preference model in bilateral multi issue negotiations

In automated bilateral multi issue negotiations, two intelligent automated agents negotiate on behalf of their owners regarding 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 the applicability of the models in real scenarios. In this study, a less restricted technique where perceptron units (POPPONENT) are applied in modeling the preferences of the opponent is proposed. This model adopts the 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 this proposed model is individually and socially efficient. This proposed MBGD method could also be adopted in other practical areas of Artificial Intelligence.

Adaptive Probabilistic Behavioural Learning System for the effective behavioural decision in cloud trading negotiation market

In cloud e-commerce application, building an automated negotiation strategy by understanding the uncertain information of the opponent preferences, utilities, and tactics is highly challenging. The key issue is to analyse and predict the uncertain behaviour of the opponent tactics to suggest the appropriate counter tactics that can reach maximum consensus. To handle such uncertain information, negotiation strategies follow several tactics with and without learning ability. Strategies without learning ability are restricted to negotiate with the opponent having only deterministic behaviour. To overcome this problem most researchers exploited the negotiation strategies with fixed learning ability using Bayesian learning, neural network learning, and genetic tactics. These tactics can learn the opponent’s behaviour and cannot guarantee to generate suitable counter-offer for all offers submitted by the opponent cloud service provider. This limitation motivates to propose a novel Adaptive Probabilistic Behavioural Learning System for managing the opponent having unpredictable random behaviours. The proposed Adaptive Probabilistic Behavioural Learning System contains a Behavioural Inference Engine to analyse the sequence of negotiation offer received by the broker for effectively learning the opponent’s behaviour over several stages of negotiation process. It also formulates the multi-stage Markov decision problem to suggest the broker with appropriate counter-offer behavioural tactics generation based on the adaptive probabilistic decision taken over the corresponding negotiation stage. Therefore, this research work can outperform the existing fixed behavioural learning tactics and hence maximize the utility value and success rate of negotiating parties without any break-off.

Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques

A negotiation between agents is typically an incomplete information game, where the agents initially do not know their opponent's preferences or strategy. This poses a challenge, as efficient and effective negotiation requires the bidding agent to take the other's wishes and future behavior into account when deciding on a proposal. Therefore, in order to reach better and earlier agreements, an agent can apply learning techniques to construct a model of the opponent. There is a mature body of research in negotiation that focuses on modeling the opponent, but there exists no recent survey of commonly used opponent modeling techniques. This work aims to advance and integrate knowledge of the field by providing a comprehensive survey of currently existing opponent models in a bilateral negotiation setting. We discuss all possible ways opponent modeling has been used to benefit agents so far, and we introduce a taxonomy of currently existing opponent models based on their underlying learning techniques. We also present techniques to measure the success of opponent models and provide guidelines for deciding on the appropriate performance measures for every opponent model type in our taxonomy.

Development of recursive decision making model in bilateral construction procurement negotiation

Abstract Price negotiation in construction procurement is a form of decision making where contractor and supplier jointly search for a mutually agreed solution. In price negotiation, with information available about the agent's preferences, a negotiation may result in a mutually beneficial agreement. However, self-interested agents may not be willing to reveal their preferences, and this can increase the difficulty of negotiating a beneficial agreement. In order to overcome this problem, this paper proposes a Bayesian-based approach which can help an agent to predict its opponent's preference in bilateral negotiation. The proposed approach employs Bayesian theory to analyze the opponent's historical offers and to approximately predict the opponent's preference over negotiation issue. A Nash equilibrium algorithm is also integrated into the prediction approach to help agents on how to propose beneficial offers based on the prediction results. Validation results indicate good performance of the proposed approach in terms of utility gain and negotiation efficiency.

Action preferences and the anticipation of action outcomes

Skilled performers of time-constrained motor actions acquire information about the action preferences of their opponents in an effort to better anticipate the outcome of that opponent's actions. However, there is reason to doubt that knowledge of an opponent's action preferences would unequivocally influence anticipatory responses in a positive way. It is possible that overt information about an opponent's actions could distract skilled performers from using the advance kinematic information they would usually rely on to anticipate actions, particularly when the opponent performs an ‘unexpected’ action that is not in accordance with his or her previous behaviour. The aim of this study was to examine how the ability to anticipate the outcome of an opponent's actions can be influenced by exposure to the action preferences of that opponent. Two groups of skilled handball goalkeepers anticipated the direction of penalty throws performed by opponents before and after a training intervention that provided situational probability information in the form of action preferences (AP). During the training phase participants in an AP-training group anticipated the action outcomes of two throwers who had a strong preference to throw in one particular direction, whilst participants in a NP-training group viewed players who threw equally to all directions. Exposure to opponents who did have an action preference during the training phase resulted in improved anticipatory performance if the opponent continued to bias their throws towards their preferred direction, but decreased performance if the opponent did not. These findings highlight that skilled observers use information about action preferences to enhance their anticipatory ability, but that doing so can be disadvantageous when the outcomes are no longer consistent with their generated expectations.

Tri-Training based Bilateral Multi-Issue Negotiation Framework

Negotiation has been extensively discussed in electronic commerce for decades. Recent growing interest in importing machine learning algorithm in electronic commerce has given increased importance to automated negotiation. A Tri-Training based algorithm was proposed to learn opponent's negotiation preference. The process of negotiation was viewed as a proposal's sequence which can be mapped into bidding trajectory feature space to form sample set. Due to fierce competition, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore, Tri-Training, as a semi- supervised method, was imported into negotiation framework to increase the number of samples and improve perdition accuracy of opponent's negotiation preference learning. Based on negotiation preference of both side, an optimization algorithm is conducted to compute win-win counter proposal. The experimental results show that the proposed method can decrease the number of negotiation steps and increase the overall utility of negotiation.

Modeling Opponent Actions for Table-Tennis Playing Robot

Opponent modeling is a critical mechanism in repeated games. It allows a player to adapt its strategy in order to better respond to the presumed preferences of its opponents. We introduce a modeling technique that adaptively balances safety and exploitability. The opponent's strategy is modeled with a set of possible strategies that contains the actual one with high probability. The algorithm is safe as the expected payoff is above the minimax payoff with high probability, and can exploit the opponent's preferences when sufficient observations are obtained. We apply the algorithm to a robot table-tennis setting where the robot player learns to prepare to return a served ball. By modeling the human players, the robot chooses a forehand, backhand or middle preparation pose before they serve. The learned strategies can exploit the opponent's preferences, leading to a higher rate of successful returns.

Pareto-optimal Algorithm in Bilateral Automated Negotiation

In this paper we present a Pareto-optimal algorithm in bilateral automated negotiation where the negotiation is modeled by split the pie game and alternating-offer protocol. Pareto-optimality is the seminal condition in the bargaining problem which leads autonomous agents to the Nash-equilibrium. Generating Pareto-optimal offer in multi-issue bargaining is a computationally complex problem, specially, when autonomous agents have incomplete information about deadline, outside options and the opponent's preferences. Unfortunately, yet to date, there is no articulation that clearly describes an algorithm to generate offer in multi-issue negotiation with perfect information. To this end, we present the maximum greedy trade-offs (MGT) algorithm that generate offers at any aspiration-level in O(n) with assuming that the order of greedy choices is given, otherwise the complexity will be O(n bg n). We also provide analytical proof for the correctness of the maximum greedy trade-offs algorithm.

The Learning of an Opponent's Approximate Preferences in Bilateral Automated Negotiation

Autonomous agents can negotiate on behalf of buyers and sellers to make a contract in the e-marketplace. In bilateral negotiation, they need to find a joint agreement by satisfying each other. That is, an agent should learn its opponent's preferences. However, the agent has limited time to find an agreement while trying to protect its payoffs by keeping its preferences private. In doing so, generating offers with incomplete information about the opponent's preferences is a complex process and, therefore, learning these preferences in a short time can assist the agent to generate proper offers. In this paper, we have developed an incremental on-line learning approach by using a hybrid soft-computing technique to learn the opponent's preferences. In our learning approach, first, the size of possible preferences is reduced by encoding the uncertain preferences into a series of fuzzy membership functions. Then, a simplified genetic algorithm is used to search the best fuzzy preferences that articulate the opponent's intention. Experimental results showed that our learning approach can estimate the opponent's preferences effectively. Moreover, results indicate that agents which use the proposed learning approach not only have more chances to reach agreements but also will be able to find agreements with greater joint utility.