Dmytro Tykhonov

GENIUS: AN INTEGRATED ENVIRONMENT FOR SUPPORTING THE DESIGN OF GENERIC AUTOMATED NEGOTIATORS

The design of automated negotiators has been the focus of abundant research in recent years. However, due to difficulties involved in creating generalized agents that can negotiate in several domains and against human counterparts, many automated negotiators are domain specific and their behavior cannot be generalized for other domains. Some of these difficulties arise from the differences inherent within the domains, the need to understand and learn negotiators’ diverse preferences concerning issues of the domain, and the different strategies negotiators can undertake. In this paper we present a system that enables alleviation of the difficulties in the design process of general automated negotiators termed Genius, a General Environment for Negotiation with Intelligent multi‐purpose Usage Simulation. With the constant introduction of new domains, e‐commerce and other applications, which require automated negotiations, generic automated negotiators encompass many benefits and advantages over agents that are designed for a specific domain. Based on experiments conducted with automated agents designed by human subjects using Genius we provide both quantitative and qualitative results to illustrate its efficacy. Finally, we also analyze a recent automated bilateral negotiators competition that was based on Genius. Our results show the advantages and underlying benefits of using Genius and how it can facilitate the design of general automated negotiators.

Eliminating interdependencies between issues for multi-issue negotiation

In multi-issue negotiations, issues may be negotiated independently or not. In the latter case, the utility associated with one issue depends on the value of another. These issue dependencies give rise to more complex, non-linear utility spaces. As a consequence, the computational cost and complexity of negotiating interdependent issues is increased significantly compared to the case of independent issues. Several techniques have been proposed to deal with this increased complexity, including, for example, introducing a mediator in the negotiation setting. In this paper, we propose an alternative approach based on a weighted approximation technique to simplify the utility space. We show that given certain natural assumptions about the outcome of negotiation the application of this technique results in an outcome that closely matches with the outcome based on the original, interdependent utility structure. Moreover, using the approximated utility structure, each of the issues can be negotiated independently which ensures that the negotiation is computationally tractable. The approach is illustrated by applying and testing it in a case study.

Towards a Quality Assessment Method for Learning Preference Profiles in Negotiation

In automated negotiation, information gained about an opponent’s preference profile by means of learning techniques may significantly improve an agent’s negotiation performance. It therefore is useful to gain a better understanding of how various negotiation factors influence the quality of learning. The quality of learning techniques in negotiation are typically assessed indirectly by means of comparing the utility levels of agreed outcomes and other more global negotiation parameters. An evaluation of learning based on such general criteria, however, does not provide any insight into the influence of various aspects of negotiation on the quality of the learned model itself. The quality may depend on such aspects as the domain of negotiation, the structure of the preference profiles, the negotiation strategies used by the parties, and others. To gain a better understanding of the performance of proposed learning techniques in the context of negotiation and to be able to assess the potential to improve the performance of such techniques a more systematic assessment method is needed. In this paper we propose such a systematic method to analyse the quality of the information gained about opponent preferences by learning in single-instance negotiations. The method includes measures to assess the quality of a learned preference profile and proposes an experimental setup to analyse the influence of various negotiation aspects on the quality of learning. We apply the method to a Bayesian learning approach for learning an opponent’s preference profile and discuss our findings.

Supporting the Design of General Automated Negotiators

The design of automated negotiators has been the focus of abundant research in recent years. However, due to difficulties involved in creating generalized agents that can negotiate in several domains and against human counterparts, many automated negotiators are domain specific and their behavior cannot be generalized for other domains. Some of these difficulties arise from the differences inherent within the domains, the need to understand and learn negotiators’ diverse preferences concerning issues of the domain and the different strategies negotiators can undertake. In this paper we present a system that enables alleviation of the difficulties in the design process of general automated negotiators termed Genius, a General Environment for Negotiation with Intelligent multi-purpose Usage Simulation. With the constant introduction of new domains, e-commerce and other applications, which require automated negotiations, generic automated negotiators encompass many benefits and advantages over agents that are designed for a specific domain. Based on experiments conducted with automated agents designed by human subjects using Genius we provide both quantitative and qualitative results to illustrate its efficacy. Our results show the advantages and underlying benefits of using Genius for designing general automated negotiators.

Negotiation Dynamics: Analysis, Concession Tactics, and Outcomes

Given that a negotiation outcome is determined to a large extent by the successive offers exchanged by negotiating agents, it is useful to analyze dynamic patterns of the bidding, what Raiffa calls the negotiation dance. Patterns in such exchanges may provide additional insight into the strategies used by the agents. The current practice of evaluating a negotiation strategy, however, is to primarily focus on fairness and quality aspects of the agreement. There is a lack of tools and methods that facilitate a precise analysis of the negotiation dynamics. To fill this gap, this paper introduces a method for analysis based on a classification of negotiation steps. The method provides the basic tools to perform a detailed and quantified analysis of a negotiation between two agents in terms of dynamic properties of the negotiation trace. The method can be applied to well-designed tournaments, but can also be used to analyze single 1-on-l negotiation. Example findings of applying the method to analyze the ABMP and Trade-Off strategies show that sensitivity to the preferences of the opponent is independent, respectively dependent, on a correct model of that opponent. Furthermore, the results illustrate that having domain knowledge is not always enough to avoid making unintentional steps.

Towards an Open Negotiation Architecture for Heterogeneous Agents

This paper presents the design of an open architecture for heterogeneous negotiating agents. Both the system level architecture as well as the architecture for negotiating agents are provided. The main contribution of this paper is that it derives a precisely specified interface from these architectures that facilitates an easy integration of heterogeneous agents into the overall negotiation framework. The interface is defined as a set of adapters that allows for various levels of integration of agents into the system architecture. The functionality provided by the system architecture depends on the number of adapters that are implemented and used to connect an agent to this architecture, ranging from functionality to conduct a bilateral negotiation to functionality for computing agent internal performance measures such as the quality of an opponent model. The architecture is used as the basis of a competitive testbed which allows us to study various negotiating agents. The design yields a flexible negotiation framework that facilitates negotiating different domains potentially using different protocols whereas no details of the internal negotiating agent structure are enforced. An application of the framework is illustrated by integrating two agents from the literature.

Analysis of Negotiation Dynamics

The process of reaching an agreement in a bilateral negotiation to a large extent determines that agreement. The tactics of proposing an offer and the perception of offers made by the other party determine how both parties engage each other and, as a consequence, the kind of agreement they will establish. It thus is important to gain a better understanding of the tactics and potential other factors that play a role in shaping that process. A negotiation, however, is typically judged by the efficiency of the outcome. The process of reaching an outcome has received less attention in literature and the analysis of the negotiation process is typically not as rigorous nor is it based on formal tools. Here we present an outline of a formal toolbox to analyze and study the dynamics of negotiation based on an analysis of the types of moves parties to a negotiation can make while exchanging offers. This toolbox can be used to study both the performance of human negotiators as well as automated negotiation systems.

The Benefits of Opponent Models in Negotiation

Information about the opponent is essential to improve automated negotiation strategies for bilateral multi-issue negotiation. In this paper we propose a negotiation strategy that exploits a technique to learn a model of opponent preferences in a single negotiation session. An opponent model may be used to achieve at least two important goals in negotiation. First, it can be used to recognize, avoid and respond appropriately to exploitation, which differentiates the strategy proposed from commonly used concession-based strategies. Second, it can be used to increase the efficiency of a negotiated agreement by searching for Pareto-optimal bids. A negotiation strategy should be efficient, transparent, maximize the chance of an agreement and should avoid exploitation. We argue that the proposed strategy satisfies these criteria and analyze its performance experimentally.

Let's dans! An analytic framework of negotiation dynamics and strategies

The “negotiation dance”, as Raiffa calls the dynamic pattern of the bidding, has an important influence on the outcome of the negotiation. The current practice of evaluating a negotiation strategy is to focus on fairness and quality aspects of the agreement. In this article we present the framework DANS (Dynamics Analysis of Negotiation Strategies) for the analysis of the dynamic patterns of the bidding as a means to evaluate the strengths and weaknesses of negotiation strategies for bidding. The method provides the tools to perform a detailed and quantified analysis of a negotiation between two agents in terms of dynamic properties of the negotiation trace. The classification of negotiation steps in the dance plays a central role in the analysis. The method can be applied to tournaments, but can also be used to analyze single 1-on-1 negotiation sessions. The sessions can be played by humans or by software agents. Using DANS we show that some strategies are sensitive to the bidding behaviour of the opponent, and some depend on a correct model of the opponent. DANS helped us in discovering that domain characteristics are important for the analysis of strategies. Some strategies rely heavily on some domain assumptions. Furthermore, the results illustrate that having domain knowledge is not always enough to avoid making unintentional steps. The method is demonstrated in the analysis of three strategies from the literature ABMP, Trade-Off and Bayesian Agent.

Formal Analysis of Trust Dynamics in Human and Software Agent Experiments

Recognizing that trust states are mental states, this paper presents a formal analysis of the dynamics of trust in terms of the functional roles and representation relations for trust states. This formal analysis is done both in a logical framework and in a mathematical framework based on integral and differential equations. Furthermore, the paper presents formal specifications of a number of relevant dynamic properties of trust. The specifications provided were used to perform automated formal analysis of empirical and simulated data from two case studies, one involving two experiments with humans, and one involving simulation experiments in the context of an economic game.