STRATUM: A METHODOLOGY FOR DESIGNING HEURISTIC AGENT NEGOTIATION STRATEGIES

Automated negotiation is a powerful (and sometimes essential) means for allocating resources among self-interested autonomous software agents. A key problem in building negotiating agents is the design of the negotiation strategy, which is used by an agent to decide its negotiation behavior. In complex domains, there is no single, obvious optimal strategy. This has led to much work on designing heuristic strategies, where agent designers usually rely on intuition and experience. In this article, we introduce STRATUM, a methodology for designing strategies for negotiating agents. The methodology provides a disciplined approach to analyzing the negotiation environment and designing strategies in light of agent capabilities and acts as a bridge between theoretical studies of automated negotiation and the software engineering of negotiation applications. We illustrate the application of the methodology by characterizing some strategies for the Trading Agent Competition and for argumentation-based negotiation.     

基于拍卖博弈的电子商务自动协商

为提高电子商务自动协商系统效率,本文以拍卖博弈理论为基础,提出并实现了一种基于拍卖博弈的自动协商Agent模型,并在此基础上实现了一个基于拍卖博弈的电子商务自动协商原型系统,应用于一个企业敏捷供应链管理系统中实现自动协商交易。     

A protocol for multi-agent negotiation in a group-choice decision making process

Negotiation is one of the most important features of agent interactions found in multi-agent systems, because it provides the basis for managing the expectations of the individual negotiating agents, and it enables selecting solutions that satisfy all the agents as much as possible. In order for negotiation to take place between two or more agents there is need for a negotiation protocol that defines the rules of the game; consequently, a variety of agent negotiation protocols have been proposed in literature. However, most of them are inappropriate for Group-Choice Decision Making (GCDM) because they do not explicitly exploit tradeoff to achieve social optimality, and their main focus is solving two-agent negotiation problems such as buyer–seller negotiation. In this paper we present an agent negotiation protocol that facilitates the solving of GCDM problems. The protocol is based on a hybrid of analytic and artificial intelligence techniques. The analytic component of the protocol utilizes a Game Theory model of an n-person general-sum game with complete information to determine the agreement options, while the knowledge-based (artificial intelligence) component of the protocol is similar to the strategic negotiation protocol. Moreover, this paper presents a tradeoff algorithm based on Qualitative Reasoning, which the agents employ to determine the ‘amount’ of tradeoff associated with various agreement options. Finally, the paper presents simulation results that illustrate the operational effectiveness of our agent negotiation protocol.     

Non-linear protocols for optimal distributed consensus in networks of dynamic agents

We consider stationary consensus protocols for networks of dynamic agents with fixed topologies. At each time instant, each agent knows only its and its neighbors’ state, but must reach consensus on a group decision value that is function of all the agents’ initial state. We show that the agents can reach consensus if the value of such a function is time-invariant when computed over the agents’ state trajectories. We use this basic result to introduce a non-linear protocol design rule allowing consensus on a quite general set of values. Such a set includes, e.g., any generalized mean of order p of the agents’ initial states. As a second contribution we show that our protocol design is the solution of individual optimizations performed by the agents. This notion suggests a game theoretic interpretation of consensus problems as mechanism design problems. Under this perspective a supervisor entails the agents to reach a consensus by imposing individual objectives. We prove that such objectives can be chosen so that rational agents have a unique optimal protocol, and asymptotically reach consensus on a desired group decision value. We use a Lyapunov approach to prove that the asymptotical consensus can be reached when the communication links between nearby agents define a time-invariant undirected network. Finally we perform a simulation study concerning the vertical alignment maneuver of a team of unmanned air vehicles.     

Negotiating Dialogue Games

Recently in the field of agent communication, many authors have adopted the view of interaction as a joint activity regulated by means of dialogue games. It is argued in particular that this approach should increase the flexibility of dialogues by allowing a variety of game compositions. In this research note, we present a framework suited to this feature. A preliminary attempt to capture the negotiation phase (which allows agents to agree upon the dialogue game currently regulating their conversation) is discussed.     

Axiomatic characterization of belief merging by negotiation

Belief merging has been an active research field with many important applications. The major approaches for the belief merging problems, considered as arbitration processes, are based on the construction of the total pre-orders of alternatives using distance functions and aggregation functions. However, these approaches require that all belief bases are provided explicitly and the role of agents, who provide the belief bases, are not adequately considered. Therefore, the results are merely ideal and difficult to apply in the multi-agent systems. In this paper, we approach the merging problems from other point of view. Namely, we treat a belief merging problem as a game, in which rational agents participate in a negotiation process to find out a jointly consistent consensus trying to preserve as many important original beliefs as possible. To this end, a model of negotiation for belief merging is presented, a set of rational and intuitive postulates to characterize the belief merging operators are proposed, and a representation theorem is presented.     

A negotiation framework for linked combinatorial optimization problems

We tackle the challenge of applying automated negotiation to self-interested agents with local but linked combinatorial optimization problems. Using a distributed production scheduling problem, we propose two negotiation strategies for making concessions in a joint search space of agreements. In the first strategy, building on Lai and Sycara (Group Decis Negot 18(2):169–187, 2009), an agent concedes on local utility in order to achieve an agreement. In the second strategy, an agent concedes on the distance in an attribute space while maximizing its local utility. Lastly, we introduce a Pareto improvement phase to bring the final agreement closer to the Pareto frontier. Experimental results show that the new attribute-space negotiation strategy outperforms its utility-based counterpart on the quality of the agreements and the Pareto improvement phase is effective in approaching the Pareto frontier. This article presents the first study of applying automated negotiation to self-interested agents each with a local, but linked, combinatorial optimization problem.     

Agents that Learn How to Generate Arguments from Other Agents

Learning how to argue is a key ability for a negotiator agent. In this paper, we propose an approach that allows agents to learn how to build arguments by observing how other agents argue in a negotiation context. Particularly, our approach enables the agent to infer the rules for argument generation that other agents apply to build their arguments. To carry out this goal, the agent stores the arguments uttered by other agents and the facts of the negotiation context where each argument is uttered. Then, an algorithm for fuzzy generalized association rules is applied to discover the desired rules. This kind of algorithm allows us (a) to obtain general rules that can be applied to different negotiation contexts; and (b) to deal with the uncertainty about the knowledge of what facts of the context are taken into account by the agents. The experimental results showed that it is possible to infer argument generation rules from a reduced number of observed arguments.     

On decentralized negotiation of optimal consensus

A consensus problem consists of finding a distributed control strategy that brings the state or output of a group of agents to a common value, a consensus point. In this paper, we propose a negotiation algorithm that computes an optimal consensus point for agents modeled as linear control systems subject to convex input constraints and linear state constraints. By primal decomposition and incremental subgradient methods, it is shown that the algorithm can be implemented such that each agent exchanges only a small amount of information per iteration with its neighbors.     

Bargaining with incomplete information

This paper analyses the process and outcomes of competitive bilateral negotiation for a model based on negotiation decision functions. Each agent has time constraints in the form of a deadline and a discounting factor. The importance of information possessed by participants is highlighted by exploring all possible incomplete information scenarios – both symmetric and asymmetric. In particular, we examine a range of negotiation scenarios in which the amount of information that agents have about their opponent’s parameters is systematically varied. For each scenario, we determine the equilibrium solution and study its properties. The main results of our study are as follows. Firstly, in some scenarios agreement takes place at the earlier deadline, while in others it takes place near the beginning of negotiation. Secondly, in some scenarios the price surplus is split equally between the agents while in others the entire price surplus goes to a single agent. Thirdly, for each possible scenario, the equilibrium outcome possesses the properties of uniqueness and symmetry – although it is not always Pareto optimal. Finally, we also show the relative impacts of the opponent’s parameters on the bargaining outcome.     

基于时间约束的不完全信息的战略协商模型

在实证的一对一协商中，协商Agent不仅要面临自己的最后期限的压力，同时又要预测协商对手的最后期限和其类型，协商Agent的协商战略必须满足理性与均衡的要求。提出了通过形式化的方法建立轮流出价协商模型，给出了轮流出价协商战略均衡的条件定义，求出了基于时间限制的不完全信息环境下满足均衡组合的协商战略，建立了依据均衡战略的实用化协商算法，最后分析了该算法产生的实验数据，并在相同环境下与Zeus协商模型比较显示，依从本模型的均衡战略的协商Agent能根据对对手的不确定信息的信念动态地采取行动，以获得最大的期望收益。     

Compromising Adjustment Strategy Based on TKI Conflict Mode for Multi‐Times Bilateral Closed Negotiations

Bilateral multi‐issue closed negotiation is an important class for real‐life negotiations. Usually, negotiation problems have constraints such as a complex and unknown opponent's utility in real time, or time discounting. In the class of negotiation with some constraints, the effective automated negotiation agents can adjust their behavior depending on the characteristics of their opponents and negotiation scenarios. Recently, the attention of this study has focused on the interleaving learning with negotiation strategies from the past negotiation sessions. By analyzing the past negotiation sessions, agents can estimate the opponent's utility function based on exchanging bids. In this article, we propose a negotiation strategy that estimates the opponent's strategies based on the past negotiation sessions. Our agent tries to compromise to the estimated maximum utility of the opponent by the end of the negotiation. In addition, our agent can adjust the speed of compromise by judging the opponent's Thomas–Kilmann conflict mode and search for the Pareto frontier using past negotiation sessions. In the experiments, we demonstrate that the proposed agent has better outcomes and greater search technique for the Pareto frontier than existing agents in the linear and nonlinear utility functions.     

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.     

Agent negotiation of target distribution enhancing system survivability

This article proposes an agent negotiation model for target distribution across a set of geographically dispersed sensors. The key idea is to consider sensors as autonomous agents that negotiate over the division of tasks among them for obtaining better payoffs. The negotiation strategies for agents are established based upon the concept of subgame perfect equilibrium from game theory. Using such negotiation leads to not only superior measuring performance from a global perspective but also possibly balanced allocations of tasks to sensors, benefiting system robustness and survivability. A simulation test and results are given to demonstrate the ability of our approach in improving system security while keeping overall measuring performance near optimal. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1251–1269, 2007.     

Bargaining on an Internet Agent-based Market: Behavioral vs. Optimizing Agents

In this paper, we analyze an Internet agent-based market where non-cooperative agents using behavioral rules negotiate the price of a given product in a bilateral and sequential manner. In this setting, we study the optimal decision-making process of a buying agent that enters the market. Our approach is based on Negotiation Analysis (Raiffa, 1982; Sebenuis, 1992) and we consider that the optimizing buying agent maximizes her discounted expected utility using subjective probabilities. The optimal decision-making process of the buying agent is treated as a stochastic control problem that can be solved by dynamic programming. Three types of behavioral agents are studied, namely conceder agents, boulware agents and imitative agents. A set of simulations is undertaken in order to predict the average outcome in a negotiation process for different parameters of the optimizing buying agent and for the three possible selling agents' behaviors. Finally, we compare the performance of the optimizing agent with that of behavioral buying agents.     

Learning in multi-agent systems: a case study of construction claims negotiation

The ability of agents to learn is of growing importance in multi-agent systems. It is considered essential to improve the quality of peer to peer negotiation in these systems. This paper reviews various aspects of agent learning, and presents the particular learning approach—Bayesian learning—adopted in the MASCOT system (multi-agent system for construction claims negotiation). The core objective of the MASCOT system is to facilitate construction claims negotiation among different project participants. Agent learning is an integral part of the negotiation mechanism. The paper demonstrates that the ability to learn greatly enhances agents' negotiation power, and speeds up the rate of convergence between agents. In this case, learning is essential for the success of peer to peer agent negotiation systems.     

Argumentation-based negotiation planning for autonomous agents

When we negotiate, the arguments uttered to persuade the opponent are not the result of an isolated analysis, but of an integral view of the problem that we want to agree about. Before the negotiation starts, we have in mind what arguments we can utter, what opponent we can persuade, which negotiation can finish successfully and which cannot. Thus, we plan the negotiation, and in particular, the argumentation. This fact allows us to take decisions in advance and to start the negotiation more confidently. With this in mind, we claim that this planning can be exploited by an autonomous agent. Agents plan the actions that they should execute to achieve their goals. In these plans, some actions are under the agent's control, while some others are not. The latter must be negotiated with other agents. Negotiation is usually carried out during the plan execution. In our opinion, however, negotiation can be considered during the planning stage, as in real life. In this paper, we present a novel approach to integrate argumentation-based negotiation planning into the general planning process of an autonomous agent. This integration allows the agent to take key decisions in advance. We evaluated this proposal in a multiagent scenario by comparing the performance of agents that plan the argumentation and agents that do not. These evaluations demonstrated that performance improves when the argumentation is planned, specially, when the negotiation alternatives increase.     

Consensus analysis of hybrid multiagent systems: A game‐theoretic approach

This paper considers a consensus problem for hybrid multiagent systems, which comprise two groups of agents: a group of continuous‐time dynamic agents and a group of discrete‐time dynamic agents. Firstly, a game‐theoretic approach is adopted to model the interactions between the two groups of agents. To achieve consensus for the considered hybrid multiagent systems, the cost functions are designed. Moreover, it is shown that the designed game admits a unique Nash equilibrium. Secondly, sufficient/necessary conditions of solving consensus are established. Thirdly, we find that the convergence speed of the system depends on the game. By the mechanism design of the game, the convergence speed is increased. Finally, simulation examples are given to validate the effectiveness of the theoretical results.     

资源分配问题的协商模型

研究Agent多次协商过程中的策略调整问题,目的是使得Agent在协商过程中具有自学能力,对环境和协商对手更敏感。结合资源分配问题,讨论Agent协商过程中的学习问题,基于博弈论分别分析了单次协商和多次协商模型,给出了协商过程中在不同信息条件下遵循的策略,并进行了证明。     

一种基于多目标优化的Agent多议题协商模型

Agent多议题协商研究是多Agent合作求解的核心内容之一,一般基于对策论的方法实现Pareto最优的协商结果。由于很多学者将其转化为单目标约束满足问题,因而只能满足一方的效用最大化要求。Nash指出在理想情况下Agent应追求自身效用最大和对手效用最大的多目标优化,以达到快速达成一致并能最优化自身效用的目的。针对该问题,本文给出一种用指数型功效系数法求解的一揽子交易多议题协商模型NMMOP,该模型能够实现双方Agent的效用最优,提高协商双方的总效用。实验结果验证了该模型的优化效率优于Fatima和Faratin等人的工作。