Addressing stability issues in mediated complex contract negotiations for constraint-based, non-monotonic utility spaces

Negotiating contracts with multiple interdependent issues may yield non- monotonic, highly uncorrelated preference spaces for the participating agents. These scenarios are specially challenging because the complexity of the agents’ utility functions makes traditional negotiation mechanisms not applicable. There is a number of recent research lines addressing complex negotiations in uncorrelated utility spaces. However, most of them focus on overcoming the problems imposed by the complexity of the scenario, without analyzing the potential consequences of the strategic behavior of the negotiating agents in the models they propose. Analyzing the dynamics of the negotiation process when agents with different strategies interact is necessary to apply these models to real, competitive environments. Specially problematic are high price of anarchy situations, which imply that individual rationality drives the agents towards strategies which yield low individual and social welfares. In scenarios involving highly uncorrelated utility spaces, “low social welfare” usually means that the negotiations fail, and therefore high price of anarchy situations should be avoided in the negotiation mechanisms. In our previous work, we proposed an auction-based negotiation model designed for negotiations about complex contracts when highly uncorrelated, constraint-based utility spaces are involved. This paper performs a strategy analysis of this model, revealing that the approach raises stability concerns, leading to situations with a high (or even infinite) price of anarchy. In addition, a set of techniques to solve this problem are proposed, and an experimental evaluation is performed to validate the adequacy of the proposed approaches to improve the strategic stability of the negotiation process. Finally, incentive-compatibility of the model is studied.     

Trust-/spl Xscr/;: a peer-to-peer framework for trust establishment

We present Trust-/spl Xscr/;, a comprehensive XML-based framework for trust negotiations, specifically conceived for a peer-to-peer environment. Trust negotiation is a promising approach for establishing trust in open systems like the Internet, where sensitive interactions may often occur between entities at first contact, with no prior knowledge of each other. The framework we propose takes into account all aspects related to negotiations, from the specification of the profiles and policies of the involved parties to the selection of the best strategy to succeed in the negotiation. Trust-/spl Xscr/; presents a number of innovative features, such as the support for protection of sensitive policies, the use of trust tickets to speed up the negotiation, and the support of different strategies to carry on a negotiation. In this paper, besides presenting the language to encode security information, we present the system architecture and algorithms according to which negotiations take place.     

A self-protecting and self-healing framework for negotiating services and trust in autonomic communication systems

In a federation of heterogeneous nodes that organize themselves, the lack of a trusted third party does not allow establishing a priori trust relationships among strangers. Automated trust negotiation (TN) is a promising approach to establish sufficient trust among parties, allowing them to access sensitive data and services in open environments. Although the literature on TN is growing, two key issues have still to be addressed. The first one concerns a typical feature of real-life negotiations: we are usually willing to trade the disclosure of personal attributes in exchange for additional services and only in a particular order (according to our preferences). The second one concerns dependability. By their nature TN systems are used in unreliable contexts where it is important not only to protect negotiations against malicious attack (self-protection), but also against accidental failures (self-healing). In this paper we address these issues proposing a novel dependable negotiation framework where services, needed credentials, and behavioral constraints on the disclosure of privileges are bundled together.     

Strategic agents for multi-resource negotiation

In electronic commerce markets where selfish agents behave individually, agents often have to acquire multiple resources in order to accomplish a high level task with each resource acquisition requiring negotiations with multiple resource providers. Thus, it is crucial to efficiently coordinate these interrelated negotiations. This paper presents the design and implementation of agents that concurrently negotiate with other entities for acquiring multiple resources. Negotiation agents in this paper are designed to adjust (1) the number of tentative agreements for each resource and (2) the amount of concession they are willing to make in response to changing market conditions and negotiation situations. In our approach, agents utilize a time-dependent negotiation strategy in which the reserve price of each resource is dynamically determined by (1) the likelihood that negotiation will not be successfully completed (conflict probability), (2) the expected agreement price of the resource, and (3) the expected number of final agreements. The negotiation deadline of each resource is determined by its relative scarcity. Agents are permitted to decommit from agreements by paying a time-dependent penalty, and a buyer can make more than one tentative agreement for each resource. The maximum number of tentative agreements for each resource made by an agent is constrained by the market situation. Experimental results show that our negotiation strategy achieved significantly more utilities than simpler strategies.     

An augmented EDA with dynamic diversity control and local neighborhood search for coevolution of optimal negotiation strategies

In this paper, we present an estimation of distribution algorithm (EDA) augmented with enhanced dynamic diversity controlling and local improvement methods to solve competitive coevolution problems for agent-based automated negotiations. Since optimal negotiation strategies ensure that interacting agents negotiate optimally, finding such strategies—particularly, for the agents having incomplete information about their opponents—is an important and challenging issue to support agent-based automated negotiation systems. To address this issue, we consider the problem of finding optimal negotiation strategies for a bilateral negotiation between self-interested agents with incomplete information through an EDA-based coevolution mechanism. Due to the competitive nature of the agents, EDAs should be able to deal with competitive coevolution based on two asymmetric populations each consisting of self-interested agents. However, finding optimal negotiation solutions via coevolutionary learning using conventional EDAs is difficult because the EDAs suffer from premature convergence and their search capability deteriorates during coevolution. To solve these problems, even though we have previously devised the dynamic diversity controlling EDA (D²C-EDA), which is mainly characterized by a diversification and refinement (DR) procedure, D²C-EDA suffers from the population reinitialization problem that leads to a computational overhead. To reduce the computational overhead and to achieve further improvements in terms of solution accuracy, we have devised an improved D²C-EDA (ID²C-EDA) by adopting an enhanced DR procedure and a local neighborhood search (LNS) method. Favorable empirical results support the effectiveness of the proposed ID²C-EDA compared to conventional and the other proposed EDAs. Furthermore, ID²C-EDA finds solutions very close to the optimum.     

A novel method for coevolving PS-optimizing negotiation strategies using improved diversity controlling EDAs

In agent-mediated negotiation systems, the majority of the research focused on finding negotiation strategies for optimizing price only. However, in negotiation systems with time constraints (e.g., resource negotiations for Grid and Cloud computing), it is crucial to optimize either or both price and negotiation speed based on preferences of participants for improving efficiency and increasing utilization. To this end, this work presents the design and implementation of negotiation agents that can optimize both price and negotiation speed (for the given preference settings of these parameters) under a negotiation setting of complete information. Then, to support negotiations with incomplete information, this work deals with the problem of finding effective negotiation strategies of agents by using coevolutionary learning, which results in optimal negotiation outcomes. In the coevolutionary learning method used here, two types of estimation of distribution algorithms (EDAs) such as conventional EDAs (S-EDAs) and novel improved dynamic diversity controlling EDAs (ID²C-EDAs) were adopted for comparative studies. A series of experiments were conducted to evaluate the performance for coevolving effective negotiation strategies using the EDAs. In the experiments, each agent adopts three representative preference criteria: (1) placing more emphasis on optimizing more price, (2) placing equal emphasis on optimizing exact price and speed and (3) placing more emphasis on optimizing more speed. Experimental results demonstrate the effectiveness of the coevolutionary learning adopting ID²C-EDAs because it generally coevolved effective converged negotiation strategies (close to the optimum) while the coevolutionary learning adopting S-EDAs often failed to coevolve such strategies within a reasonable number of generations.     

A stability study on first-order neutral systems with three rationally independent time delays

First-order linear time invariant and time-delayed dynamics of neutral type is taken into account with three rationally independent delays. There are two main contributions of this study. (a) It is the first complete treatment in the literature on the stability analysis of systems with three delays. We use a recent procedure, the cluster treatment of characteristic roots (CTCR), for this purpose. This procedure results in an exact and exhaustive stability tableau in the domain of the three delays. (b) It provides a proof of a complex concept called the delay-stabilisability (also known as strong stability) as a by-product of CTCR. Furthermore, we deploy a numerical method (infinitesimal generator approach) to approximate the dominant characteristic roots of this class of systems, which concur with the stability outlook generated by CTCR.     

Discrete-event modeling of multi-agent systems with broadcasting-based parallel composition

Multi-agent systems have been widely used in logistics and manufacturing. In this paper we develop an automaton-based modeling framework for a special type of multi-agent systems, where agents are instantiated from a finite number of finite-state automaton templates, and interactions among agents are characterized via cooperative synchronization and broadcasting. To describe the compositional behavior of all agents, we propose a novel broadcasting-based parallel composition rule and show that it is commutative and associative. The effectiveness of this modeling framework and the parallel composition rule is illustrated in a simple multi-agent system.     

Negotiation in a multi-agent system for construction claims negotiation

Negotiation is an important approach for agents to co-operate and reach agreement in multiagent systems (MAS). Different negotiation theories and models have been deployed in a variety of applications. This paper is concerned with the applicability of these theories to the domain of agent-based construction claims negotiation. The peculiarities of this domain are highlighted and the approach adopted in the development of a multi-agent system for construction claims negotiation (MASCOT) described. Of particular interest is the integration of Zeuthen's bargaining model with a Bayesian learning mechanism, which addresses the characeristics of the construction claims negotiation. Examples are presented to demonstrate the impact of various negotiation approaches on the conduct and outcome of construction claims negotiations.     

Classification-based self-adaptive differential evolution and its application in multi-lateral multi-issue negotiation

Multi-lateral multi-issue negotiations are the most complex realistic negotiation problems. Automated approaches have proven particularly promising for complex negotiations and previous research indicates evolutionary computation could be useful for such complex systems. To improve the efficiency of realistic multi-lateral multi-issue negotiations and avoid the requirement of complete information about negotiators, a novel negotiation model based on an improved evolutionary algorithm p-ADE is proposed. The new model includes a new multi-agent negotiation protocol and strategy which utilize p-ADE to improve the negotiation efficiency by generating more acceptable solutions with stronger suitability for all the participants. Where p-ADE is improved based on the well-known differential evolution (DE), in which a new classification-based mutation strategy DE/rand-to-best/pbest as well as a dynamic self-adaptive parameter setting strategy are proposed. Experimental results confirm the superiority of p-ADE over several state-of-the-art evolutionary optimizers. In addition, the p-ADE based multiagent negotiation model shows good performance in solving realistic multi-lateral multi-issue negotiations.     

Beliefs, time and incomplete information in multiple encounter negotiations among autonomous agents

In negotiations among autonomous agents over resource allocation, beliefs about opponents, and about opponents’ beliefs, become particularly important when there is incomplete information. This paper considers interactions among self‐motivated, rational, and autonomous agents, each with its own utility function, and each seeking to maximize its expected utility. The paper expands upon previous work and focuses on incomplete information and multiple encounters among the agents. It presents a strategic model that takes into consideration the passage of time during the negotiation and also includes belief systems. The paper provides strategies for a wide range of situations. The framework satisfies the following criteria: symmetrical distribution, simplicity, instantaneously, efficiency and stability. This revised version was published online in June 2006 with corrections to the Cover Date.     

Efficient Management of Multi-Linked Negotiation Based on a Formalized Model

A Multi-linked negotiation problem occurs when an agent needs to negotiate with multiple other agents about different subjects (tasks, conflicts, or resource requirements), and the negotiation over one subject has influence on negotiations over other subjects. The solution of the multi-linked negotiations problem will become increasingly important for the next generation of advanced multi-agent systems. However, most current negotiation research looks only at a single negotiation and thus does not present techniques to manage and reason about multi-linked negotiations. In this paper, we first present a technique based on the use of a partial-order schedule and a measure of the schedule, called flexibility, which enables an agent to reason explicitly about the interactions among multiple negotiations. Next, we introduce a formalized model of the multi-linked negotiation problem. Based on this model, a heuristic search algorithm is developed for finding a near-optimal ordering of negotiation issues and their parameters. Using this algorithm, an agent can evaluate and compare different negotiation approaches and choose the best one. We show how an agent uses this technology to effectively manage interacting negotiation issues. Experimental work is presented which shows the efficiency of this approach.     

egoisst: a negotiation support system for electronic business-to-business negotiations in e-commerce

Electronic negotiations can range from simple offer exchanges to complex communicative acts concerning packages of products and services. In contrast to dominant approaches aiming at automating the negotiation process (e.g. auction models), we introduce the notion of negotiation support for human negotiators conducting complex electronic negotiations. The negotiation support system egoisst for business-to-business electronic commerce is presented that is based on theories of communication and information systems and that combines communication and document management. egoisst has been successfully validated for e-negotiations in the construction industry.     

egoisst: a negotiation support system for electronic business-to-business negotiations in e-commerce

Electronic negotiations can range from simple offer exchanges to complex communicative acts concerning packages of products and services. In contrast to dominant approaches aiming at automating the negotiation process (e.g. auction models), we introduce the notion of negotiation support for human negotiators conducting complex electronic negotiations. The negotiation support system egoisst for business-to-business electronic commerce is presented that is based on theories of communication and information systems and that combines communication and document management. egoisst has been successfully validated for e-negotiations in the construction industry.     

Framework of agent-based intelligence system with two-stage decision-making process for distributed dynamic scheduling

The advent of multiagent systems, a branch of distributed artificial intelligence, introduced a new approach to problem solving through agents interacting in the problem solving process. In this paper, a collaborative framework of a distributed agent-based intelligence system is addressed to control and resolve dynamic scheduling problem of distributed projects for practical purposes. If any delay event occurs, the self-interested activity agent, the major agent for the problem solving of dynamic scheduling in the framework, can automatically cooperate with other agents in real time to solve the problem through a two-stage decision-making process: the fuzzy decision-making process and the compensatory negotiation process. The first stage determines which behavior strategy will be taken by agents while delay event occurs, and prepares to next negotiation process; then the compensatory negotiations among agents are opened related with determination of compensations for respective decisions and strategies, to solve dynamic scheduling problem in the second stage. A prototype system is also developed and simulated with a case to validate the problem solving of distributed dynamic scheduling in the framework.     

DynamiCS: An Actor-Based Framework for Negotiating Mobile Agents

In this article, a framework to integrate negotiation capabilities—particularly, components implementing a negotiation strategy—into mobile agents is described. This approach is conceptually based on the notion of an actor system which decomposes an application component into autonomously executing subcomponents cooperating with each other. Technically, the framework is based on a plug-in mechanism enabling a dynamic composition of negotiating agents. Additionally, this contribution describes how interaction-oriented rule mechanisms can be deployed to control the behavior of strategy actors.     

Hierarchical multi-agent reinforcement learning

In this paper, we investigate the use of hierarchical reinforcement learning (HRL) to speed up the acquisition of cooperative multi-agent tasks. We introduce a hierarchical multi-agent reinforcement learning (RL) framework, and propose a hierarchical multi-agent RL algorithm called Cooperative HRL. In this framework, agents are cooperative and homogeneous (use the same task decomposition). Learning is decentralized, with each agent learning three interrelated skills: how to perform each individual subtask, the order in which to carry them out, and how to coordinate with other agents. We define cooperative subtasks to be those subtasks in which coordination among agents significantly improves the performance of the overall task. Those levels of the hierarchy which include cooperative subtasks are called cooperation levels. A fundamental property of the proposed approach is that it allows agents to learn coordination faster by sharing information at the level of cooperative subtasks, rather than attempting to learn coordination at the level of primitive actions. We study the empirical performance of the Cooperative HRL algorithm using two testbeds: a simulated two-robot trash collection task, and a larger four-agent automated guided vehicle (AGV) scheduling problem. We compare the performance and speed of Cooperative HRL with other learning algorithms, as well as several well-known industrial AGV heuristics. We also address the issue of rational communication behavior among autonomous agents in this paper. The goal is for agents to learn both action and communication policies that together optimize the task given a communication cost. We extend the multi-agent HRL framework to include communication decisions and propose a cooperative multi-agent HRL algorithm called COM-Cooperative HRL. In this algorithm, we add a communication level to the hierarchical decomposition of the problem below each cooperation level. Before an agent makes a decision at a cooperative subtask, it decides if it is worthwhile to perform a communication action. A communication action has a certain cost and provides the agent with the actions selected by the other agents at a cooperation level. We demonstrate the efficiency of the COM-Cooperative HRL algorithm as well as the relation between the communication cost and the learned communication policy using a multi-agent taxi problem.     

Enabling assisted strategic negotiations in actual-world procurement scenarios

In the everyday business world, the sourcing process of multiple goods and services usually involves complex negotiations (via telephone, fax, etc.) that include discussion of product and service features. Nowadays, this is a high-cost process due to the scarce use of tools that streamline this negotiation process and assist purchasing managers’ decision-making. With the advent of Internet-based technologies, it has become feasible the idea of tools enabling low-cost, assisted, fluid, on-line dialogs between buyer enterprises and their providers wherever they are located. Consequently, several commercial systems to support on-line negotiations (e-sourcing tools) have been released. It is our view that there is still a need for these systems to incorporate effective decision support. This article presents the foundations of Quotes, a commercial sourcing application developed by iSOCO that, in addition to cover the whole sequence of sourcing tasks, incorporates decision support facilities based on Artificial Intelligence (AI) techniques that successfully address previous limitations within a single and coherent framework. The paper focuses on the computational realization of sourcing tasks along with the decision support facilities they require. While supported negotiation events are Request for Quotations/Proposals (RFQs/RFPs) and reverse auctions, decision support facilities include offer generation, offer comparison, and optimal bid set computation (winner determination) in combinatorial negotiations. Additionally, the paper presents a compound of experiences and lessons learned when using Quotes for real sourcing processes.     

A bargaining-specific architecture for supporting automated service agreement negotiation systems

The provision of services is often regulated by means of agreements that must be negotiated beforehand. Automating such negotiations is appealing insofar as it overcomes one of the most often cited shortcomings of human negotiation: slowness. Our analysis of the requirements of automated negotiation systems in open environments suggests that some of them cannot be tackled in a protocol-independent manner, which motivates the need for a protocol-specific architecture. However, current state-of-the-art bargaining architectures fail to address all of these requirements together. Our key contribution is a bargaining architecture that addresses all of the requirements we have identified. The definition of the architecture includes a logical view that identifies the key architectural elements and their interactions, a process view that identifies how the architectural elements can be grouped together into processes, a development view that includes a software framework that provides a reference implementation developers can use to build their own negotiation systems, and a scenarios view by means of which the architecture is illustrated and validated.