A price- and-time-slot-negotiation mechanism for Cloud service reservations

When making reservations for Cloud services, consumers and providers need to establish service-level agreements through negotiation. Whereas it is essential for both a consumer and a provider to reach an agreement on the price of a service and when to use the service, to date, there is little or no negotiation support for both price and time-slot negotiations (PTNs) for Cloud service reservations. This paper presents a multi-issue negotiation mechanism to facilitate the following: 1) PTNs between Cloud agents and 2) tradeoff between price and time-slot utilities. Unlike many existing negotiation mechanisms in which a negotiation agent can only make one proposal at a time, agents in this work are designed to concurrently make multiple proposals in a negotiation round that generate the same aggregated utility, differing only in terms of individual price and time-slot utilities. Another novelty of this work is formulating a novel time-slot utility function that characterizes preferences for different time slots. These ideas are implemented in an agent-based Cloud testbed. Using the testbed, experiments were carried out to compare this work with related approaches. Empirical results show that PTN agents reach faster agreements and achieve higher utilities than other related approaches. A case study was carried out to demonstrate the application of the PTN mechanism for pricing Cloud resources.     

Managing commitments in multiple concurrent negotiations

Automated negotiation by software agents is a key enabling technology for agent mediated e-commerce. To this end, this paper considers an important class of such negotiations – namely those in which an agent engages in multiple concurrent bilateral negotiations for a good or service. In particular, we consider the situation in which a buyer agent is looking for a single service provider from a number of available ones in its environment. By bargaining simultaneously with these providers and interleaving partial agreements that it makes with them, a buyer can reach good deals in an efficient manner. However, a key problem in such encounters is managing commitments since an agent may want to make intermediate deals (so that it has a definite agreement) with other agents before it gets to finalize a deal at the end of the encounter. To do this effectively, however, the agents need to have a flexible model of commitments that they can reason about in order to determine when to commit and to decommit. This paper provides and evaluates such a commitment model and integrates it into a concurrent negotiation model.     

Managing commitments in multiple concurrent negotiations

Automated negotiation by software agents is a key enabling technology for agent mediated e-commerce. To this end, this paper considers an important class of such negotiations – namely those in which an agent engages in multiple concurrent bilateral negotiations for a good or service. In particular, we consider the situation in which a buyer agent is looking for a single service provider from a number of available ones in its environment. By bargaining simultaneously with these providers and interleaving partial agreements that it makes with them, a buyer can reach good deals in an efficient manner. However, a key problem in such encounters is managing commitments since an agent may want to make intermediate deals (so that it has a definite agreement) with other agents before it gets to finalize a deal at the end of the encounter. To do this effectively, however, the agents need to have a flexible model of commitments that they can reason about in order to determine when to commit and to decommit. This paper provides and evaluates such a commitment model and integrates it into a concurrent negotiation model.     

A formal framework for connective stability of highly decentralized cooperative negotiations

Multiagent cooperative negotiation is a promising technique for modeling and controlling complex systems. Effective and flexible cooperative negotiations are especially useful for open complex systems characterized by high decentralization (which implies a low amount of exchanged information) and by dynamic connection and disconnection of agents. Applications include ad hoc network management, vehicle formation, and physiological model combination. To obtain an effective control action, the stability of the negotiation, namely the guarantee that an agreement will be eventually reached, is of paramount importance. However, the techniques usually employed for assessing the stability of a negotiation can be hardly applied in open scenarios. In this paper, whose nature is mainly theoretical, we make a first attempt towards engineering stable cooperative negotiations proposing a framework for their analysis and design. Specifically, we present a formal protocol for cooperative negotiations between a number of agents and we propose a criterion for negotiation stability based on the concept of connective stability. This is a form of stability that accounts for the effects of structural changes on the composition of a system and that appears very suitable for multiagent cooperative negotiations. To show its possible uses, we apply our framework for connective stability to some negotiations taken from literature.     

REACTIVE TUNING OF TARGET ESTIMATE ACCURACY IN MULTISENSOR DATA FUSION

Dealing with conflicting and target-specific requirements is an important issue in multisensor and multitarget tracking. This paper aims to allocate sensing resources among various targets in reaction to individual information requests. The proposed approach is to introduce agents for every relevant target responsible for its tracking. Such agents are expected to bargain with each other for a division of resources. A bilateral negotiation model is established for resource allocation in two-target tracking. The applications of agent negotiation to target covariance tuning are illustrated together with simulation results presented. Moreover, we suggest a way of organizing simultaneous one-to-one negotiations, making our negotiation model still applicable in scenarios of tracking more than two targets.     

Using similarity criteria to make issue trade-offs in automated negotiations

Automated negotiation is a key form of interaction in systems that are composed of multiple autonomous agents. The aim of such interactions is to reach agreements through an iterative process of making offers. The content of such proposals are, however, a function of the strategy of the agents. Here we present a strategy called the trade-off strategy where multiple negotiation decision variables are traded-off against one another (e.g., paying a higher price in order to obtain an earlier delivery date or waiting longer in order to obtain a higher quality service). Such a strategy is commonly known to increase the social welfare of agents. Yet, to date, most computational work in this area has ignored the issue of trade-offs, instead aiming to increase social welfare through mechanism design. The aim of this paper is to develop a heuristic computational model of the trade-off strategy and show that it can lead to an increased social welfare of the system. A novel linear algorithm is presented that enables software agents to make trade-offs for multi-dimensional goods for the problem of distributed resource allocation. Our algorithm is motivated by a number of real-world negotiation applications that we have developed and can operate in the presence of varying degrees of uncertainty. Moreover, we show that on average the total time used by the algorithm is linearly proportional to the number of negotiation issues under consideration. This formal analysis is complemented by an empirical evaluation that highlights the operational effectiveness of the algorithm in a range of negotiation scenarios. The algorithm itself operates by using the notion of fuzzy similarity to approximate the preference structure of the other negotiator and then uses a hill-climbing technique to explore the space of possible trade-offs for the one that is most likely to be acceptable.     

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.     

Multi-Issue Negotiation Processes by Evolutionary Simulation, Validation and Social Extensions

We describe a system for bilateral negotiations in which artificial agents aregenerated by an evolutionary algorithm (EA). The negotiations are governed bya finite-horizon version of the alternating-offers protocol. Several issuesare negotiated simulataneously. We first analyse and validate the outcomes ofthe evolutionary system, using the game-theoretic subgame-perfect equilibriumas a benchmark. We then present two extensions of the negotiation model. Inthe first extension agents take into account the fairness of the obtainedpayoff. We find that when the fairness norm is consistently applied during thenegotiation, agents reach symmetric outcomes which are robust and ratherinsensitive to the actual fairness settings. In the second extension we modela competitive market situation where agents have multiple bargainingopportunities before reaching the final agreement. Symmetric outcomes are nowalso obtained, even when the number of bargaining opportunities is small. Wefurthermore study the influence of search or negotiation costs in this game.     

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.     

STRATEGIC NEGOTIATION FOR SHARING A RESOURCE BETWEEN TWO AGENTS*

In this article, we propose a strategic negotiation model that enables self‐motivated rational agents to share resources. The strategic negotiation model takes the passage of time during the negotiation process itself into account. The model considers bilateral negotiations in situations characterized by complete information, in which one agent loses over time whereas the other gains over time. Using this negotiation mechanism, autonomous agents apply simple and stable negotiation strategies that result in efficient agreements without delay, even when there are dynamic changes in the environment. Simulation results show that our mechanism performs as well as a centralized scheduler and also has the property of balancing the resources' usage.     

基于GAI多属性依赖的协商模型

多属性之间的依赖关系增加协商Agent效用函数的复杂性,从而也增加多属性协商问题的复杂度.本文提出一种基于GAI多属性依赖的协商模型.该模型使用GAI分解将协商Agent的非线性效用函数表示为依赖属性子集的子效用之和.在协商过程中,协商双方采用不同的让步策略和提议策略来改变提议的内容.卖方Agent利用本文提出的GAI网合并算法将协商双方的GAI网合并,并利用生成的GAI树产生使社会福利评估值最大的提议.实验表明当买方Agent采用局部让步策略且卖方Agent采用全局让步策略时,协商双方能够在有限的协商步内达到接近Pareto最优的协商结局.     

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.     

A Market–Driven Model for Designing Negotiation Agents

Although there are many extant agent–based systems for negotiation in e–commerce, the negotiation strategies of agents in these systems are mostly static. This article presents a model for designing negotiation agents that make adjustable rates of concession by reacting to changing market situations. To determine the amount of concession for each trading cycle, these market–driven agents are guided by four mathematical functions of eagerness, trading time, trading opportunity , and competition . Trading opportunity is determined by considering: (i) number of trading partners, (ii) spreads —differences in utilities between an agent and its trading partners, and (iii) probability of completing a deal. Competition is determined by the probability that an agent is not considered the most preferred trader by other negotiating parties. Motivated by factors such as corporate policies and resource needs, eagerness represents an agent's desire to complete a deal. Agents with different time sensitivity to deadlines employ different trading strategies by making different rates of concession at different stages of negotiation. In this article, three classes of strategies with respect to remaining trading time are discussed. Theoretical analyses show that market–driven agents are designed to make prudent and appropriate amounts of concession for a given market situation.     

基于模糊理论的一对多自动谈判协调策略

针对一对多自动谈判参与各方在谈判过程中因为相互等待而效率低下的问题,提出一种能使谈判过程连续化的基于模糊的协调策略,以达到在尽可能少的时间内得到最满意协议的目的。该策略通过协调Agent灵活地创建和撤离新的谈判线程并在谈判过程中不断更新谈判的信念值进而支持连续谈判,满足开放和动态的谈判环境,提高谈判效率。模拟实验表明,该策略能够对谈判效用和时间进行优化,具有有效性和实用性。     

The Design, Usage, and Performance of GRUBER: A Grid Usage Service Level Agreement based BrokERing Infrastructure

We present GRUBER, a Grid Resource Usage service level agreement (uSLA) based BrokERing infrastructure, aimed at addressing the challenging issues that can arise within virtual organizations (VOs) that integrate participants and resources spanning multiple physical administrative domains. In such environments, participants delegate to one or more VOs the right to use certain resources subject to local policy and service level agreements; each VO then uses those resources subject to VO policy. GRUBER supports the explicit representation, enforcement, and management of service level agreements (SLAs) concerning resource usage (uSLAs) that can serve as an objective organizing principle for controlled resource sharing in distributed systems. uSLAs express how resources must be used over various time intervals and represent a novelty for the Grid domain. This paper provides a detailed overview of the GRUBER infrastructure, the evolution of its design to improve scalability, specifically the distribution of the resource brokering service, and the extended support for dynamic environments. We also present various results achieved over time that demonstrate both the utility and performance of GRUBER under various application workloads and scenarios. This work was carried out for CoreGrid IST project n°004265, funded by the European Commission.     

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.     

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.     

An intelligent negotiator agent design for bilateral contracts of electrical energy

In this paper, an intelligent agent (using the Fuzzy SARSA learning approach) is proposed to negotiate for bilateral contracts (BC) of electrical energy in Block Forward Markets (BFM or similar market environments). In the BFM energy markets, the buyers (or loads) and the sellers (or generators) submit their bids and offers on a daily basis. The loads and generators could employ intelligent software agents to trade energy in BC markets on their behalves. Since each agent attempts to choose the best bid/offer in the market, conflict of interests might happen. In this work, the trading of energy in BC markets is modeled and solved using Game Theory and Reinforcement Learning (RL) approaches. The Stackelberg equation concept is used for the match making among load and generator agents. Then to overcome the negotiation limited time problems (it is assumed that a limited time is given to each generator–load pairs to negotiate and make an agreement), a Fuzzy SARSA Learning (FSL) method is used. The fuzzy feature of FSL helps the agent cope with continuous characteristics of the environment and also prevents it from the curse of dimensionality. The performance of the FSL (compared to other well-known traditional negotiation techniques, such as time-dependent and imitative techniques) is illustrated through simulation studies. The case study simulation results show that the FSL based agent could achieve more profits compared to the agents using other reviewed techniques in the BC energy market.