Agents that react to changing market situations

Market-driven agents are negotiation agents that react to changing market situations by making adjustable rates of concession. This paper presents 1) the foundations for designing market-driven strategies of agents, 2) a testbed of market-driven agents, 3) experimental results in simulating the market-driven approach, and 4) theoretical analyses of agents' performance in extremely large markets. In determining the amount of concession for each trading cycle, market-driven agents in this research are guided by four mathematical functions of eagerness, remaining trading time, trading opportunity , and competition. At different stages of trading, agents may adopt different trading strategies, and make different rates of concession. Four classes of strategies with respect to remaining trading time are discussed. Trading opportunity is determined by considering: 1) number of trading partners, 2) spreads-differences in utilities between an agent and its trading partners, and 3) probability of completing a deal. While eagerness represents an agent's desire to trade, trading competition is determined by the probability that it is not considered as the most preferred trader by its trading partners. Experimental results and theoretical analyses showed that agents guided by market-driven strategies 1) react to changing market situations by making prudent and appropriate rates of concession, and 2) achieve trading outcomes that correspond to intuitions in real-life trading.     

Continuous-Time Negotiation Mechanism for Software Agents

While there are several existing mechanisms and systems addressing the crucial and difficult issues of automated one-to-many negotiation, this paper develops a flexible one-to-many negotiation mechanism for software agents. Unlike the existing general one-to-many negotiation mechanism, in which an agent should wait until it has received proposals from all its trading partners before generating counterproposals, in the flexible one-to-many negotiation mechanism, an agent can make a proposal in a flexible way during negotiation, i.e., negotiation is conducted in continuous time. To decide when to make a proposal, two strategies based on fixed waiting time and a fixed waiting ratio is proposed. Results from a series of experiments suggest that, guided by the two strategies for deciding when to make a proposal, the flexible negotiation mechanism achieved more favorable trading outcomes as compared with the general one-to-many negotiation mechanism. To determine the amount of concession, negotiation agents are guided by four mathematical functions based on factors such as time, trading partners' strategies, negotiation situations of other threads, and competition. Experimental results show that agents guided by the four functions react to changing market situations by making prudent and appropriate rates of concession and achieve generally favorable negotiation outcomes     

一种基于市场驱动的Multi-agent自动谈判策略

针对当前电子商务中基于Agent的谈判系统的谈判策略的静态性问题,提出基于市场驱动的谈判策略。Agent在谈判中能根据变化的市场情况做出可以调整比率的让步,帮助用户做出最优的交易决策,且自动地选择合适的策略。实验结果表明,采用基于市场驱动的策略比采用固定的策略的谈判结果更让用户感到满意。     

Toward market-driven agents for electronic auction

While there are several existing agent-based systems addressing the crucial and difficult issues of automated negotiation and auction, this research has designed and engineered a society of trading agents with two distinguishing features: 1) a market-driven negotiation strategy and 2) a deal optimizing auction protocol. Unlike some of the existing systems where users manually select predefined trading strategies, in the market-driven approach, trading agents automatically select the appropriate strategies by examining the changing market situations. Results from a series of experiments suggest that the market-driven approach generally achieved more favorable outcomes as compared to the fixed strategy approach. Furthermore, it provides a more intuitive simulation of trading because trading agents are able to respond to different market situations with appropriate strategies. By augmenting the auction protocol with a deal optimization stage, trading agents can be programmed to optimize transaction deals by delaying the finalization of deals in search of better deals. Experimental results showed that by having a deal optimization stage, the auction protocol produced generally optimistic outcomes     

Market_based grid resource allocation using new negotiation model

This paper presents a new negotiation model for designing Market- and Behavior-driven Negotiation Agents (MBDNAs) that address computational grid resource allocation problem. To determine the amount of concession for each trading cycle, the MBDNAs are guided by six factors: (1) number of negotiator's trading partners, (2) number of negotiator's competitors, (3) negotiator's time preference, (4) flexibility in negotiator's trading partner's proposal, (5) negotiator's proposal deviation from the average of its trading partners’ proposals, and (6) previous concession behavior of negotiator's trading partner. In our experiments, we compare grid resource consumer (GRC) of type MBDNAs (respectively grid resource owner (GRO) of type MBDNAs) with MDAs (Market Driven Agents) in terms of the following metrics: total tasks complementation and average utility (respectively resource utilization level and average utility). The results show that by taking the proposed factors into account, MBDNAs of both types make a more efficient concession amount than MDAs and are, therefore, considered an appropriate mechanism for grid resource allocation in different grid workloads and market types.     

Equilibria, prudent compromises, and the "waiting" game.

While evaluation of many e-negotiation agents are carried out through empirical studies, this work supplements and complements existing literature by analyzing the problem of designing market-driven agents (MDAs) in terms of equilibrium points and stable strategies. MDAs are negotiation agents designed to make prudent compromises taking into account factors such as time preference, outside option, and rivalry. This work shows that 1) in a given market situation, an MDA negotiates optimally because it makes minimally sufficient concession, and 2) by modeling negotiation of MDAs as a game gamma of incomplete information, it is shown that the strategies adopted by MDAs are stable. In a bilateral negotiation, it is proven that the strategy pair of two MDAs forms a sequential equilibrium for gamma. In a multilateral negotiation, it is shown that the strategy profile of MDAs forms a market equilibrium for gamma.     

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.     

Learning opponent’s beliefs via fuzzy constraint-directed approach to make effective agent negotiation

This work presents a general framework of agent negotiation with opponent learning via fuzzy constraint-directed approach. The fuzzy constraint-directed approach involves the fuzzy probability constraint and the fuzzy instance reasoning. The proposed approach via fuzzy probability constraint can not only cluster the opponent’s information in negotiation process as proximate regularities to improve the convergence of behavior patterns, but also eliminate the noisy hypotheses or beliefs to enhance the effectiveness on beliefs learning. By using fuzzy instance method, our approach can reuse the prior opponent knowledge to speed up the problem-solving, and reason the proximate regularities to acquire desirable results on predicting opponent behavior. In addition, the proposed interaction method enables the agent to make a concession dynamically based on expected objectives. Moreover, experimental results suggest that the proposed framework allows an agent to achieve a higher reward, a fairer deal, or a smaller cost of negotiation.     

Developing actionable trading agents

Trading agents are useful for developing and back-testing quality trading strategies to support smart trading actions in the market. However, most of the existing trading agent research oversimplifies trading strategies, and focuses on simulated ones. As a result, there exists a big gap between the deliverables and business needs when the developed strategies are deployed into the real life. Therefore, the actionable capability of developed trading agents is often very limited. This paper for the first time introduces effective approaches for optimizing and integrating multiple classes of strategies through trading agent collaboration. An integration and optimization approach is proposed to identify optimal trading strategy in each category, and further integrate optimal strategies crossing classes. Positions associated with these optimal strategies are recommended for trading agents to take actions in the market. Extensive experiments on a large quantity of real-life market data show that trading agents following the recommended strategies have great potential to obtain high benefits while low costs. This verifies that it is promising to develop trading agents toward workable and satisfying business needs.

A BDI Assignment Protocol With New Cooperative-Concession Strategies

This paper addresses the collaborative linear- assignment problem (CLAP) for a class of allocation applications. CLAP entails using agents to seek a concurrent allocation of one different object for every agent, to optimize a linear sum efficiency function as their (soft) social goal. Anchoring in the standard framework of automated negotiation allows an original belief-desire-intention (BDI) negotiation model for CLAP to be conceptually separated into a BDI assignment protocol and an adopted strategy. Facilitated by this conceptual separation, the contributions of this paper are as follows: 1) providing a rigorous analysis of the protocol and demonstrating its salient properties and 2) formulating new strategies using a novel idea of cooperative concession. Four different strategies for a negotiation agent and the arbitration agent provide 16 arbitration-negotiation combinations running with the protocol, and these are empirically assessed for their performance profiles in negotiation speed and solution quality. Important findings, including the stability of the protocol in producing better than good enough global allocations and the strategic influence of cooperative concessions on performance, are examined. The significance and practicality of this paper in relation to existing work are also discussed.     

Negotiation Agents that Make Prudent Compromises and are Slightly Flexible in Reaching Consensus

The contribution of this work is designing and developing enhanced market-driven agents with the flexibility to (1) respond to changing market conditions, and (2) raise and relax trade aspirations. Previous theoretical analyses have shown that market-driven agents ( MDA s) make prudent compromises by reacting to changing market situations by taking into account factors such as competition, deadlines, and trading options. This work augments the design of an MDA with three fuzzy decision controllers that guide the agent in (i) relaxing trade aspiration in face of intense negotiation pressure, and (ii) raising trade aspiration in extremely favorable markets. Results from extensive simulations conducted using an implemented testbed suggest that when compared to MDA s, agents in this work achieved (1) higher success rates in reaching deals, (2) higher average utilities, and (3) higher expected utility.     

Grid commerce, market-driven G-negotiation, and Grid resource management.

Although the management of resources is essential for realizing a computational grid, providing an efficient resource allocation mechanism is a complex undertaking. Since Grid providers and consumers may be independent bodies, negotiation among them is necessary. The contribution of this paper is showing that market-driven agents (MDAs) are appropriate tools for Grid resource negotiation. MDAs are e-negotiation agents designed with the flexibility of: 1) making adjustable amounts of concession taking into account market rivalry, outside options, and time preferences and 2) relaxing bargaining terms in the face of intense pressure. A heterogeneous testbed consisting of several types of e-negotiation agents to simulate a Grid computing environment was developed. It compares the performance of MDAs against other e-negotiation agents (e.g., Kasbah) in a Grid-commerce environment. Empirical results show that MDAs generally achieve: 1) higher budget efficiencies in many market situations than other e-negotiation agents in the testbed and 2) higher success rates in acquiring Grid resources under high Grid loadings.     

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.     

基于对手不完全信息的订单在线智能协商模型

针对订单在线协商延误率和失败率高的问题,基于Zeuthen协商策略提出多阶段多边协进化协商算法。引入新的协调者角色控制多边谈判,并利用贝叶斯原理,通过逐渐修正对手底价估算向量的概率分布和动态调整报价曲线获得最优协商让步幅度,结合同步淘汰机制有效避免了无效协商。实验表明该模型能够充分利用对手信息实时更新智能体协商信念,进而明显地改进了协商行为的效用。     

Automatic price negotiation on the web: An agent-based web application using fuzzy expert system

A traditional internet auction is restricted by the limitation of time. It is necessary to conduct an internet auction in a certain time period. The final trading price is determined until this certain period ends. This study improves this situation by removing the time limitation. Based on the fuzzy inference theory, this paper proposes an agent-based price negotiation system for on-line auctions. Mainly, three agents are used in the study: a seller agent, a buyer agent, and a mediator agent. The proposed system provides an easy-to-use environment and good customizability for users (buyers or sellers) to customize their price negotiation strategies using user-defined fuzzy rules. The final negotiated price is immediately determined after the buyer sends his bids to the proposed system. This study develops a Java-based computer package to implement the price negotiation system where Model-View-Controller (MVC) design pattern is employed in design of the package. Unified Modeling Language (UML) is also utilized to describe the structures and behaviors of the package. To validate the proposed system, this study built an on-line auction website with the proposed price negotiation mechanism for internet users to buy or sell their merchandises. An evaluation was finally conducted to investigate the users’ satisfaction with the proposed system.Briefly, the proposed system is featured by: (1) instantly getting negotiated price without waiting; (2) conducting price negotiation at any time; (3) determining strategy rules easily, and (4) using customizable negotiation strategies defined by users.     

Multi-item auctions for automatic negotiation

Available resources can often be limited with regard to the number of demands. In this paper we propose an approach for solving this problem, which consists of using the mechanisms of multi-item auctions for allocating the resources to a set of software agents. We consider the resource problem as a market in which there are vendor agents and buyer agents trading on items representing the resources. These agents use multi-item auctions, which are viewed here as a process of automatic negotiation, and implemented as a network of intelligent software agents. In this negotiation, agents exhibit different acquisition capabilities that let them act differently depending on the current context or situation of the market. For example, the ‘richer’ an agent is, the more items it can buy, i.e. the more resources it can acquire. We present a model for this approach based on the English auction, then we discuss experimental evidence of such a model.     

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.     

A context-aware approach to automated negotiation using reinforcement learning

Agents negotiate depending on individual perceptions of facts, events, trends and special circumstances that define the negotiation context. The negotiation context affects in different ways each agent’s preferences, bargaining strategies and resulting benefits, given the possible negotiation outcomes. Despite the relevance of the context, the existing literature on automated negotiation is scarce about how to account for it in learning and adapting negotiation strategies. In this paper, a novel contextual representation of the negotiation setting is proposed, where an agent resorts to private and public data to negotiate using an individual perception of its necessity and risk. A context-aware negotiation agent that learns through Self-Play and Reinforcement Learning (RL) how to use key contextual information to gain a competitive edge over its opponents is discussed in two levels of temporal abstraction. Learning to negotiate in an Eco-Industrial Park (EIP) is presented as a case study. In the Peer-to-Peer (P2P) market of an EIP, two instances of context-aware agents, in the roles of a buyer and a seller, are set to bilaterally negotiate exchanges of electrical energy surpluses over a discrete timeline to demonstrate that they can profit from learning to choose a negotiation strategy while selfishly accounting for contextual information under different circumstances in a data-driven way. Furthermore, several negotiation episodes are conducted in the proposed EIP between a context-aware agent and other types of agents proposed in the existing literature. Results obtained highlight that context-aware agents do not only reap selfishly higher benefits, but also promote social welfare as they resort to contextual information while learning to negotiate.     

CMieux: Adaptive strategies for competitive supply chain trading

Supply chains are a central element of today’s global economy. Existing management practices consist primarily of static interactions between established partners. Global competition, shorter product life cycles and the emergence of Internet-mediated business solutions create an incentive for exploring more dynamic supply chain practices. The supply chain trading agent competition (TAC SCM) was designed to explore approaches to dynamic supply chain trading between automated software agents. TAC SCM pits trading agents developed by teams from around the world against one another. Each agent is responsible for running the procurement, planning and bidding operations of a PC assembly company, while competing with others for both customer orders and supplies under varying market conditions. This paper presents Carnegie Mellon University’s 2005 TAC SCM entry, the CMieux supply chain trading agent. CMieux implements a novel approach for coordinating supply chain bidding, procurement and planning, with an emphasis on the ability to rapidly adapt to changing market conditions. We present empirical results based on 200 games involving agents entered by 25 different teams during what can be seen as the most competitive phase of the 2005 tournament. Not only did CMieux perform among the top five agents, it significantly outperformed these agents in procurement while matching their bidding performance. We also simulated 40 games against the best publicly available agent binaries. Our results show CMieux has significantly better average overall performance than any of these agents.     

The Strategic Exploitation of Limited Information and Opportunity in Networked Markets

This paper studies the effect of constraining interactions within a market. A model is analysed in which boundedly rational agents trade with and gather information from their neighbours within a trade network. It is demonstrated that a trader’s ability to profit and to identify the equilibrium price is positively correlated with its degree of connectivity within the market. Where traders differ in their number of potential trading partners, well-connected traders are found to benefit from aggressive trading behaviour. Where information propagation is constrained by the topology of the trade network, connectedness affects the nature of the strategies employed.