Agents in Electronic Commerce: Component Technologies for Automated Negotiation and Coalition Formation

Automated negotiation and coalition formation among self-interested agents are playing an increasingly important role in electronic commerce. Such agents cannot be coordinated by externally imposing their strategies. Instead the interaction protocols have to be designed so that each agent is motivated to follow the strategy that the protocol designer wants it to follow. This paper reviews six component technologies that we have developed for making such interactions less manipulable and more efficient in terms of the computational processes and the outcomes: 1. OCSM-contracts in marginal cost based contracting, 2. leveled commitment contracts, 3. anytime coalition structure generation with worst case guarantees, 4. trading off computation cost against optimization quality within each coalition, 5. distributing search among insincere agents, and 6. unenforced contract execution. Each of these technologies represents a different way of battling self-interest and combinatorial complexity simultaneously. This is a key battle when multi-agent systems move into large-scale open settings.     

ECOOP: Applying Dynamic Coalition Formation to the Power Regulation Problem in Smart Grids

In this work, we focus on one particular area of the smart grid, namely, the challenges faced by distribution network operators in securing the balance between supply and demand in the intraday market, as a growing number of load‐controllable devices and small‐scale, intermittent generators coming from renewables are expected to pervade the system. We introduce a multiagent design to facilitate coordinating the various actors in the grid. The underpinning of our approach consists of an online cooperation scheme, ECOOP, where agents learn a prediction model regarding potential coalition partners and so can respond in an agile manner to situations that are occurring in the grid, by means of negotiating and formulating speculative solutions, with respect to the estimated behavior of the system. We provide a computational characterization for our solution in terms of complexity, as well as an empirical analysis against real consumption data sets, based on the macro‐model of the Australian energy market, showing a performance improvement of about 17%.     

Agent之间重复交互策略研究

在Agent构成的社会中信息可以共享,由于单一的交互对共享信息是没有作用的,因此,提出重复的交互策略。重复交互可以使Agent彼此共享信息,通过惩罚来达到系统的平衡,惩罚是通过忽视被惩罚的Agent的询问来实现的。通过简单地介绍一个周期模型,并且定义了延迟的折扣因子和预期效用,从而考虑了更一般的情况,即建立了n个周期模型,描述了通过观察询问的历史记录中没有应答的情况,来决定是否执行惩罚,并且通过试验进行了验证。随着n的增加,Agent的预期效用增加,而平衡的存在越来越少。     

Learning Sequences of Compatible Actions Among Agents

Action coordination in multiagent systemsis a difficult task especially in dynamicenvironments. If the environment possessescooperation, least communication,incompatibility and local informationconstraints, the task becomes even moredifficult. Learning compatible action sequencesto achieve a designated goal under theseconstraints is studied in this work. Two newmultiagent learning algorithms called QACE andNoCommQACE are developed. To improve theperformance of the QACE and NoCommQACEalgorithms four heuristics, stateiteration, means-ends analysis, decreasing reward and do-nothing, aredeveloped. The proposed algorithms are testedon the blocks world domain and the performanceresults are reported.     

Real-Time Search for Autonomous Agents and Multiagent Systems

Since real-time search provides an attractive framework for resource-bounded problem solving, this paper extends the framework for autonomous agents and for a multiagent world. To adaptively control search processes, we propose -search which allows suboptimal solutions with error, and -search which balances the tradeoff between exploration and exploitation. We then consider search in uncertain situations, where the goal may change during the course of the search, and propose a moving target search (MTS) algorithm. We also investigate real-time bidirectional search (RTBS) algorithms, where two problem solvers cooperatively achieve a shared goal. Finally, we introduce a new problem solving paradigm, called organizational problem solving, for multiagent systems.     

A LATTICE‐BASED APPROACH TO THE PROBLEM OF RECRUITMENT IN MULTIAGENT SYSTEMS

Multiagent systems constitute an independent topic at the intersection between distributed computing and artificial intelligence. As the algorithmic techniques and the applications for multiagent systems have been continuously developing over the last two decades reaching significantly mature stages, many methodological problems have been addressed. In this paper, we aim to contribute to this methodological assessment of multiagent systems by considering the problem of choosing, or recruiting, a subset of agents from a set of available agents to satisfy a given request. This problem, which we call problem of recruitment, is encountered, for example, in matchmaking and in task allocation. We present and study a novel formal approach to the problem of recruitment, based on the algebraic formalism of lattices. The resulting formal framework can support the development of algorithms for automatic recruitment.     

多主体系统中的联盟形成技术综述

本文给出了多主体联盟技术的定义,介绍了联盟形成的行为规范机制、CFG对策及其它的联盟形成模型。最后,给出了该领域的研究展望。     

Formation control of singular multiagent systems with switching topologies

This paper is concerned with the time‐varying formation control problem for singular multiagent systems with switching topologies. First, in order to eliminate the pulse solution of singular systems and extend the formation function set, the distributed formation controller has been formulated based on the output information of the agents. Then, the explicit expression of formation position function is presented based on the impulse free and the equivalent transformation of singular multiagent systems. Next, the sufficient and necessary conditions of the feasibility of the formation function are provided. Moreover, the sufficient conditions of formation control of singular multiagent systems with switching topologies are presented and the algorithm is designed to solve the distributed controller. Finally, the validity of the proposed approaches is verified by numerical simulation in this paper.     

Complementary‐based coalition formation for energy microgrids

In recent years, the notion of electrical energy microgrids (MGs), in which communities share their locally generated power, has gained increasing interest. Typically, the energy generated comes from renewable resources, which means that its availability is variable, ie, sometimes there may be energy surpluses and at other times energy deficits. This energy variability can be ameliorated by trading energy with a connected electricity grid. However, since main electricity grids are subject to faults or other outages, it can be advantageous for energy MGs to form coalitions and share their energy among themselves. In this work, we present our model for the dynamic formation of such MG coalitions. In our model, MGs form coalitions on the basis of complementary weather patterns. Our agent‐based model, which is scalable and affords autonomy among the MGs participating in the coalition (agents can join and depart from coalitions at any time), features methods to reduce overall “discomfort” so that, even when all participating MGs in a coalition experience deficits, they can share energy so that their overall discomfort is reduced. We demonstrate the efficacy of our model by showing empirical studies conducted with real energy production and consumption data.