Simultaneous Independent Online Auctions with Discrete Bid Increments

Decentralized multi-item auctions offer great opportunities for integrating fragmented online auction markets into larger markets with more efficient outcomes. This paper extends the theory of multi-item ascending auctions of substitutes by considering any finite positive bid increment and allowing the bidders to bid asyn-chronously instead of bidding in a round-robin fashion. We consider a setup where the bidders utilities over multiple items are additive and bound the maximum inefficiency in the allocation when the bidders follow a simple greedy strategy. We also obtain the limits within which the prices of individual items can vary from one outcome to another. For the special case of single unit bidder demand, we also bound the maximum surplus which a bidder can extract by unilaterally switching to some other strategy. The paper suggests an upper bound for the minimum required bid increment which would be necessary for competitive price discovery and truthful bidding in a practical online implementation.The work was done while the author was with IBM Research.     

Probabilistic Automated Bidding in Multiple Auctions

This paper presents an approach to develop bidding agents that participate in multiple auctions with the goal of obtaining an item with a given probability. The approach consists of a prediction method and a planning algorithm. The prediction method exploits the history of past auctions to compute probability functions capturing the belief that a bid of a given price may win a given auction. The planning algorithm computes a price and a set of compatible auctions, such that by sequentially bidding this price in each of the auctions, the agent can obtain the item with the desired probability. Experiments show that the approach increases the payoff of their users and the welfare of the market.     

Knowledge Management in Societies of Intelligent Adaptive Agents

A model is developed of the emergence of the knowledge level in asociety of agents where agents model and manage other agents as resources,and manage the learning of other agents to develop such resources. It isargued that any persistent system that actively creates the conditions forits persistence is appropriately modeled in terms of the rationalteleological models that Newell defines as characterizing the knowledgelevel. The need to distribute tasks in agent societies motivates suchmodeling, and it is shown that if there is a rich order relationship ofdifficulty on tasks that is reasonably independent of agents then it isefficient to model agents competencies in terms of their possessingknowledge. It is shown that a simple training strategy of keeping an agent'sperformance constant by allocating tasks of increasing difficulty as anagent adapts optimizes the rate of learning and linearizes the otherwisesigmoidal learning curves. It is suggested that this provides a basis forassigning a granularity to knowledge that enables learning processes to bemanaged simply and efficiently.     

智能机器人的自适应自主Agents的建模

在问题求解期间及穿越动态的、不可预测的环境的过程中 ,真实世界的问题需要自适应的求解方法来‘裁剪’Agent的行为 ,并使其落入任务域 ,否则不完备的知识、不确定性、不确定的Agents及过程的存在、硬件失灵和不精确性都会引发不确定事件的发生。自适应自主Agents是一种存在于动态的、不可预测的环境中 ,并试图满足一系列随时间变化的目标或动机的系统 ,而且此类Agents在处理这些目标的过程中 ,能基于已有的经验改进其能力。自适应自主Agents为智能机器人实现这一目标提供了一种新的途径。主要讨论了智能机器人的自适应自主Agents的建模问题 ,并指出它的局限性和开问题     

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.     

Games That Agents Play: A Formal Framework for Dialogues between Autonomous Agents

We present a logic-based formalism for modeling ofdialogues between intelligent and autonomous software agents,building on a theory of abstract dialogue games which we present.The formalism enables representation of complex dialogues assequences of moves in a combination of dialogue games, and allowsdialogues to be embedded inside one another. The formalism iscomputational and its modular nature enables different types ofdialogues to be represented.     

求解组合拍卖NP问题的遗传算法

多智能体系统采用多件物品组合拍卖协商协议能高效地实现组合资源及多任务的分配,但在传统组合拍卖协商协议中拍卖方选择买方以实现最大赢利的过程是一个NP问题,本文提出了利用改进的遗传算法来求解该NP问题的新方法,并应用于一个企业供应链管理的自动协商交易系统中。实验表明,该算法具有较优的性能。     

Auctions in a Two-Sided Network: The Market for Meal Voucher Services

In two-sided networks, two parties interact on a platform, caring about the numbers of subjects on the other side. A typical problem, in this context, is setting prices for network services so as to get “both side on boards”. The standard approach in the literature considers the networks ability to convince both sides to join the network, whereas this paper consider an alternative setting, in which one side determines the price balance. This case is exemplified by the market for meal voucher services in Italy, in which one agency organizes the procurement of services for the public sector, through a competitive auction. A formal model of optimal auction is illustrated and used to assess the current procurement scheme. The model reveals that the current system is inefficient and could be improved through increased flexibility and adaptation to local economic conditions.     

New Algorithms for Mining the Reputation of Participants of Online Auctions

The assessment of credibility and reputation of contractors in online auctions is the key issue in providing reliable environment for customer-to-customer e-commerce. Confident reputation rating system is an important factor in managing risk and building customer satisfaction. Unfortunately, most online auction sites employ a very simple reputation rating scheme that utilizes user feedbacks and comments issued after committed auctions. Such schemes are easy to deceive and do not provide satisfactory protection against several types of fraud. In this paper we propose two novel measures of trustworthiness, namely, credibility and density. We draw inspiration from social network analysis and present two algorithms for reputation rating estimation. Our first algorithm computes the credibility of participants by an iterative search of inter-participant connections. Our second algorithm discovers clusters of participants who are densely connected through committed auctions. We test both measures on real-world data and we experimentally compare them with existing solutions.     

Inapproximability Results for Combinatorial Auctions with Submodular Utility Functions

We consider the following allocation problem arising in the setting of combinatorial auctions: a set of goods is to be allocated to a set of players so as to maximize the sum of the utilities of the players (i.e., the social welfare). In the case when the utility of each player is a monotone submodular function, we prove that there is no polynomial time approximation algorithm which approximates the maximum social welfare by a factor better than 1−1/e≃0.632, unless P=NP. Our result is based on a reduction from a multi-prover proof system for MAX-3-COLORING. This work was performed when all authors were at the Georgia Institute of Technology. A preliminary version of this work appears in Khot et al. (Workshop on internet and network economics, pp. 92–101, 2005) R.J. Lipton’s research supported by NSF grant CCF-0431023.     

The Advantages of Designing Adaptive Business Agents Using Reputation Modeling Compared to the Approach of Recursive Modeling

Adaptive business agents operate in electronic marketplaces, learning from past experiences to make effective decisions on behalf of their users. How best to design these agents is an open question. In this article, we present an approach for the design of adaptive business agents that uses a combination of reinforcement learning and reputation modeling. In particular, we take into account the fact that multiple selling agents may offer the same good with different qualities, and that selling agents may alter the quality of their goods. We also consider the possibility of dishonest agents in the marketplace. Our buying agents exploit the reputation of selling agents to avoid interaction with the disreputable ones, and therefore to reduce the risk of purchasing low value goods. We then experimentally compare the performance of our agents with those designed using a recursive modeling approach. We are able to show that agents designed according to our algorithms achieve better performance in terms of satisfaction and computational time and as such are well suited for the design of electronic marketplaces.     

Epistemic Logic for Rule-Based Agents

The logical omniscience problem, whereby standard models of epistemic logic treat an agent as believing all consequences of its beliefs and knowing whatever follows from what else it knows, has received plenty of attention in the literature. But many attempted solutions focus on a fairly narrow specification of the problem: avoiding the closure of belief or knowledge, rather than showing how the proposed logic is of philosophical interest or of use in computer science or artificial intelligence. Sentential epistemic logics, as opposed to traditional possible worlds approaches, do not suffer from the problems of logical omniscience but are often thought to lack interesting epistemic properties. In this paper, I focus on the case of rule-based agents, which play a key role in contemporary AI research but have been neglected in the logical literature. I develop a framework for modelling monotonic, nonmonotonic and introspective rule-based reasoners which have limited cognitive resources and prove that the resulting models have a number of interesting properties. An axiomatization of the resulting logic is given, together with completeness, decidability and complexity results.     

一种基于Agent的自适应的分布式入侵检测系统

针对传统分布式入侵检测系统组件之间依赖程度大、系统不够健壮且入侵检测系统自身结构固定不能适应入侵的变化的问题,提出了一种基于Agent的自适应的分布式入侵检测系统（简称AAADIDS）．AAADIDS采用Agent概念重新构造系统的组件,改进了分布式入侵检测系统由于高层节点单一无冗余而产生的可靠性差的缺陷,从构造上克服了分布式入侵检测系统的脆弱性．同时,AAADIDS系统采用智能技术构建了自适应的入侵检测系统模型,增加了系统应对入侵行为变化的智能性．AAADIDS系统相对于传统的分布式入侵检测系统有效地提高了系统自身的可靠性和针对外界变化的适应能力．     

Software Agents and Their Bodies

Within artificial intelligence and the philosophy of mind,there is considerable disagreement over the relationship between anagent's body and its capacity for intelligent behavior. Some treatthe body as peripheral and tangential to intelligence; others arguethat embodiment and intelligence are inextricably linked. Softwareagents–-computer programs that interact with software environmentssuch as the Internet–-provide an ideal context in which to studythis tension. I develop a computational framework for analyzingembodiment. The framework generalizes the notion of a body beyondmerely having a physical presence. My analysis sheds light oncertain claims made about the relevance of the body to intelligence,as well as on embodiment in software worlds.     

Ethology as an Inspiration for Adaptive Behavior Synthesis in Autonomous Planetary Rovers

A navigation and control approach that supports adaptive behavior in rovers is presented. It is motivated by ethological models that suggest hierarchical organizations of behavior. The methodology employs fuzzy logic as a means to emulate animal behavior control mechanisms such as behavior activation levels, multi-behavior modulation, and threshold activation. The paper describes how these concepts can be tailored for autonomous navigation to provide a suitable framework for situated adaptation in rover control algorithms. In addition, an interesting characteristic of observed behavioral interactions achieved by an implementation of the approach is discussed, which is analogous to phenomena observed in measurements of animal brain activity during transitions between distinct behaviors.     

一种采用模糊逻辑方法的卖方报价策略

随着Internet的快速发展,电子商贸应运而生。由于商贸业务的复杂性,所以在电子商贸应用系统中通常采用具有一定自主特性和智能特性的Agent(软件实体)模拟商贸业务中的活动主体,由此产生了多Agent之间的交易协商技术的研究。该文提出了一种基于模糊逻辑方法的能够适用于电子商贸活动中的卖方Agent的报价策略。此策略运用了模糊规则和模糊推理机制,能很好地适应交易信息的不完整性和不确定性。     

单神经元自适应PID控制器及其应用

研究了单神经元自适应PID控制器,阐述了该控制器的特点、控制律、适用对象及工程整定方法,在和利时公司的SmartPro系统平台上开发出单神经元自适应PID控制器,进行了单神经元自适应PID控制器的典型一、二阶对象闭环仿真,最终将单神经元自适应PID控制器应用于制药厂发酵罐温度控制回路中。单神经元控制器具有可调参数少、易于整定、控制输出平稳、鲁棒性强的独特优点,适用于大滞后且要求平稳控制输出的工业过程。     

Autonomous Mapping of E-Business Demands and Supplies via Invisible Internet Agents

In business Internet-based systems demands and supplies can be hidden in different forms and locations. We develop a simple type of agents called knowledgeable objects for carrying and mapping heterogeneous and distributed business data in a self-processing way. Internet agents coordinate these invisible agent objects to implement a competitive mapping via agent bidding. Interval-valued numbers and fuzzy ranking are utilized for representing and clustering dynamic business data. The concept of agent awareness and invisibility is used for regulating agent interaction scope to decrease mapping time and to accord with system capacity. Our analysis shows that this combination of agent-interval-based techniques not only meets the distributed, heterogeneous, and dynamic tendencies of E-business systems but also makes the mapping process more autonomous and efficient.     

User Environments and Individuals: Experience with Adaptive Interface Agents

The traditional style of working with computers generally revolves around the computer being used as a tool, with individual users directly initiating operations and waiting for the results of them. A more recent paradigm of human-computer interaction, based on the indirect management of computing resources, is agent-based interaction. The idea of delegation plays a key part in this approach to computer-based work, which allows individuals to relinquish the routine, mechanistic parts of their everyday tasks, having them performed automatically instead. Adaptive interfaces combine elements of both these approaches, where the goal is to have the interface adapt to its users rather than the reverse. This paper addresses some of the issues arising from a practical software development process which aimed to support individuals using this style of interaction. This paper documents the development of a set of classes which implement an architecture for adaptive interfaces. These classes are intended to be used as part of larger user interface systems which are to exhibit adaptive behaviour. One approach to the implementation of an adaptive interface is to use a set of software “agents”– simple processes which effectively run “in the background”– to decompose the task of implementing the interface. These agents form part of a larger adaptive interface architecture, which in turn forms a component of the adaptive system.     

基于移动Agent的主动网络自适应入侵响应系统的研究

随着网络入侵及安全事件的频繁发生,使得自动响应受到广泛关注。在对相关研究领域已有的工作进行总结的基础上,提出了一种基于移动Agent的主动网络自适应入侵响应系统（Intrusion Response System,IRS）。系统通过响应分析,自动产生响应策略,并派遣移动Agent去执行;根据入侵检测系统（Intrusion Detection System,IDS）的报警可信度和响应执行情况,系统能够自适应地调整响应策略,体现了系统的自动性和自适应性。