FAIRNESS IN RECURRENT AUCTIONS WITH COMPETING MARKETS AND SUPPLY FLUCTUATIONS

Auctions have been used to deal with resource allocation in multiagent environments, especially in service‐oriented electronic markets. In this type of market, resources are perishable and auctions are repeated over time with the same or a very similar set of agents. In this scenario it is advisable to use recurrent auctions: a sequence of auctions of any kind where the result of one auction may influence the following one. Some problems do appear in these situations, as for instance, the bidder drop problem, the asymmetric balance of negotiation power or resource waste, which could cause the market to collapse. Fair mechanisms can be useful to minimize the effects of these problems. With this aim, we have analyzed four previous fair mechanisms under dynamic scenarios and we have proposed a new one that takes into account changes in the supply as well as the presence of alternative marketplaces. We experimentally show how the new mechanism presents a higher average performance under all simulated conditions, resulting in a higher profit for the auctioneer than with the previous ones, and in most cases avoiding the waste of resources.     

DECLARATIVE SPECIFICATION OF FAULT TOLERANT AUCTION PROTOCOLS: THE ENGLISH AUCTION CASE STUDY

Auction mechanisms are nowadays widely used in electronic commerce Web sites for buying and selling items among different users. The increasing importance of auction protocols in the negotiation phase is not limited to online marketplaces. In fact, the wide applicability of auctions as resource‐allocation and negotiation mechanisms have also led to a great deal of interest in auctions within the agent community. A challenging issue for agents operating in open Multiagent Systems (such as the emerging semantic Web infrastructure) concerns the specification of declarative communication rules which could be published and shared allowing agents to dynamically engage well‐known and trusted negotiation protocols. To cope with real‐world applications, these rules should also specify fault tolerant patterns of interaction, enabling negotiating agents to interact with each other tolerating failures, for instance terminating an auction process even if some bidding agents dynamically crash. In this paper, we propose an approach to specify fault tolerant auction protocols in open and dynamic environments by means of communication rules dealing with crash failures of agents. We illustrate these concepts considering a case study about the specification of an English Auction protocol which tolerate crashes of bidding agents and we discuss its properties.     

Enacting agent-based services for automated procurement

Negotiation events in industrial procurement involving multiple, highly customisable goods pose serious challenges to buying agents when trying to determine the best set of providing agents’ offers. Typically, a buying agent's decision involves a large variety of constraints that may involve attributes of a very same item as well as attributes of different, multiple items. In this paper we present iBundler, an agent-aware service offered to buying agents to help them determine the optimal bundle of received offers based on their business rules. In this way, buying agents are relieved with the burden of solving too hard a problem and concentrate on strategic issues. iBundler is intended as a negotiation service for buying agents and as a winner determination service for reverse combinatorial auctions with side constraints. Furthermore, we assess the computational cost added by employing agent technology in the development of iBundler to characterise the type of negotiation scenarios that it can acceptably handle.     

STRATUM: A METHODOLOGY FOR DESIGNING HEURISTIC AGENT NEGOTIATION STRATEGIES

Automated negotiation is a powerful (and sometimes essential) means for allocating resources among self-interested autonomous software agents. A key problem in building negotiating agents is the design of the negotiation strategy, which is used by an agent to decide its negotiation behavior. In complex domains, there is no single, obvious optimal strategy. This has led to much work on designing heuristic strategies, where agent designers usually rely on intuition and experience. In this article, we introduce STRATUM, a methodology for designing strategies for negotiating agents. The methodology provides a disciplined approach to analyzing the negotiation environment and designing strategies in light of agent capabilities and acts as a bridge between theoretical studies of automated negotiation and the software engineering of negotiation applications. We illustrate the application of the methodology by characterizing some strategies for the Trading Agent Competition and for argumentation-based negotiation.     

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.     

Coordination of purchasing and bidding activities across posted offer and auction markets

In both consumer purchasing and industrial procurement, combinatorial interdependencies among the items to be purchased are commonplace. E-commerce compounds the problem by providing more opportunities for switching suppliers at low costs, but also potentially eases the problem by enabling automated market decision-making systems, commonly referred to as trading agents, to make purchasing decisions in an integrated manner across markets. We are investigating a new approach to deal with the combinatorial interdependency challenges for online markets. This approach relies on existing commercial online market institutions such as posted-offer markets and various online auctions that sell single items. It uses trading agents to coordinate a buyer’s purchasing and bidding activities across multiple online markets simultaneously to achieve the best overall procurement effectiveness. This paper presents two sets of models related to this approach. The first set of models formalizes optimal purchasing decisions across posted-offer markets with fixed transaction costs. The second set of models is concerned with the coordination of bidding activities across multiple online auctions.Research reported in this paper was partially supported by a Proposition 301 Electronic Commerce Grant, University of Arizona Eller College of Management. The first author is also affiliated with The Key Lab of Complex Systems and Intelligence Science, Chinese Academy of Sciences (CAS), Beijing, and was supported in part by a grant for open research projects (ORP-0303) from CAS. Earlier versions of this paper have appeared in the Proceedings of the Hawai’i International Conference on System Sciences (HICSS-37) and the Proceedings of the 13th Annual Workshop on Information Technology and Systems (WITS ’03).     

Bid evaluation in combinatorial auctions: optimization and learning

In combinatorial auctions bidders can post bids on groups of items. The problem of selecting the winning bids, called Winner Determination Problem, is NP‐hard. In this paper, we consider an interesting variant of this problem, called Bid Evaluation Problem (BEP), where items are services and are subject to precedence constraints and temporal windows. The BEP has many practical applications, such as, for instance, in transportation routes auctions and in take off and landing time slot allocation problems. We have developed a number of optimization algorithms based on Constraint Programming, on Integer Programming and on the combination of the two techniques. We first show that all algorithms proposed outperform the only commercial system for solving BEP instances called Multi AGent Negotiation Testbed, a more general tool for agent negotiation. Then, we evaluate the developed algorithms and use the decision tree machine learning technique for finding a relation between the instance structure and the solving algorithm and providing an automatic algorithm selection procedure. We show that we can achieve an accuracy of 90% in predicting the best algorithm given the instance to be solved with a significant time saving w.r.t. a single solving technique or a costless, but less accurate, prediction technique. Copyright © 2009 John Wiley & Sons, Ltd.     

A secure multi-item e-auction mechanism with bid privacy

The recent focus within the auction field has been multi-item auctions where bidders are not restricted to buying only one item of the merchandise. It has been of practical importance in Internet auction sites and has been widely executed by them. In this paper, we concentrate on the use of the multi-item auction for task assignment scenarios and propose a novel PUPA auction protocol to solve the problem of bid privacy in multi-item auctions. A verifiable technique of shared key chain is proposed to find the winners without revealing the losing bid and bidder’s privacy. It can be shown that our new scheme is robust against cheating bidders.     

Negotiating for goods and services

Can a negotiation protocol be both fair and “envy-free” when more than two agents are involved? The authors consider how envy-free apportioning is more difficult than fair apportioning, but both can help to manage critical resources. They discuss an envy-free protocol and agent-based Web auctions     

Optimal-in-expectation redistribution mechanisms

Many important problems in multiagent systems involve the allocation of multiple resources among the agents. If agents are self-interested, they will lie about their valuations for the resources if they perceive this to be in their interest. The well-known VCG mechanism allocates the items efficiently, is strategy-proof (agents have no incentive to lie), and never runs a deficit. Nevertheless, the agents may have to make large payments to a party outside the system of agents, leading to decreased utility for the agents. Recent work has investigated the possibility of redistributing some of the payments back to the agents, without violating the other desirable properties of the VCG mechanism.Previous research on redistribution mechanisms has resulted in a worst-case optimal redistribution mechanism, that is, a mechanism that maximizes the fraction of VCG payments redistributed in the worst case. In contrast, in this paper, we assume that a prior distribution over the agents' valuations is available, and our goal is to maximize the expected total redistribution.In the first part of this paper, we study multi-unit auctions with unit demand. We analytically solve for a mechanism that is optimal among linear redistribution mechanisms. We also propose discretized redistribution mechanisms. We show how to automatically solve for the optimal discretized redistribution mechanism for a given discretization step size, and show that the resulting mechanisms converge to optimality as the step size goes to zero. We present experimental results showing that for auctions with many bidders, the optimal linear redistribution mechanism redistributes almost everything, whereas for auctions with few bidders, we can solve for the optimal discretized redistribution mechanism with a very small step size.In the second part of this paper, we study multi-unit auctions with nonincreasing marginal values. We extend the notion of linear redistribution mechanisms, previously defined only in the unit demand setting, to this more general setting. We introduce a linear program for finding the optimal linear redistribution mechanism. This linear program is unwieldy, so we also introduce one simplified linear program that produces relatively good linear redistribution mechanisms. We conjecture an analytical solution for the simplified linear program.     

MoRVAM: A reverse Vickrey auction system for mobile commerce

Internet auctions bring buyers and sellers together for the purpose of trading goods and services online. In order to get the goods, a buyer must search for items through several auction sites. When the auction starts, the buyer needs to connect to these auction sites frequently so that he/she can monitor the bid states and re-bid. In this paper, we propose an automated negotiation model between two participants, for mobile commerce, using collaborative mobile agents called MoRVAM, which mediates between the buyer and the sellers, and executes bidding asynchronously and autonomously. A new RVT protocol is also implemented to achieve unconditional bid privacy. Advantages of the RVT protocol are addressed as well. All the bidding process can be implemented without revealing losing bid and unnecessary information.     

Auction-Based Mechanisms for Electronic Procurement

Auction-based mechanisms are extremely relevant in modern day electronic procurement systems since they enable a promising way of automating negotiations with suppliers and achieve the ideal goals of procurement efficiency and cost minimization. This paper surveys recent research and current art in the area of auction-based mechanisms for e-procurement. The survey delineates different representative scenarios in e-procurement where auctions can be deployed and describes the conceptual and mathematical aspects of different categories of procurement auctions. We discuss three broad categories: 1) single-item auctions: auctions for procuring a single unit or multiple units of a single homogeneous type of item; 2) multi-item auctions: auctions for procuring a single unit or multiple units of multiple items; and 3) multiattribute auctions where the procurement decisions are based not only on costs but also on attributes, such as lead times, maintenance contracts, quality, etc. In our review, we present the mathematical formulations under each of the above categories, bring out the game theoretic and computational issues involved in solving the problems, and summarize the current art. We also present a significant case study of auction based e-procurement at General Motors.     

Contextual Resource Negotiation-Based Task Allocation and Load Balancing in Complex Software Systems

In the complex software systems, software agents always need to negotiate with other agents within their physical and social contexts when they execute tasks. Obviously, the capacity of a software agent to execute tasks is determined by not only itself but also its contextual agents; thus, the number of tasks allocated on an agent should be directly proportional to its self-owned resources as well as its contextual agents' resources. This paper presents a novel task allocation model based on the contextual resource negotiation. In the presented task allocation model, while a task comes to the software system, it is first assigned to a principal agent that has high contextual enrichment factor for the required resources; then, the principal agent will negotiate with its contextual agents to execute the assigned task. However, while multiple tasks come to the software system, it is necessary to make load balancing to avoid overconvergence of tasks at certain agents that are rich of contextual resources. Thus, this paper also presents a novel load balancing method: if there are overlarge number of tasks queued for a certain agent, the capacities of both the agent itself and its contextual agents to accept new tasks will be reduced. Therefore, in this paper, the task allocation and load balancing are implemented according to the contextual resource distribution of agents, which can be well suited for the characteristics of complex software systems; and the presented model can reduce more communication costs between allocated agents than the previous methods based on self-owned resource distribution of agents.     

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.     

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.     

Cooperative-Competitive Healthcare Service Negotiation

Service negotiation is a complex activity, especially in complex domains such as healthcare. The provision of healthcare services typically involves the coordination of several professionals with different skills and locations. There is usually negotiation between healthcare service providers as different services have specific constraints, variables, and features (scheduling, waiting lists, availability of resources, etc.), which may conflict with each other. While automating the negotiation processes by using software can improve the effciency and quality of healthcare services, most of the existing negotiation automations are positional bargaining in nature, and are not suitable for complex scenarios in healthcare services. This paper proposes a cooperative-competitive negotiation model that enables negotiating parties to share their knowledge and work toward optimal solutions. In this model, patients and healthcare providers work together to develop a patient-centered treatment plan. We further automate the new negotiation model with software agents.     

Autonomous Adaptive Agents for Single Seller Sealed Bid Auctions

In developing open, heterogeneous and distributed multi-agent systems researchers often face a problem of facilitating negotiation and bargaining amongst agents. It is increasingly common to use auction mechanisms for negotiation in multi-agent systems. The choice of auction mechanism and the bidding strategy of an agent are of central importance to the success of the agent model. Our aim is to determine the best agent learning algorithm for bidding in a variety of single seller auction structures in both static environments where a known optimal strategy exists and in complex environments where the optimal strategy may be constantly changing. In this paper we present a model of single seller auctions and describe three adaptive agent algorithms to learn strategies through repeated competition. We experiment in a range of auction environments of increasing complexity to determine how well each agent performs, in relation to an optimal strategy in cases where one can be deduced, or in relation to each other in other cases. We find that, with a uniform value distribution, a purely reactive agent based on Cliff’s ZIP algorithm for continuous double auctions (CDA) performs well, although is outperformed in some cases by a memory based agent based on the Gjerstad Dickhaut agent for CDA.     

Intelligent negotiation model for ubiquitous group decision scenarios

Supporting group decision-making in ubiquitous contexts is a complex task that must deal with a large amount of factors to succeed. Here we propose an approach for an intelligent negotiation model to support the group decision-making process specifically designed for ubiquitous contexts. Our approach can be used by researchers that intend to include arguments, complex algorithms, and agents’ modeling in a negotiation model. It uses a social networking logic due to the type of communication employed by the agents and it intends to support the ubiquitous group decision-making process in a similar way to the real process, which simultaneously preserves the amount and quality of intelligence generated in face-to-face meetings. We propose a new look into this problem by considering and defining strategies to deal with important points such as the type of attributes in the multicriterion problems, agents’ reasoning, and intelligent dialogues.     

Principal–agent problem: a cognitive map approach

As software agents become more and more intelligent, it becomes more and more difficult for human principals to understand and control them. This is a well-known principal–agent problem. There is, thus, need for a tool that can bridge the gap between human principals and software agents. In this paper, we discuss a new approach based on cognitive map to understand and control the knowledge of software agents. We propose a hierarchical framework to construct cognitive map from the rule base of software agents with the help of some guidelines. We applied the cognitive map approach to the famous Pursuit problem and demonstrated how learning can takes place with the help of cognitive maps. In this study, we found that cognitive map could be a promising tool for understanding and controlling intelligent software agents.