A one-shot bargaining strategy for dealing with multifarious opponents

Bargaining is an effective paradigm to solve the problem of resource allocation. The consideration of factors such as bounded rationality of negotiators, time constraints, incomplete information, and complexity of dynamic environment make the design of optimal strategy for one-shot bargaining much tougher than the situation that all bargainers are assumed to be absolutely rational. Lots of prediction-based strategies have been explored either based on assuming a finite number of models for opponents, or focusing on the prediction of opponent’s reserve price, deadline, or the probabilities of different behaviors. Following the methods of estimating opponent’s private information, this paper gives a strategy which improves the BLGAN strategy to adapt to various possible bargaining situations and deal with multifarious opponents. In addition, this paper compares the improved BLGAN strategy with related work. Experimental results show that the improved BLGAN strategy can outperform related ones when faced with various opponents, especially the agents who frequently change their strategies for anti-learning.     

A fuzzy-logic based bidding strategy for autonomous agents in continuous double auctions

Increasingly, many systems are being conceptualized, designed, and implemented as marketplaces in which autonomous software entities (agents) trade services. These services can be commodities in e-commerce applications or data and knowledge services in information economies. In many of these cases, there are both multiple agents that are looking to procure services and multiple agents that are looking to sell services at any one time. Such marketplaces are termed continuous double auctions (CDAs). Against this background, this paper develops new algorithms that buyer and seller agents can use to participate in CDAs. These algorithms employ heuristic fuzzy rules and fuzzy reasoning mechanisms in order to determine the best bid to make given the state of the marketplace. Moreover, we show how an agent can dynamically adjust its bidding behavior to respond effectively to changes in the supply and demand in the marketplace. We then show, by empirical evaluations, how our agents outperform four of the most prominent algorithms previously developed for CDAs (several of which have been shown to outperform human bidders in experimental studies).     

Agents in E-Commerce: State of the Art

This paper surveys the state of the art of agent-mediated electronic commerce (e-commerce), especially in business-to-consumer (B2C) e-commerce and business-to-business (B2B) e-commerce. From the consumer buying behaviour perspective, the roles of agents in B2C e-commerce are: product brokering, merchant brokering, and negotiation. The applications of agents in B2B e-commerce are mainly in supply chain management. Mobile agents, evolutionary agents, and data-mining agents are some special techniques which can be applied in agent-mediated e-commerce. In addition, some technologies for implementation are briefly reviewed. Finally, we conclude this paper by discussions on the future directions of agent-mediated e-commerce. Received 14 September 2000 / Revised 13 January 2001 / Accepted in revised form 27 February 2001     

Fairness in secure computing protocols based on incentives

Cloud computing is based on utility and consumption of computer resources. To solve the security issues in cloud computing, secure computing protocols are often used. Recently, rational parties as a new kind of parties are proposed, who wish to maximize their utilities in secure computing protocols. The utility definitions in most previous rational secure computing protocols derive from prisoner’s dilemma game (PD game). In two-party rational computing protocols, parties decide to send their shares according to their utilities. Recently, we revisit the incentives for rational parties in secure computing protocols and give new utility definitions according to them. We find that the new utility definition is not similar to PD game any more. We discuss two-party and multi-party cases, respectively, and prove that parties have incentives to send their share to others. Furthermore, we also prove that parties can maximize their utilities in both cases.     

On entropy-based term weighting schemes for text categorization

In text categorization, Vector Space Model (VSM) has been widely used for representing documents, in which a document is represented by a vector of terms. Since different terms contribute to a document’s semantics in various degrees, a number of term weighting schemes have been proposed for VSM to improve text categorization performance. Much evidence shows that the performance of a term weighting scheme often varies across different text categorization tasks, while the mechanism underlying variability in a scheme’s performance remains unclear. Moreover, existing schemes often weight a term with respect to a category locally, without considering the global distribution of a term’s occurrences across all categories in a corpus. In this paper, we first systematically examine pros and cons of existing term weighting schemes in text categorization and explore the reasons why some schemes with sound theoretical bases, such as chi-square test and information gain, perform poorly in empirical evaluations. By measuring the concentration that a term distributes across all categories in a corpus, we then propose a series of entropy-based term weighting schemes to measure the distinguishing power of a term in text categorization. Through extensive experiments on five different datasets, the proposed term weighting schemes consistently outperform the state-of-the-art schemes. Moreover, our findings shed new light on how to choose and develop an effective term weighting scheme for a specific text categorization task.

     

Towards ubiquitous tourist service coordination and process integration: A collaborative travel agent system architecture with semantic web services

With the recent advances in Internet and mobile technologies, there are increasing demands for ubiquitous access to tourist information systems for service coordination and process integration. However, due to disparate tourist information and service resources such as airlines, hotels, tour operators, it is still difficult for tourists to use them effectively during their trips or even in the planning stage. Neither can current tourist portals assist tourists proactively. To overcome this problem, we propose a Collaborative Travel Agent System (CTAS) based on a scalable, flexible, and intelligent Multi-Agent Information System (MAIS) architecture for proactive aids to Internet and mobile users. We also employ Semantic Web technologies for effective organization of information resources and service processes. We formulate our MAIS architecture for CTAS further with agent clusters based on a case study of a large service-oriented travel agency. Agent clusters may comprise several types of agents to achieve the goals involved in the major processes of a tourist’s trip. We show how agents can make use of ontology from the Semantic Web to help tourists better plan, understand, and specify their requirements collaboratively with the CTAS. We further illustrate how this can be successfully implemented with Web service technologies to integrate disparate Internet tourist resources. To conclude, we discuss and evaluate our approach from different stakeholders’ perspectives.

Rational computing protocol based on fuzzy theory

Secure multi-party computing (SMPC) is often used to solve security problems in cloud computing. Rational SMPC is a kind of SMPC in the presence of rational parties, who wish to maximize their utilities. Previous works about rational SMPC only studied the security properties under complete information scenario, where parties’ types are common knowledge. However, parties in practical applications have private types, which is unknown to others. This scenario is called incomplete information. In this paper, rational parties are allowed to have private types, which affect their utilities. Previously, rational parties obtain expected utilities due to unknown private types under incomplete information scenario. However, rational parties prefer to obtain pure utilities in actual life. To solve this contradiction, we use fuzzy theory to confirm the private type of his opponent; then they execute the protocol as if they know the private types just like the execution under complete information scenario. Consequently, they obtain pure utilities other than expected utility. In addition, our protocol can reduce round complexity than previous ones. Consequently, it will improve the security level and efficiency of cloud computing.