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     

Better in 3D? An Empirical Investigation of User Satisfaction and Preferences Concerning Two-Dimensional and Three-Dimensional Product Representations in Business-to-Consumer E-Commerce

This study aimed at determining the user preferences and satisfaction concerning three-dimensional product representations in business-to-consumer electronic commerce. An experiment was designed and conducted on 20 college-age electronic shoppers to determine the user preference and satisfaction issues concerning two-dimensional (2D), three-dimensional low-interaction (3DL), and three-dimensional high-interaction (3DH) product representations. A valid and reliable survey with 0.89 Cronbach's alpha internal reliability coefficient was presented to participants after they completed tasks on each product representation type. Results indicated that participants found the 3D representations (both low and high interaction) more detailed, easier and more fun to use, more accurate, and carrying more information than 2D representations. It was concluded that 3D representations in general resulted in higher satisfaction for the shoppers. Future studies can be conducted to determine the business aspects of different product representations as well as human information visualization and processing issues relating to product representations in electronic commerce.     

Management of Experience Data for Rapid Adaptation to New Preferences Based on Bayesian Significance Evaluation

This paper presents a rapid adaptation method of behavior preference based on Bayesian significance evaluation of experience data. Rapid adaptation to user preferences cannot be achieved when data from every process cycle are used for learning because significant data are not differentiated from insignificant data. We propose a method to solve this problem by selecting significant data for the learning based on the change in the degree of confidence of the behavior decision. A small change in the degree of confidence can be regarded as reflecting insignificant data for learning, so that data can be discarded. Accordingly, the system can avoid having to store too frequent experience data and the robot can adapt rapidly to changes in the user preferences. We discuss the experimental results of two experiments in which user preference changes among three preferences on a mobile robot. In an interactive experiments with a robot following its user preference with a data frequency of 5 Hz, the robot could adapt to a new preference within 3.75 s.     

Complexity–Based Induction

A central problem in inductive logic programming is theory evaluation. Without some sort of preference criterion, any two theories that explain a set of examples are equally acceptable. This paper presents a scheme for evaluating alternative inductive theories based on an objective preference criterion. It strives to extract maximal redundancy from examples, transforming structure into randomness. A major strength of the method is its application to learning problems where negative examples of concepts are scarce or unavailable. A new measure called model complexity is introduced, and its use is illustrated and compared with a proof complexity measure on relational learning tasks. The complementarity of model and proof complexity parallels that of model and proof–theoretic semantics. Model complexity, where applicable, seems to be an appropriate measure for evaluating inductive logic theories.     

Logic programming with solution preferences

Preference logic programming (PLP) is an extension of logic programming for declaratively specifying problems requiring optimization or comparison and selection among alternative solutions to a query. PLP essentially separates the programming of a problem itself from the criteria specification of its solution selection. In this paper we present a declarative method for specifying preference logic programs. The method introduces a precise formalization for the syntax and semantics of PLP. The syntax of a preference logic program contains two disjoint sets of definite clauses, separating a core program specifying a general computational problem from its preference rules for optimization; the semantics of PLP is given based on the Herbrand model and fixed point theory, where how preferences affects the least Herbrand model of a logic program is interpreted as a sequence of meta-level mapping operations. In addition, we present an operational semantics based on a new resolution strategy and a memoized recursive algorithm for computing strictly stratified logic programs with well-formed preferences, and further show that the operational semantics of such a preference logic program is consistent to its declarative semantics.     

On agent-mediated electronic commerce

This paper surveys and analyzes the state of the art of agent-mediated electronic commerce (e-commerce), concentrating particularly on the business-to-consumer (B2C) and business-to-business (B2B) aspects. From the consumer buying behavior perspective, agents are being used in the following activities: need identification, product brokering, buyer coalition formation, merchant brokering, and negotiation. The roles of agents in B2B e-commerce are discussed through the business-to-business transaction model that identifies agents as being employed in partnership formation, brokering, and negotiation. Having identified the roles for agents in B2C and B2B e-commerce, some of the key underpinning technologies of this vision are highlighted. Finally, we conclude by discussing the future directions and potential impediments to the wide-scale adoption of agent-mediated e-commerce.     

基于Agent的个性化电子商务系统研究

该文介绍了一种基于Agent的带有个性化服务的电子商务系统。通过买方Agent和卖方Agent带有主动性和智能性的活动,能有效地对用户(顾客和销售商)行为提供建议与支持,从而保证了用户决策的正确性。该文着重对个体之间的联系机制作了一番详细介绍。此外,卖方Agent还能对顾客的个性进行动态描述,从而实现了个性化的服务,大大增加了商品对顾客的吸引力,有利于电子商务的顺利进行。     

First-order jk-clausal theories are PAC-learnable

We present positive PAC-learning results for the nonmonotonic inductive logic programming setting. In particular, we show that first-order range-restricted clausal theories that consist of clauses with up to k literals of size at most j each are polynomial-sample polynomial-time PAC-learnable with one-sided error from positive examples only. In our framework, concepts are clausal theories and examples are finite interpretations. We discuss the problems encountered when learning theories which only have infinite nontrivial models and propose a way to avoid these problems using a representation change called flattening. Finally, we compare our results to PAC-learnability results for the normal inductive logic programming setting.     

Human factors in the design of a personalizable EPG: preference-indication strategies, habit watching and trust

The article describes a user study to support the design of a personalizable EPG (Electronic TV Programme Guide), and of its user interface for editing user preference profiles regarding TV channels and categories. This study focuses on issues related to users' behaviour and perceptions regarding a personalizable EPG, and especially regarding the personalization process. Users were presented with a paper-and-pencil procedure to indicate their TV viewing preferences, as well as with an electronic version. Their strategies were observed and their opinions asked, especially on trusting a system that uses this data. Moreover, their viewing behaviour was monitored over a period of two weeks, and recommendations for the second week were based on the viewing behaviour of the first week. The results indicate that users are reasonably comfortable and consistent in describing their viewing habits in terms of preferences, both for the paper-and-pencil and electronic preference-indication procedures, but that fine tuning this profile on the basis of habit watching would considerably improve the efficacy of the recommendations. It was found that subjects trust the system with the task of preselecting their TV programmes on the basis of their preference indications, although they are not sure whether a habit-watching system would be capable of following their changing habits over time.     

Strategies for avoiding preference profiling in agent-based e-commerce environments

Agent-based electronic commerce is known to offer many advantages to users. However, very few studies have been devoted to deal with privacy issues in this domain. Privacy is of great concern and preserving users’ privacy plays a crucial role to promote their trust in agent-based technologies. In this paper, we focus on preference profiling, which is a well-known threat to users’ privacy. Specifically, we review strategies for customers’ agents to prevent seller agents from obtaining accurate preference profiles of the former group by using data mining techniques. We experimentally show the efficacy of each of these strategies and discuss their suitability in different situations. Our experimental results show that customers can improve their privacy notably with these strategies.     

Learning opponent’s preferences for effective negotiation: an approach based on concept learning

We consider automated negotiation as a process carried out by software agents to reach a consensus. To automate negotiation, we expect agents to understand their user’s preferences, generate offers that will satisfy their user, and decide whether counter offers are satisfactory. For this purpose, a crucial aspect is the treatment of preferences. An agent not only needs to understand its own user’s preferences, but also its opponent’s preferences so that agreements can be reached. Accordingly, this paper proposes a learning algorithm that can be used by a producer during negotiation to understand consumer’s needs and to offer services that respect consumer’s preferences. Our proposed algorithm is based on inductive learning but also incorporates the idea of revision. Thus, as the negotiation proceeds, a producer can revise its idea of the consumer’s preferences. The learning is enhanced with the use of ontologies so that similar service requests can be identified and treated similarly. Further, the algorithm is targeted to learning both conjunctive as well as disjunctive preferences. Hence, even if the consumer’s preferences are specified in complex ways, our algorithm can learn and guide the producer to create well-targeted offers. Further, our algorithm can detect whether some preferences cannot be satisfied early and thus consensus cannot be reached. Our experimental results show that the producer using our learning algorithm negotiates faster and more successfully with customers compared to several other algorithms.     

Logical formalisms for representing bipolar preferences

Bipolar preferences distinguish between negative preferences inducing what is acceptable by complementation and positive preferences representing what is really satisfactory. This article provides a review of the main logics for preference representation. Representing preferences in a bipolar logical way has the advantage of enabling us to reason about them, while increasing their expressive power in a cognitively meaningful way. In the article, we first focus on the possibilistic logic setting and then discuss two other logics: qualitative choice logic and penalty logic. Finally, an application of bipolar preferences querying systems is outlined. © 2008 Wiley Periodicals, Inc.     

Scaling Up Inductive Logic Programming by Learning from Interpretations

When comparing inductive logic programming (ILP) and attribute-value learning techniques, there is a trade-off between expressive power and efficiency. Inductive logic programming techniques are typically more expressive but also less efficient. Therefore, the data sets handled by current inductive logic programming systems are small according to general standards within the data mining community. The main source of inefficiency lies in the assumption that several examples may be related to each other, so they cannot be handled independently.Within the learning from interpretations framework for inductive logic programming this assumption is unnecessary, which allows to scale up existing ILP algorithms. In this paper we explain this learning setting in the context of relational databases. We relate the setting to propositional data mining and to the classical ILP setting, and show that learning from interpretations corresponds to learning from multiple relations and thus extends the expressiveness of propositional learning, while maintaining its efficiency to a large extent (which is not the case in the classical ILP setting).As a case study, we present two alternative implementations of the ILP system TILDE (Top-down Induction of Logical DEcision trees): TILDEclassic, which loads all data in main memory, and TILDELDS, which loads the examples one by one. We experimentally compare the implementations, showing TILDELDS can handle large data sets (in the order of 100,000 examples or 100 MB) and indeed scales up linearly in the number of examples.     

Exploring agent support at the user interface in e-commerce applications

In this paper, we present some of the results from our ongoing research work in the area of ‘agent support’ for electronic commerce, particularly at the user interface level. Our goal is to provide intelligent agents to assist both the consumers and the vendors in an electronic shopping environment. Users with a wide variety of different needs are expected to use the electronic shopping application and their expectations about the interface could vary a lot. Traditional studies of user interface technology have shown the existence of a ‘gap’ between what the user interface actually lets the users do and the users’ expectations. Agent technology, in the form of personalized user interface agents, can help to narrow this gap. Such agents can be used to give a personalized service to the user by knowing the user’s preferences. By doing so, they can assist in the various stages of the users’ shopping process, provide tailored product recommendations by filtering information on behalf of their users and reduce the information overload. From a vendor’s perspective, a software sales agent could be used for price negotiation with the consumer. Such agents would give the flexibility offered by negotiation without the burden of having to provide human presence to an online store to handle such negotiations. Published online: 25 July 2001     

Hohfeld in cyberspace and other applications of normative reasoning in agent technology

Two areas of importance for agents and multiagent systems are investigated: design of agent programming languages, and design of agent communication languages. The paper contributes in the above mentioned areas by demonstrating improved or novel applications for deontic logic and normative reasoning. Examples are taken from computer-supported cooperative work, and electronic commerce.     

Developing recommender systems with the consideration of product profitability for sellers

In electronic commerce web sites, recommender systems are popularly being employed to help customers in selecting suitable products to meet their personal needs. These systems learn about user preferences over time and automatically suggest products that fit the learned model of user preferences. Traditionally, recommendations are provided to customers depending on purchase probability and customers’ preferences, without considering the profitability factor for sellers. This study attempts to integrate the profitability factor into the traditional recommender systems. Based on this consideration, we propose two profitability-based recommender systems called CPPRS (Convenience plus Profitability Perspective Recommender System) and HPRS (Hybrid Perspective Recommender System). Moreover, comparisons between our proposed systems (considering both purchase probability and profitability) and traditional systems (emphasizing an individual’s preference) are made to clarify the advantages and disadvantages of these systems in terms of recommendation accuracy and/or profit from cross-selling. The experimental results show that the proposed HPRS can increase profit from cross-selling without losing recommendation accuracy.     

A novel method for coevolving PS-optimizing negotiation strategies using improved diversity controlling EDAs

In agent-mediated negotiation systems, the majority of the research focused on finding negotiation strategies for optimizing price only. However, in negotiation systems with time constraints (e.g., resource negotiations for Grid and Cloud computing), it is crucial to optimize either or both price and negotiation speed based on preferences of participants for improving efficiency and increasing utilization. To this end, this work presents the design and implementation of negotiation agents that can optimize both price and negotiation speed (for the given preference settings of these parameters) under a negotiation setting of complete information. Then, to support negotiations with incomplete information, this work deals with the problem of finding effective negotiation strategies of agents by using coevolutionary learning, which results in optimal negotiation outcomes. In the coevolutionary learning method used here, two types of estimation of distribution algorithms (EDAs) such as conventional EDAs (S-EDAs) and novel improved dynamic diversity controlling EDAs (ID²C-EDAs) were adopted for comparative studies. A series of experiments were conducted to evaluate the performance for coevolving effective negotiation strategies using the EDAs. In the experiments, each agent adopts three representative preference criteria: (1) placing more emphasis on optimizing more price, (2) placing equal emphasis on optimizing exact price and speed and (3) placing more emphasis on optimizing more speed. Experimental results demonstrate the effectiveness of the coevolutionary learning adopting ID²C-EDAs because it generally coevolved effective converged negotiation strategies (close to the optimum) while the coevolutionary learning adopting S-EDAs often failed to coevolve such strategies within a reasonable number of generations.     

Learning constraints in spreadsheets and tabular data

Spreadsheets, comma separated value files and other tabular data representations are in wide use today. However, writing, maintaining and identifying good formulas for tabular data and spreadsheets can be time-consuming and error-prone. We investigate the automatic learning of constraints (formulas and relations) in raw tabular data in an unsupervised way. We represent common spreadsheet formulas and relations through predicates and expressions whose arguments must satisfy the inherent properties of the constraint. The challenge is to automatically infer the set of constraints present in the data, without labeled examples or user feedback. We propose a two-stage generate and test method where the first stage uses constraint solving techniques to efficiently reduce the number of candidates, based on the predicate signatures. Our approach takes inspiration from inductive logic programming, constraint learning and constraint satisfaction. We show that we are able to accurately discover constraints in spreadsheets from various sources.