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1、Towards Ubiquitous Tourist Service Coordination and Process Integration: a Collaborative Travel Agent System Architecture with Semantic Web Services+ A preliminary version of this paper has been presented at the IEEE 21st International Conference on Advanced Information Networking and Applications (
2、Yueh et al. 2007).Dickson K.W. Chiu1, Yves T.F. Yueh2, Ho-fung Leung 3, and Patrick C. K. Hung41Dickson Computer Systems, 7 Victory Avenue, Kowloon, Hong Kong (contact)2Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong3Department of Computer Sc
3、ience and Engineering, The Chinese University of Hong Kong, Hong Kong4Faculty of Business and Information Technology, University of Ontario Institute of Technology, Canadaemail: dicksonchiuieee.org, tfyueh, lhfcuhk.edu.hk, patrick.hunguoit.caABSTRACTWith the recent advances in Internet and mobile te
4、chnologies, 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
5、 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) archite
6、cture 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
7、agency. Agent clusters may comprise several types of agents to achieve the goals involved in the major processes of a tourists 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 CTA
8、S. 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.KeywordsTourist information system, ubiquitous computing, coll
9、aborative process integration, multi-agent information system, Semantic Web services, ontology1. INTRODUCTIONTourism has become the worlds largest industry and has experienced consistent growth over the recent years. The World Tourism Organization (2006) predicts that by 2020, tourist arrivals aroun
10、d the world will increase over 200%. Tourism has become a highly competitive business all over the world. Competitive advantage is increasingly driven by the advancement of information technology and innovation. Currently, the Internet is the primary source of tourist destination information for tra
11、velers. With the recent advances in hardware and software technologies, the Internet is quickly evolving towards wireless adoption (Lin & Chlamtac 2000). New mobile applications running on these devices provide users with easy access to remote services available anytime and anywhere, and will soon t
12、ake advantage of the ubiquity of wireless networking in order to create new virtual worlds (Lyytinen & Yoo 2002). Besides, intelligent software agents can run on these devices and can provide personalized assistance to tourists during their trip. Together with traditional information agents such as
13、hotel broker agents, tour planning agents, and other disparate tourist resources, they form a Multi-Agent Information System (MAIS) (Chiu et al. 2005) for collaborative and intelligent assistance to tourists.At the same time, Semantic Web technologies (Fensel et al. 2001) have been maturing to make
14、e-commerce interactions more flexible and automated. Ontology defines the terms used to present a domain of knowledge that is shared by people, databases, and applications. In particular, ontology encodes knowledge, possibly spanning different domains as well as describes the relationships among the
15、m. Currently, ontology is actively being developed in various business domains. The Semantic Web thus provides explicit meaning to the information available on the Web for automated processing and information integration based on the underlying ontology. As such, we propose to expand tourist coordin
16、ation and integration towards ubiquitous support by employing all the above-mentioned technologies. We call this a Collaborative Travel Agent System (CTAS). The main challenge of such a CTAS is to provide an effective coordination and integration of disparate information and service resources anytim
17、e, anywhere; as well as the provision of personalized assistance and automation to the tourists, each having different preferences and support requirements that often being changed during the trip. With the help of ontology, the CTAS can help tourists better understand and guide them to specify thei
18、r needs and preferences collaboratively, so that the appropriate information and services resources could be located from the Semantic Web (Chiu et al. 2005).Because scalability and flexibility, tourists cannot be flexibly assisted in a centralized manner. The assistance of increasingly powerful mob
19、ile devices becomes the enabling technologies. Under individuals instructions and preferences, intelligent software agents within CTAS can be delegated to help recommend, plan, and negotiate personalized activities and schedules, thereby augmenting the users decisions collaboratively. As such, we pr
20、opose a scalable, flexible, and intelligent multi-agent information system (MAIS) infrastructure for a CTAS with agent clusters for tourist service coordination and integration. Each agent cluster comprises several types of agents to achieve the goals of the major tasks of a tourists trip, such as,
21、information gathering, preference matchmaking, planning, service brokering, commuting, and mobile servicing. The agents also make use of ontology from the Semantic Web to search information and make recommendations to the tourists. Further, we detail how this can be effectively implemented with Web
22、service and Semantic Web technologies, integrating disparate Internet tourist resources.The remainder of this paper is organized as follows. Section 2 introduces background and related work. Section 3 explains an overview of an MAIS and a development methodology for such a CTAS. Section 4 details ho
23、w our MAIS architecture and implementation framework can meet the tourists needs. Section 5 concludes the paper by discussing the applicability of our approach in different stakeholders perspectives in collaboration with our plans for further research.2. BACKGROUND AND RELATED WORKWe have not found
24、any similar work on CTAS with this holistic approach and the deployment of MAIS for this purpose. Traditionally, travelers often have to manually visit multiple independent Web sites or use traditional means such as telephone, fax, or even in one-on-one consultation to plan their trips. This require
25、s tourists to register their personal information multiple times, spend hours or days waiting for a response or confirmation, and make multiple payments by credit cards. This could be a tedious and error-prone process, especially when a tourist has a complex plan or wants to search as much informati
26、on as possible before making a decision. Tourists are discouraged with the lack of functionality via traditional ways. They are demanding the ability to create, manage, and update itineraries. Buhalis and Licata (2002) discuss the future of e-tourism intermediaries while Rayman-Bacchus and Molina (2
27、001) predict the business issues and trends of Internet-based tourism. However, both groups did not focus on a tourists requirements or a software development perspective. Intelligent agents are considered as autonomous entities with abilities to execute tasks independently. He et al. (2003) present
28、 a comprehensive survey on agent-mediated e-commerce. An agent should be proactive and subject to personalization, with a high degree of autonomy, assisting the users collaboration with other information systems. In particular, due to the different limitations on different platforms, users may need
29、different options in agent delegation. Prior research studies usually focus on the technical issues in a domain-specific application. For example, Lo and Kersten (1999) present an integrated negotiation environment by using software agent technologies for supporting negotiators but they did not supp
30、ort their operations on different platforms. The emergence of MAIS dates back to Sycara and Zeng (1996), who discuss the issues in the collaboration of multiple intelligent software agents. In general, an MAIS provides a platform to bring multiple types of expertise for any decision making (Luo et a
31、l. 2002). Lin et al. (1998) present an MAIS with four main components: agents, tasks, organizations, and information infrastructure for modeling the order fulfillment process in a supply chain network. Lin and Pai (2000) discuss the implementation of MAIS based on a multi-agent simulation platform c
32、alled Swarm. Further, Shakshuki et al. (2000) present an MAIS architecture, in which each agent is autonomous, collaborative, coordinated, intelligent, rational, and able to communicate with other agents to fulfill the users needs. Choy et al. (2003) propose the use of mobile agents to aid in meetin
33、g the critical requirement of universal access in an efficient manner. Wegner et al. (1996) present a multi-agent collaboration algorithm using the concepts of belief, desire, and intention (BDI). Fraile et al. (1999) present a negotiation, collaboration, and cooperation model for supporting a team
34、of distributed agents to achieve the goals of assembly tasks. Chiu et al. (2003) also propose the use of a three-tier view based methodology for adapting human-agent collaborative systems for multiple mobile platforms. In order to ensure interoperability of MAIS, standardization on different levels
35、is highly required (Gerst 2003). Thus, based on all these prior works, our proposed MAIS framework adapts and coordinates collaborative agents with standardized mobile and Semantic Web technologies for a CTAS.Researches in mobile workforce management (MWM) motivate this research work. Guido et al. (
36、1998) point out some MWM issues and evaluation criteria, but the details are no longer up-to-date because of the fast evolving technologies. Jing et al. (2000) prototypes a system called WHAM (workflow enhancements for mobility) to support mobile workforce and applications in a collaborative workflo
37、w environment, with emphasis on a two-level (central and local) resource management approach. Both groups did not consider distributed agent based, flexible multi-platform business process interactions, or any collaboration support. There are many similarities in MWM and CTAS, such as mobility of th
38、e users, disparate information and service resources, and collaborative decision requirements. However, user-to-user collaboration (Bafoutsou & Mentzas 2001), being a foundation of MWM, focuses on the communication, coordination, and cooperation for a set of geographically dispersed users. That is n
39、ormally less important for tourists, unless under situations where phone calls to tourist consultants are inadequate. Nevertheless, as workforce members normally access information from their own enterprise, the coordination and integration problem in CTAS is much more challenging, because tourist r
40、esources are heterogeneous and belong to different organizations. Secondly, planning in a CTAS is much more difficult because workforce members have to follow management instructions while tourists may often freely change their preferences and plans. In addition, the duration of a tour plan is usual
41、ly much longer. Another foundation of CTAS is meeting scheduling because the related algorithms can be used for booking. There are some commercial products but they are just calendars or simple diaries with special features, such as availability checkers and meeting reminders (Garrido et al. 1996).
42、Shitani et al. (2000) highlight a negotiation approach among agents for a distributed meeting scheduler based on the multi-attribute utility theory. Van Lamsweerde et al. (1995) discuss goal-directed elaboration of requirements for a meeting scheduler, but do not discuss any implementation framework
43、s. Sandip (1997) summarizes an agent based system for automated distribution meeting scheduler, but the system is not based on the BDI agent architecture. All these systems cannot support manual interactions in the decision process or any mobile support issues. More specific to tourism, Yeung et al.
44、 (1998) present a multi-agent based tourism kiosk for Hong Kong based on Internet information categories such as hotels, shopping centers, and cinemas with the Knowledge Query and Manipulation Language (KQML) as the agent communication language. Poslad et al. (2001) outline an MAIS approach for the
45、creation of user-friendly mobile services personalized for tourism in the CRUMPET project, aiming to provide new information delivery services for a far more heterogeneous tourist population. Lin and Kuo (2002) describe a collaborative multi-agent negotiation system for electronic commerce based on
46、mobile agents with an example based on tourism application.Although Semantic Web technologies are maturing, ontology standards are still forming (Fensel et al. 2001). Challenges remain for reusing available ontological information, and researchers focus on information integration. In the past years,
47、 there are different standardized languages proposed. For example, DARPA Agent Markup Language (Lacy 2005) is a language created by DARPA as an ontology language based upon the Resources Description Framework. DAML-S was designed to serve as the basis for representing descriptions of inverses, unamb
48、iguous properties, unique properties, lists, restrictions, cardinalities, pair-wise disjoint lists, and data types. The World Wide Web Consortium (W3C) has recently adopted the Web Ontology Language (OWL) (Lacy 2005) in an eXtended Markup Language (XML) format for defining Web ontologies. OWL ontolo
49、gy includes descriptions of classes, properties, and their instances, as well as formal semantics for deriving logical consequences in entailments. Bullock and Goble (1998) propose the application of a description logic based semantic hypermedia system for tourism. Stabb et al. (2002) point out the possible use of semantics for intelligent systems for tourism as well as the importance of catching user needs and decision styles, but without details in how to achieve it. Recently, we have proposed an MAIS
