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ABSTRACT

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.

Keywords

Tourist information system, ubiquitous computing, collaborative process integration, multi-agent information system, Semantic Web services, ontology

1. INTRODUCTION

Tourism has become the world’s largest industry and has experienced consistent growth over the recent years. The World Tourism Organization (2006) predicts that by 2020, tourist arrivals around 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 travelers.

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 take 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 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 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 them. 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 coordination 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 anytime, 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 their 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 mobile devices becomes the enabling technologies. Under individual’s instructions and preferences, intelligent software agents within CTAS can be delegated to help recommend, plan, and negotiate personalized activities and schedules, thereby augmenting the user’s decisions collaboratively. As such, we propose 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 tourist’s trip, such as, 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 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 how 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 Work

We have not found 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 requires 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 information 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 (2001) predict the business issues and trends of Internet-based tourism. However, both groups did not focus on a tourist’s requirements or a software development perspective.

Intelligent agents are considered as autonomous entities with abilities to execute tasks independently. He et al. (2003) present 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 user’s collaboration with other information systems. In particular, due to the different limitations on different platforms, users may need 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 support 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 al. 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 called 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 meeting 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 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 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. (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 workflow 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 the 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 normally 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 resources 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 usually 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). 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 frameworks. 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. (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 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 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, 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, unambiguous 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 ontology 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 framework for MWM (Chiu et al. 2005) with an in-depth study on how to integrate these technologies for a scalable MWM MAIS. We have also studied the use of agents (Chiu et al. 2005c) in the construction of a mobile route advisory system. However, these works have not yet considered the application of ontology. On the other hand, we have demonstrated the use of ontology to help users specify their requirements collaboratively for matchmaking and negotiation (Chiu et al. 2005d). These works provide the foundations that motivate this paper.

In summary, none of the existing work considers a MAIS infrastructure for a CTAS with a holistic and flexible approach for the coordination and integration of information and services. Scattered efforts have looked into sub-problems but these efforts are inadequate for an integrated solution. There is neither any work describing a concrete implementation framework and methodology by means of a portfolio of contemporary MAIS, Semantic Web, Web services, and mobile technologies (and particularly for m-tourism).

3.MAIS Infrastructure

An MAIS provides an infrastructure for multiple agents as well as users to exchange information under a pre-defined collaboration protocol. Agents in the MAIS are distributed and autonomous; each carrying out actions based on their own strategies. In this section, we explain our MAIS infrastructure based on a BDI framework. Then, we summarized our methodology for design and analysis of an MAIS for the CTAS.

3.1 MAIS Layered Infrastructure for a CTAS

Personal Assistance

Information / Service Resources

Planning

Tourist Information System

Multi-agent Information System (MAIS)

BDI Agents

Ontology

Collaboration Protocol

Web-based 3-tier Implementation Architecture

         

Figure 1 summarizes our layered infrastructure for a CTAS. Conventionally, tourism information and services are accessible manually through the Web or through traditional means such as telephone, fax, or even in person. This could be a tedious and error-prone process, especially when a tourist has a complex plan or wants to search as much information as possible. Furthermore, agents can provide adequate computerized personal assistance to individual travelers over the Web and facilitate the protection of privacy and security (Chiu et al. 2004). These agents, acting on behalf of their delegators, collaborate through both wired and wireless Internet, forming a dynamic MAIS over the Web. The Believe-Desire-Intention (BDI) framework is a well-established computational model for deliberative intelligent agents. A BDI agent constantly monitors the changes in the environment and updates its information accordingly. Ontology help generate possible goals reflecting a tourist’s requirements, from which intentions to be pursued are identified and a sequence of actions will be performed to achieve the intentions in consideration of the tourist’s preferences. BDI agents are proactive by taking initiatives to achieve their goals, yet adaptive by reacting to the changes in the environment in a timely manner. They can also accumulate experience from previous interactions with the environment and other agents. The BDI model can also solve for acceptable tourist arrangements and even a tour plan by mapping constraints generated to the well-known paradigm of the Constraint Satisfaction Problem (CSP)(Tsang 1993), where efficient solvers are available.

Internet applications are generally developed with a three-tier architecture comprising front, application, and data tiers. Though the use of three-tier architecture in the agent community is relatively new, it is a well-accepted pattern to provide flexibility in each tier (Chiu et al. 2003) and is absolutely required in the expansion of e-collaboration support. Such flexibility is particularly important to the front tier, which often involves the support of different solutions on multiple platforms. In our architecture, users may either interact manually with other collaborators or delegate an agent to make decision on behalf of their client. Thus, users without agent support can still participate through flexible user interfaces for multiple platforms.

As the MAIS architecture involves a large number of autonomous agents, and each agent has its own architecture-specific features such as strategy to find another agent, query preference, advertisement, etc. (Chiu et al. 2005; Choy et al. 2003), the problem of such architecture is that locating and collaborating with agents in the agent communities become difficult. In order to interact, agents must first know of each other’s presence and location in the MAIS. Since the MAIS is open for agents to enter or leave at anytime, it is impossible for programming the agent under the assumption that they know all of their peers. A possible way is to introduce an agent discovery mechanism for agents to find each other dynamically, say, through directory services. Dynamic discovery mechanism requires a language to express the capabilities of services, and the specification of a matching algorithm between service advertisements and service requests that recognizes when a request matches an advertisement.

Ontologies constitute an essential ingredient for discovery. They provide the means to represent different aspects of agents and the basic mechanisms for the match between agents’ requests and advertisements. Advertisements are descriptions of the services provided by the agent and used by the middle agents to identify which agent provides a specified service (Garrido et al. 1996; Gerst 2003). Once the provider is found, the requesting agent still needs to query the provider to obtain a service. We adopt OWL (Lacy 2005) as a service description language as it provides a semantically based view of Web services. This spans from the abstract description to the specification of the service interaction protocol, to the actual messages that it exchanges with other Web services or agents.  Figure 2 shows a typical agent collaboration process in a sequence diagram of the Unified Modeling Language (UML).

In MAIS, knowledge and capabilities are distributed across the agents in a way that no single agent has a complete knowledge of the whole MAIS; and no single agent can perform all the operations that can be performed by all the other agents. Despite their limited knowledge and capabilities, agents are able to ask other agents to perform some actions or to provide information. Therefore, the ability to communicate with other agents is one of the central collaboration skills of any agent in the MAIS. The inter-agent communications can be performed by adopting the speech-act theory such as FIPA ACL. The following example shows such a message:

Ontologies provide the tools to interpret the content of the message. For example, the speaker may encode its message using the OWL ontology shown in Figure 3. As the example shows, ontology, by providing a shared conceptualization of the domain, effectively contributes to agent communication by providing a language and dictionary that can be used to express concepts and statements about the domain of the agents. Furthermore, those languages and dictionaries can be standardized and shared by all the agents in the MAIS.

3.2 MAIS Analysis and Design Methodology for a CTAS

Based on the framework of Chiu et al. (2005), we adapt the methodology for MWM MAIS to a CTAS in this study. We also advocate the system analysis and design methodology to be carried out in two parts. Part 1 deals with the overall architectural design. That is, we have to analyze the high-level requirements and formulate an overall MAIS infrastructure for the collaboration and integration aspects required by a CTAS. The context of a CTAS has been studied partially before and is therefore the focus of this paper. The steps for part 1 are as follows:

Identify different categories of services and objectives for the tourists with the help of ontologies.

If existing ontologies are inadequate, augment them with the specific concepts required by the CTAS.

Identify different types of process of the tourist that the CTAS supports.

For each process, identify the major agent to represent each of the process types and then the interactions among the processes based on the CTAS requirements.

Further identify minor agents that assist the major agents to carry out these functionalities. As a result, clusters of different types of agents (instead of a single monolithic pool of agents) constitute the MAIS. This is required because of the complexity of a CTAS.

Identify the interactions required for the collaboration of each minor agent type.

Design and define the basic logics for all these agents.

Identify the (mobile) platforms to be supported and where to host different types of agents. See if any adaptation is required.

Only after the successful high-level requirement studies and the design of the overall architecture can we proceed to the next part. Part 2 deals with the detail design of agents and the methodology has been preliminarily studied by Chiu et al. (2004). It should be noted that the actual detailed design for each types of agents in the CTAS has high potentials for further research because of its complexities and emerging adoptions. Here, we summarize the steps as follows for conveying a more complete picture of the required efforts:

Design and adapt the user interface required for users to input their personal preferences. Customize displays to individual user’s interacting platforms.

Determine how user preferences are mapped into constraints and exchange them in a standardized format.

Consider implementing automated decision support with agents. Identify the stimulus, collaboration parameters, and output actions to be performed by a BDI agent.

Partition the collaboration parameters into three data sets: belief, desire, and intention. Formulate a data sub-schema for each of these data sets. Implement the schema at the data tier.

Derive transformations amongst the three data sets.

Implement these transformations at the application tier.

Implement the external interfaces among all involved agents and systems with Web services.

Enhance the performance and intelligence of the agents with various heuristics gathering during the testing and pilot phase of the project.

4.System Architecture and Implementation framework

This study is based on the requirements and experiences obtained from a large regional traveling service provider specialized in self-service tour packages, i.e., targets for tourists who buy air-tickets and book hotels from them but travel on their own. Their main value-added service is to provide (pointers to) tourist information as well as consultancy in tour planning. In the first phase, the traveling service provider aims at providing automatic or at least computer-assisted consultancy to their clients in order to cut costs. The second phase aims at providing mobile assistance to the tourists, integrating the services at each local office over the region, thereby increasing commission income through services, such as ticketing and referring clients to shops.  The overall object is to improve customer relationships through better service quality via the CTAS.

Summarizing the overall requirements of typical tourists in the context of the case study, we identify the following main CTAS process types and their corresponding main agents. Figure 4 depicts our proposed CTAS architecture.

l  The Ontology Maintenance and Search Processes (see Section 4.2) concern with a tourist’s inquiry and search for relevant tourist information.

l  The Requirement and Preference Management Processes (see Section 4.3) concern with the elicitation and specification of a tourist’s requirements and preferences as well as their updates.

l  The Package Planning Processes (see Section 4.4) concern with the formulation of tour plans, focusing on the itinerary formulation and the possible connection transportations (particularly flights) and hotel booking.

l  The Local Tour Planning Processes (see Section 4.5) concern with the tour plan within a certain city or region with local transportations and the recommendation of local services.

l  The Tourist Assistant Processes (Section 4.6) concern with the user interface particularly relating to the mobile devices.

4.1     Ontology Maintenance and Search Processes

The tourism ontology provides a way of viewing the world of tourism. It organizes tourism-related information and concepts. The ontology allows achieving interoperability through the use of a shared vocabulary and meanings for terms with respect to other terms. So, ontology is the central mechanism of the CTAS.

Table 1. Key agents in the Ontology Maintenances and Search Agent Cluster

Agents

Functions

Flight

Search for flights (traditional way)

Hotel

Search for hotels (traditional way)

Ontology Search

Search for information from the centralized ontology in the knowledge base

Web Crawler

Expand the ontology and knowledge by grapping information from the Web

 

Table 1 summarizes the key agents for these processes. Ontology is the central mechanism of a CTAS. Tourist information and service resources are classified according to a common ontology of the CTAS, so that all the agents in the CTAS have a common basis for searching, interpretation, and reasoning.  How each type of agents can make good use of the ontology could be different and is explained in each of the sub-sections below. Ontology search agents are responsible for searching this centralized ontology because other agents may reside in different locations, particularly with the mobile tourists during their trips. They can also collaborate with traditional hotel agents, flight agents, etc., to search for more available information.

As currently there is no existing commonly adopted ontology for tourism, the establishment of such ontology needs the expertise of experienced tourist consultants to incorporate the categorizations from different sub-domains, such as locations, tourist attractions, events, hotels, etc. Web crawler agents are relevant technologies that can expand the number of available tourist information and service resources. Classifying them into appropriate categories could be automated by queries into existing databases but often have to agent-assisted with confirmations from consultants in order to be accurate. As the process of establishing the ontology inevitably involves major human efforts, each local office should contribute to the part of ontology related to their location according to the definitions provided by the head office so that local expertise and knowledge can be effectively integrated. Due to the same reason, we call for public efforts from the tourism ministers to speed up this process and to establish an industry-wide ontology instead. How to establish ontology is another big research topic and not a focus of this paper but in our future work agenda.

So, to start off this project, a partially completed ontology for tourism was created using the Protégé tool  in OWL. It is a very time-consuming task in establishing such ontology as it needs the expertise of experienced tourist consultants to find out information about real tourism activities and infrastructures, then categorized from different sub-domains, such as locations, sights, hotels, activities, and events, etc. Besides, such information needs to be fed into the knowledge base.

Due to the costly expenses in building an ontology of tourism from scratch manually, we first extract information and service resources from the existing Web pages. Web crawler agents, with the use of semantically annotations, are related technologies to extract the necessary information. The differences among the data presentation in distinct tourism Web pages, such as currencies, time units, keywords, etc., can be resolved through automation by agents. We adopted WebXcript (Chiu et al. 2005b) to integrate the legacy tourist Websites into the Web services. The Ontology Maintenance and Search Agent has no option to change the access methods of the existing agency Websites over the Internet, and often cannot request for the provision of a programmatic interface. Therefore, a Web crawler simulates an interactive user accessing the target Websites. Besides, Web crawlers can also communicate with the service providers with provision of Web services interface. The extracted information will be stored in ontology or knowledge database for further reference. Figure 5 demonstrates an example script of WebXcript that gathers information over the Internet for hotel prices.

As the process of establishing the ontology involves a huge amount of human effort, the contribution from local travel agency, expertise, and public efforts is very important. However, the maintenance of the ontology can be assisted by agents, which can be run as a service in the background. Information agents can monitor the Web resources through their recorded Universal Resource Locators (URLs), and the Web crawler can search for new resources and store them to the knowledge database (Cheong et. al 2007). Moreover, the ontology maintenance and search processes can integrate with Semantic Web services of other partners or service providers. New or updated information can be forwarded to the tourists through alert agents (see section 4.6) in CTAS that are interested or affected. The following summarizes the services implementing the key agent processes.

Service Name: OntologySearchService

Purpose: searches information from the centralized ontology in the knowledge base. The ontology may be contributed by the public or from the result of a Web crawler.

Input: Search type, ontology, criteria, maximum number of search results

Output: Search results

Service Name: WebXcriptExtractionService

Purpose: a Web crawler that expands the ontology and knowledge base by extracting information from the Web.

Input: Target URLs, conditions, ontology or knowledge database

Output: Extracted information

Service Name: FlightSearchService

Purpose: traditional way to search for flight information such as available seats, departure time, arrival time, etc., based on the requirements or preferences of the tourist.

Input: Flight date and time, destination, departure, airline, price, other search criteria

Output: Flight information

Service Name: HotelSearchService

Purpose: traditional way to search for hotel information such as available room, locations, class of the hotel, etc.

Input: Location, search type, class, number of persons, price, add-on service, duration, other requirements of the room, other search criteria

Output: Hotel information and/or room information

4.2     Requirement and Preference Management Processes

Table 2. Key agents in the Requirement / Preference Management Agent Cluster

Agents

Functions

Preference

Guide the tourists to specify their requirements and preferences and maintain them

Ranking

Ranking information and results according to user preferences

 

Tourists are usually foreigners and therefore unfamiliar with their destinations. With the help of the ontology search agents, preference agents can guide tourists to specify their requirements and preferences collaboratively. Table 2 summarizes the key agents for these processes. The agents work as follows.

The types of information and resources that are searchable and specifiable can be retrieved with the help of ontology (Chiu et al. 2005d). Search criteria, options, and alternatives can be formulated from the ontology according to the categorization and attributes. The relevant information, therefore, is retrieved according to the tourist’s preferences and then ranked by ranking agents. The selections become the preference and requirements of the tourist to help in planning, scheduling, and packaging formulation after certain refinements.

The relationships and dependencies among information and resources (Chiu et al. 2005d) such as local tours discount, availability of an entry pass, availability of local transportation tickets, and historical relationships among tourist interests are recorded based on ontology. The agents can then find more favorable and more suitable combinations for individual tourists. Related websites are also rescored in the ontology, so that the tourists can access for more personalized information to facilitate their decision or further reference.

The ontology enables tourists to subscribe to information update of the categories that they are interested in. Ranking agents help the tourists rank the information obtained from the search processes according to their preferences. The key services for the implementation of the agent processes are summarized as follow.

Service Name: PreferenceService

Purpose: guides tourists to specify their requirements and preferences and maintains such information.

Input: Tourists Information, requirements, preferences, ontology, knowledge database

Output: None

Service Name: ConditionRanking

Purpose: ranks information and results according to a tourist’s preferences and requirements.

Input: Tourist Information, requirements, preferences, ontology, knowledge database

Output: Ranked results 

4.3     Package Planning Processes

Table 3. Key agents in the Package Planning Agent Cluster

Agents

Functions

Matchmaking

Match user preferences with available services and options

Confirmation

Assist users in waiting and confirmation of bookings

 

The package planning processes concern with the formulation of tour plans, focusing on the itinerary, particularly the possible connections (such as flights and trains) and hotel booking. Table 3 summarizes the key agents for these processes.

Figure 6 shows the detail architecture of the Package Planning Processes. The Package Planning agent formulates and evaluates the options with the help of matchmaking agents. The confirmation agents are responsible for handling waiting and confirmations, so that the tourist can be notified through alert agents when bookings are ready and when the whole desired booking actions are completed for transactions. Tourists may revise their plans in case some bookings cannot be confirmed as deadlines are approaching. In this case, tourists will be notified by the confirmation agents. In this section, we explain the details implementation of the agents of Package Planning Processes, namely, the matchmaking agents and confirmation agents.

4.3.1 Matchmaking Agent

The matchmaking agent provides capabilities to retrieve, select, and compose packages that are similar to the needs and preferences, according to individual tourists’ profiles stored in the knowledge base. Because the tourists now better understand and express their requirements and preferences by specifying them as categories of the ontology, the matchmaking agent can reason and select from much more viable options in a flexible manner. In order to achieve this objective, matchmaking agents complete a process of using conditional planning, ranking, and selection. The main objective of the matchmaking process is to schedule an appropriate timeframe during which the tourist will realize a particular activity reference by a package. After using conditional planning, more viable plans can be found and most likely better ones can be formulated.

In addition, the Matchmaking Agent is also the agent responsible for matching and filtering the meta-data describing the Web services and therefore, evaluates the similarity and the consequent ranking between a Web service and the user. Here, we are interested in the extra Web services functional parameters, which include a formal description of a set of Quality of Service (QoS) parameters. The QoS parameters are domain dependent, that is a set of additional parameters specific to a domain and to a service. It is used to select more appropriate packages, given a tourist profile. The parameters are formulated with OWL-based Web service ontology.

The other input for the matchmaking agent is the user profile. The concepts of the MAIS ontology are used to express all the parameters contained in this profile. A user profile contains the information about services’ extra-functional parameters and can be obtained by the analysis of the events captured by the system during the interaction session with the user. Therefore, it is possible to extract information on the typologies of user behavior in the form of rules. An example rule is given as follows.

IF

the tourist is single, requesting from Taiwan,

asks for a package belonging to

PackageCategory HotelReservation

THEN

the tourist prefers 4stars category,

room with TV, Internet connection,

freeBreakfast, air conditioning, nearShoppingArcade

The service name and corresponding input and output are listed as follows.

Service Name: MatchmakingService

Input: Tourist profile, preferences, conditions, ontology

Output: Recommended package

4.3.2 Confirmation Agent

Once the package has been evaluated, they are presented through the alert agents to the tourist for possible refinements. So this is an iterative process. The tourist can then select the package that appears more appealing or suitable, according to one’s preferences or requirements. The processes such as hotel reservation, flight booking, and payments will be completed once the tourist made the final confirmation. Finally, the confirmation agent handles waiting, confirmation, and other special cases, so that the tourist can be notified through alert agents. The corresponding services for the Confirmation Agent are illustrated as follows.

Service Name: HotelReservationService

Input: Tourists profile, requirements, ontology, preferences

Output: Confirmed reservation

Service Name: TicketBookingService

Input: Tourists profile, requirements, ontology, preferences

Output: Confirmed booking

Service Name: ConfirmationService

Input: Tourist profile, ontology, recommended package, refinements, alert agent information

Output: Confirmed package

4.4     Local Tour Planning Processes

Table 4. Key agents in the Local Tour Planning Agent Cluster

Agents

Functions

Locator

Keep the location of tourists and vehicles

Wrapper

Wrap existing third-party Websites and automate programmatic interfaces to them

Route Advisor

Find routes for user for driving or from public transportation

Map

Show appropriate sections of maps to users

Matchmaking

Match user preferences with available services and options

 

The Local Tour Guide (LTG) agents plan local tours for tourists within a certain city or region. LTG agents search for Tourist Interested Blocks (TIB).  Each TIB is a station of the tour. It can be a sight like a building or a service provided such as a restaurant. Each LTG agent maintains a personal interest profile and that of the TIBs. Table 4 summarizes the key agents for these processes while Figure 7 highlights their key interactions. How the ontology can help is similar to that of the package planning processes. However, tourists often request local tour planning only when they arrive at a certain place. Moreover, they often change their preferences and requirements because such activities are not constrained with bookings (and particularly the costs involved). Thus, mobile devices have to be supported in the collaborative process for its maximum usability, since tourists are usually unfamiliar with the destination.

After arriving at a destination, a tourist sets the available time period and a LTG agent researches the TIBs available nearby. The LTG agent addresses the most urgent information needs of a tourist, such as restaurant, attractions, and events. In particular, recommendations of such service partners to tourists can be potential sources for commission income. For larger service partners that have their own existing reservation Web sites, wrapper agents could be built rapidly with script based tools such as WebXcript (Chiu et al. 2005b). For smaller service partners, SMS messages sent through alert agents could be used as an alternative.

The mobile device used by the tourist determines the location using, say, GPS-WAAS (Lin and Chlamtac 2000). It is connected to the Internet either via GPRS or UMTS. Current information about each TIB is provided by the ontology, knowledge base, or Web services. A service provider like a restaurant or vehicle agent can create a Web service interface to the reservation or rental API of the corresponding management system. The LTG agent then computes the tour and the actual navigation of the tour can be visualized using the mobile device through the alert agent.

Route advisory agents (Chiu et al. 2005c) search for driving routes if the tourists drive their own car. Otherwise, route advisory agents search for suitable routes from public transportation or arrange for hired vehicles (such as taxis and vans by contacting vehicle agents on each vehicle). If the tourists are mobile in a large metropolis, the main challenges are the performance and efficiency because of the large number of attraction sights, restaurants, public transportation routes, etc. In addition, both available time and cost often need to be considered. Map agents are also handy if relevant sections of the map are available for being sent to the tourists’ mobile devices.

As the tourists do not know too much about the destination, specific services are needed in different scenarios. Based on the characteristic of services and/or information needed, four services are provided by the LTG agent: information service, TIB search service, route navigation service, and monitor service. Some of these services are illustrated as follows.

Service Name: InformationService

Purpose: provides a tourist with relevant information of the tourist’s location, for example, other registered travelers’ locations, weather, local news, etc.

Input: GPS position, tourist profile, information subscription

Output: Requested information

Service Name: TIBSearchService

Purpose: provides a tourist with the location information of TIB and current position; and helps the tourist make a selection for the planning.

Input: Tourist location, TIB requirement, additional conditions

Output: Recommended TIB, recommended route planning

Service Name: RouteNavigationService

Purpose: generates possible routes to guide the tourist to reach the destination.

Input: Source destination, intermediate locations, cost, additional preferences/requirements, output requirements

Output: Recommended route

Service Name: MonitorService

Purpose: tracks the tourist’s position, then based on the predefined conditions, such as the weather condition, traffic condition, activity region boundary, etc., generates alert for the tourist..

Input: Traveler position, reference position records, predefined conditions/preferences

Output: Alert messages

4.5     Tourist Assistant Processes

Table 5. Key agents in the Tourist Assistant Agent Cluster

Agents

Functions

Calendar

Maintain package and travel plans

Alert

Remind users of their upcoming activities, bookings, and urgent information received

User Interface

Customize input and output to user devices

 

Each tourist has a tourist assistant agent cluster to assist their trip and participate in collaborative service processes. Table 5 summarizes the key agents for these processes. Calendar agents maintain package plans and travel plans, including various bookings. Alert agents help the calendar agents remind tourists of their upcoming activities, bookings, and urgent information received from other alert agents (as described in the previous sub-sections). These alert agents also handles the message resend, rerouting, and even service re-assignment according to the alert management model of Kafeza et. al (2004). Upon a tourist’s consent, a location agent can help track the current position, so that location depend information could be sent to the tourist.

User interface agents provide interfaces for users to input their requests and preferences. They transform the eXtended Markup Language (XML) output from other agents to the current user platform with XML Stylesheet Language (XSL) technologies. For example, different Hypertext Markup Language (HTML) outputs are generated for Web browsers on desktop PCs and PDAs respectively, while WAP Markup Language (WML) outputs are generated for mobile phones (Lin and Chlamtac 2000). This objective can be easily achieved by adopting our earlier Three-Tier View-Based methodology (Chiu et al. 2003). Figure 7 illustrates different process view definition in XML.

5.Discussions and Conclusion

In this paper, we have presented the key architectural details and design rationale for a Collaborative Travel Agency System (CTAS) that allows interoperability across MAIS, integrating heterogeneous and disparate information with Semantic Web and Web service technologies. We have proposed a layered framework that supports multiple platforms (in particular wireless mobile ones) and a methodology for the analysis and design of CTAS. We have explained an overview of the major functionalities of a CTAS and the detail design of each agent cluster, together with the corresponding implementation frameworks and services for the major tourist processes. We have also shown how ontology helps agents to improve planning as well as helps tourist to better understand and specify their requirements and preferences collaboratively.

We now discuss the applicability of our implementation framework and methodology with respect to the major stakeholders, including tourists, traveling service providers, and system developers. The issues considered are based on the research framework on nomadic computing proposed by Lyytinen and Yoo (2002).

Tourists always desire the provision of anytime and anywhere assistance. One can imagine the difficulties of a tourist when he/she gets lost and cannot communicate with the local people. In particular, the flexibility of supporting multiple front-end devices increases tourists’ choice of hardware and therefore their means of connectivity. Agents help improve reliability and robustness of messaging (especially alert agents) by retrying upon unsuccessful attempts, searching for alternatives, and so on. In particular, information agents could forward important relevant news (such as a terrorist attack) or important messages (such a cancelled flight or blocked railway) to mobile tourists so that plans could be kept apprized through mobile devices. Under any circumstances, tourists primarily want to enjoy and should have the freedom and flexibility of changing their plans anytime and anywhere.

Even without mobile devices, a CTAS based on intelligent agents can still conveniently gather adequate information, provide flexible tour planning, and perform tedious booking procedures beforehand for tourists. The ontology helps both the tourists and the agents understand more available alternatives and options so that more effective plans are possible. Disparate information and service resources are also thus integrated.

A major concern of the traveling service providers is the costs against the benefits of the CTAS. At the first phase, agent-based planning could improve the productive of their consultants and possibly the quality of recommendations as well as the consistency of quality through pre-programmed intelligence. Potential clients could also obtain preliminary information and formulate draft plans before discussing options with the consultants, therefore, reducing the consultants’ workload. With the value-added services of a CTAS, it helps improve the professional image as well as customer relationships through ubiquitous system collaborations. Business opportunities may also increase due to service extension and the increase of service partners. Our approach is actually capturing the knowledge of tourist consultants and therefore accelerates the impact to their job security in addition to the impacts from the current available Web-based services.

As for implementation cost, our approach is suitable for adaptation of existing services and information sources by wrapping them with information agents. Through software reuse, a reduction not only to the total development cost but also training and support cost can be achieved. System developers are concern about the system development costs and subsequent maintenance efforts. These concerns can be addressed by systematic fine-grained requirements elicitation of the functions of various agent types. Thus, with loosely coupled and tightly coherent intelligent software modules encapsulated in agents, system complexity can be better managed. Agents are highly reusable and can be maintained with relative ease. Further, it should be noted that the use of XSL technologies and databases views as the main mechanism for user interface adaptation by presentation agents facilitates software maintenance at the application tier. This can significantly shorten the system development time, meeting management expectations in this competitive environment. Because W3C has recently designed OWL as a standard (Web-Ontology Working Group 2004), there are much less obstacles for software development involving ontology. Although this research does not aim at improving core planning algorithms (Corkill 1979) that has been employed in MAIS, the use of ontology improves searching and planning by increasing the number of recognized viable alternatives (Chiu et al. 2005).

Recent advances in technologies have resulted in fast evolving mobile device models and standards. The CTAS requires significant efforts in the adaptations and integrations to reach the ultimate goals. Agents are readily adaptable to cope with new technologies and can further help reduce uncertainties through adequate testing and experimentations of new technologies. Agents and their functionalities can also be implemented gradually and by phase. They can also help meet the scalability requirements with a distributed approach. As such, we are addressing the main challenge of a CTAS, which is the collaboration and integration of disparate information and service resources, together with the provision of personalized assistance to mobile tourists during their trip.

As for future research, our key focus is about ontologies. Despite the considerable efforts toward the construction of MAIS ontologies for agent interoperation, some difficulties and challenges are still open. Throughout the paper, we assume ontologies to be consistent and available to the agents. In reality, many ontologies are redundant in the sense that they present the same domain with little or no interoperation between different ontologies. The ontologies play a vital role in the MAIS as they provide a shared representation of the domain and of the concept that the agents need to use. Agents may fail to communicate if they are using different or inconsistent ontologies. Besides, ontologies may provide a different prospective and a different set of information on the concepts that they present. This is one important direction of our research to overcome these problems, particularly the handling of incomplete and inconsistent ontologies.


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