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Introduction

Towards Ubiquitous Tourist Service Coordination and Integration: a Multi-Agent and Semantic Web Approach. Introduction. Agents - programs that act on behalf of their human users and exhibit some aspects of autonomous behavior

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Introduction

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  1. Towards Ubiquitous Tourist Service Coordination and Integration: a Multi-Agent and Semantic Web Approach

  2. Introduction • Agents - programs that act on behalf of their human users and exhibit some aspects of autonomous behavior • Multi-agent information system (MAIS) used to conduct e-commerce activities • Support ubiquitous access through mobile devices over wired and wireless networks • As mobile devices become more powerful, intelligent software agents can now be deployed on these devices and hence also subject to mobility => peer-to-peer mobile computing • Semantic Web technologies and ontology help agents to reason and MAIS more flexible and intelligent • Propose next generation Ubiquitous Tourist Assistant System (UTAS) based on these technologies

  3. Layered Infrastructure for UTAS

  4. MAIS Analysis and Design Methodology for UTAS • Part 1 - overall architectural design • Focus of this paper • Part 2 - detailed design of agents • Each types of agents in the UTAS domain has high potentials for further in depth research because of its emerging adoptions • See our paper in HICSS37 NSS track

  5. MAIS Overall Architectural Design • Identify different categories of services for tourists with the help of ontology. • If existing ontologies are inadequate, augment with required concepts. • Identify different types of process to be supported. • For each process, identify the major agent and then the interactions among the processes. • Identify minor agents that assist the major agents to carry out these functionalities. • Identify the interactions required for each minor agent type. • Design the basic logics for all these agents. • Identify the (mobile) platforms to be supported • See if any adaptation is required

  6. Detailed Design of Agents • Design and adapt the user interface required for users to input their preferences. Customize displays to individual users and platforms. • Determine how user preferences are mapped into constraints and exchange them in a standardized format. • Now, we can consider 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. • Enhance the performance and intelligence of the agents with various heuristics gathering during the testing and pilot phase of the project.

  7. Service Process Categories • Ontology Maintenance and Search • Requirement and Preference Management • Package Planning • Local Tour Planning • Tourist Assistant

  8. MAIS Overview of UTAS Local Location Database Reminder Agents Calendar Agents Web Sites Web Sites Tourist Assistant Agent Cluster PartnerWeb Sites User Interface Agents Vehicle Agents Alert Alert Agent Alert Tourist Requests / Responses Local Tour Planning Agent Cluster Local Offices Alert Agent Location Ontology Maintenance & Search Agent Cluster Package Planning Agent Cluster Requirement / Preference Management Agent Cluster Tourist Portal Call Center Ontology / Knowledge Base / Tourist profiles, location, plans

  9. Ontology Maintenance and Search Processes • Call for industry-wide ontology

  10. Requirement and Preference Management Processes • The ontology let the tourists know what types of information and resources are searchable and specifiable. • The categorization and their attributes from the ontology can become the search criteria, options, and alternatives. • The ontology records the relationships and dependencies among information and resources, e.g., better combinations. • Ontology records the related web sites / subscribe for information update

  11. Package Planning Processes • Because the tourists now better understand and express their requirements and preferences with ontology, agents can reason and select from much more viable options in a flexible manner. • More viable plans can be found and thus better ones can be formulated.

  12. Local Tour Planning Processes

  13. Tourist Assistant Processes

  14. Tourists’ Benefits • Anytime and anywhere assistance • Multiple front-end devices increases tourists’ choice of hardware and connectivity • Agents help improve reliability and robustness of messaging (especially alerts) – retry, alternatives • Forward important relevant news (e.g., terrorist attack) and important messages (e.g., cancelled flight) • Plan revisions • Ontology helps both the tourists and the agents understand more available alternatives and options

  15. Traveling Service Providers • Costs vs. Benefits • Improve the productive of their consultants and possibly the quality of recommendations • Consistency of quality through pre-programmed intelligence • Reduce the consultants’ workload • Value-added services • Improve the professional image as well as customer relationships • Increase business opportunities – more partners and services

  16. System Developer’s Perspective • System development costs and subsequent maintenance efforts • Our approach is suitable for adaptation of existing services and information sources by wrapping them with information agents • Loosely coupled and tightly coherent intelligent software modules encapsulated in agents => manage system complexity • Agents are highly reusable and adaptable • Shorten the system development time via adaptation and integration • Keep up with fast evolving technologies

  17. Conclusion • A pragmatic approach of developing a UTAS with an MAIS infrastructure • Multiple platforms (in particular wireless mobile ones) and their integration • Overview of MVM requirements and process • Methodology for analysis and design of a MAIS for UTAS • Discuss the design of each agent cluster • Merits and applicability of our approach from the perspectives of major system stakeholders • Reference model for UTAS

  18. Further and Ongoing work • Study or re-examine the technical and management perspectives of each phase and functions of the UTAS process in details • Implementation • MAIS architecture for other emerging domains, e.g., mobile workforce management, m-government • Ubiquitous computing and context

  19. Q&A Thank you!

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