Chapter 12. Web Information Integration Using Multiple Character Agents

1. Chapter 12.Web Information Integration Using Multiple Character Agents Soft computing Laboratory Yonsei University October27, 2004

2. Outline • Introduction • Information integration on multiple character interface • Application prototypes based on the MCI • Venus and Mars • Recommendation battlers • Implementation issues of the MCI • An initial evaluation of the MCI using the wizard of Oz method • Wizard of Oz method • Experiments • Results • Related work • Conclusion

3. Introduction • Life-like agent or character (LLA or LLC) • Software agent with a virtual face and body on a computer display and behaviors like a creature or a person • Work as an interface between a human user and a computer system • User-friendly than conventional GUIs • Advantage • To provide an active interface to a system • cf. conventional man0machine interfaces • Web information retrieval • LLA can be applied to help • User-friendly interfaces are welcome • Help navigate users to their preferred web pages • This paper • Discusses a team of agents that work together as mediators between a user and multiple information sites • cf. most LLA used work as a standalone guide

4. Information integration on multiple character interface • Information on the web • Tends to be scattered among a number of sites • Information integration • Scheme to integrate distributed information sites into an interoperable system • It makes a collection of information sites more valuable than the individual components • Conventional information integration system • Designers determine how to integrate the information sites is specified • User did not know about it • User did not be allowed to change the combination of information sites nor the integration mechanism

5. Information integration on MCI Multiple character interface (1/2) • Multiple character interface (MCI) • Motivation of MCI • Each individual user has different demands or preferences for information integration • The best framework is one that allows the user to easily construct a team of his or her favorite information sites that work together and to customize them flexibly • Provides an environment where multiple information agents and a human user mutually interact • Information agent • Body part • Acts as an information gathering engine • Header part • Implemented as an animated LLA

6. Information integration on MCI Multiple character interface (2/2) • Communication between user and agent • User can access the agent by sending a message • Agent can respond to the message by talking with gestures

7. Information integration on MCI MCI-based agent • Have some advantages • Provide a friendly interface between the user and the information sources • Agents collaborate to assist the user in retrieving and integrating information • User can easily understand the functionality and role of each information agent by visualizing information agents as characters

8. Application prototypes based on the MCI • Venus and Mars • Cooperative search engine • Three LLA cooperate with each other to assist an user in locating cooking recipe pages • Recommendation Battlers • Competitive restaurant recommendation system • Two LLA compete with each other to recommend restaurants to a user

9. Application Prototypes Based on the MCI Venus and Mars (1/2) • Search engine • Most widely used tools to retrieve information from the web • Not always very useful for novice users such as elderly people • Authors utilize domain specific information agents • Provides noiseless information concerning a particular domain such as recipes, restaurants, or retailers • Venus and Mars • System that allows information integration based on keyword associations through conversations among LLCs • Search results are shown in two frames • Left : a list of recipe pages • Right : web page of a list entry when the entry is clicked

10. Application Prototypes Based on the MCI Venus and Mars (2/2) • Three information agents • Kon-san • Cho-san • Pekko • Collaborate with each other • Assists in reducing the number of search results in dialogue steps • Asks for a tip on seasoning and answers on behalf of the user in utterance step • Have potential of realizing various types of information search by adding agents to the team

11. Application Prototypes Based on the MCI Recommendation Battlers (1/2) • Electronic commerce (EC) • One of the most successful application domains of the internet • Most conventional shopping sites are running in an independent and closed manner • Comparison shopping sites are run by a third party, which is independent from buyers and sellers • Recommendation Battlers • New multiagent-based system for EC where multiple shopping sites or information recommendation sites are integrated in a flexible and interactive manner • Provides a virtual space where multiple animated agents • Customer compares items recommended by multiple agents and finds a preferred one by watching a competition performed by the agents on a browser • Agents can learn his or her preference and use it for further recommendations through interactions with the customer

12. Application Prototypes Based on the MCI Recommendation Battlers (2/2) • Two restaurant recommendation agents • Peddy • Genie • Peedy and Genie start to recommend restaurants in a competitive manner after gathering restaurant information from web sites • Recommendation • Performed by two character agents interacting with each other and user • Show the web page that contains the restaurant information • Add comments about the average cost and the distance from the nearest station

13. Implementation issues of the MCI • Architecture of the MCI • Each agent recognizes actions taken by the user or other agents through data captured by its sensor, interprets the actions, and responds through its actuator • When the agent hears something, variables $utterance and $agent are instantiated • By combining commands, an agent can perform complicated actions

14. Implementation Issues of the MCI Agent scenario • Agent behavior • controlled by scenarios written in Q • Agent scenario • Represented as a state transition graph

15. Implementation Issues of the MCI Implementation of MCI • MCI implement • Using a control frame and multiple agent frames • When MCI is initialized • Managers are loaded into the control frame

16. Evaluation of the MCI Using the Wizard of Oz Method Wizard of Oz method • Evaluation of Venus and Mars or Recommendation Battlers system is difficult • They are still at prototype stage • They are not able to communicate with a human user fluently • Wizard of Oz method • Method to observe the behavior of human subjects toward a computer system in which a human operator called wizard simulates the whole or a part of the system • In the paper, the authors modified the Venus and Mars system so the user interacts with wizards through characters

17. Evaluation of the MCI Using the Wizard of Oz Method Experiments • Three features of MCI • Multiple characters appear • Characters interact with each other • Characters are heterogeneous and each one has its own role • Five interfaces used

18. Evaluation of the MCI Using the Wizard of Oz Method Experimental results (1/2)

19. Evaluation of the MCI Using the Wizard of Oz Method Experimental results (2/2)

20. Related work • Meta-search engines integrate the output of multiple search engines and succeed in offering improved performance • In conventional collaborative information integration systems • Techniques used to coordinate the information agents or information resources are specified by the system designers • Remain hidden from users • Andre and Rist propose a system employing multiple characters • Their work mainly emphasizes the advantage of multiple characters as presentation media  Proposed system in this paper is more like a multiagent system because the information agents are physically distributed over the internet

21. Conclusion • This paper • Propose an information integration platform called MCI • Show two application prototypes • Venus and Mars • Recommendation Battlers • Evaluate the MCI by using the wizard Oz method • Future works • Capability for life-likeness • Capability for collaboration • Capability for presentation • Capability for conversation