1. 1 Chapter 12. Web Information Integration Using Multiple Character Agents Soft computing Laboratory Yonsei University October 27, 2004

2. 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. 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. 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. 5 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 Information integration on MCI

6. 6 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 Information integration on MCI

7. 7 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 Information integration on MCI

8. 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. 9 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 Application Prototypes Based on the MCI

10. 10 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 Application Prototypes Based on the MCI

11. 11 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 Application Prototypes Based on the MCI

12. 12 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 Application Prototypes Based on the MCI

13. 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. 14 Agent scenario Agent behavior –controlled by scenarios written in Q Agent scenario –Represented as a state transition graph Implementation Issues of the MCI

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

16. 16 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 Evaluation of the MCI Using the Wizard of Oz Method

17. 17 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 Evaluation of the MCI Using the Wizard of Oz Method Number of Characters CooperationRoles A. Cooperative3Yes B. Single1-- C. Chat0-- D. Non-Cooperative3NoYes E. Homogeneous3TesNo

18. 18 Experimental results (1/2) Evaluation of the MCI Using the Wizard of Oz Method Chat vs. Single TopicChatSinglet-value (n=8)p-value Specialty0.21.4-1.260.12 Recipe14.813.40.880.20 Health0.41-0.80.22 Character01.8-1.610.07 Others4.62.42.170.03 Single vs. Cooperative TopicChatSinglet-value (n=8)p-value Specialty1.42.8-1.180.13 Recipe13.4102.420.02 Health12.4-1.720.06 Character1.81.60.120.45 Others2.43.2-0.700.25 Chat vs. Cooperative TopicChatSinglet-value (n=8)p-value Specialty0.22.8-3.410.004 Recipe14.8103.630.003 Health0.42.4-3.080.007 Character01.6-1.370.10 Others4.63.21.100.15

19. 19 Experimental results (2/2) Evaluation of the MCI Using the Wizard of Oz Method Non-Cooperative vs. Cooperative TopicNon-CooperativeCooperativet-value (n=8)p-value Specialty0.21.4-1.260.12 Recipe14.813.40.880.20 Health0.41-0.80.22 Character01.8-1.610.07 Others4.62.42.170.03 Homogeneous vs. Heterogeneous TopicHomogeneousHeterogeneoust-value (n=8)p-value Specialty1.42.8-1.180.13 Recipe13.4102.420.02 Health12.4-1.720.06 Character1.81.60.120.45 Others2.43.2-0.700.25

20. 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. 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