chapter 12. web information integration using multiple character agents
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Chapter 12. Web Information Integration Using Multiple Character Agents. Soft computing Laboratory Yonsei University October 27, 200 4. Outline. Introduction Information integration on multiple character interface Application prototypes based on the MCI Venus and Mars - PowerPoint PPT PresentationTRANSCRIPT
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Chapter 12.Chapter 12.Web Information Integration Using Web Information Integration Using
Multiple Character AgentsMultiple Character Agents
Soft computing LaboratoryYonsei University
October 27, 2004
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Outline 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
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Introduction 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
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IInformation integration on multiple character nformation integration on multiple character interfaceinterface
• 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
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MMultiple character interface (1/2)ultiple character interface (1/2)• Multiple character interface (MCI)
– Motivation of MCIEach individual user has different demands or preferences
for information integrationThe 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
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MMultiple character interface (2/2)ultiple 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
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MCI-based agentMCI-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
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AApplication prototypes based on the MCIpplication 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
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VVenus and Mars (1/2)enus 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 pagesRight : web page of a list entry when the entry is clicked
Application Prototypes Based on the MCI
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Venus and Mars (2/2)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
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Recommendation Battlers (1/2)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
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Recommendation Battlers (2/2)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
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IImplementation issues of the MCImplementation 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 actuatorWhen the agent hears something, variables $utterance and
$agent are instantiated
– By combining commands, an agent can perform complicated actions
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AAgent scenariogent scenario• Agent behavior
– controlled by scenarios written in Q
• Agent scenario– Represented as a state transition graph
Self-introduction Idling
"May I help you?"
Web InformationRetrival
Qustion aboutingredients
Question aboutseasoning
Clicked
Unknownkeyword
The number ofresults is over
1000
Knownkeyword
Knownkeyword
The number ofresults is over
1000
Unknownkeyword
Knownkeyword
Implementation Issues of the MCI
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IImplementation of MCImplementation of MCI• MCI implement
– Using a control frame and multiple agent frames– When MCI is initialized
Managers are loaded into the control frame
Agent Manager(Java Script)
User Manager(Java Script)
Dialogue Manager(Java Script)
CommandTransmitter
(Applet)
Command Receiver(Applet)
CommandTransmitter
(Applet)
CommandTransmitter
(Applet)
CharacterController
(Java Script)
CharacterController
(Java Script)
...
Control Frame
Agent Frame 1 Agent Frame 2
Implementation Issues of the MCI
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Wizard of Oz methodWizard 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
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ExperimentsExperiments• 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
Cooperation Roles
A. Cooperative 3 Yes Yes
B. Single 1 - -
C. Chat 0 - -
D. Non-Cooperative 3 No Yes
E. Homogeneous 3 Tes No
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EExperimental results (1/2)xperimental results (1/2)
Evaluation of the MCI Using the Wizard of Oz Method
Chat vs. Single
Topic Chat Single t-value (n=8) p-value
Specialty 0.2 1.4 -1.26 0.12
Recipe 14.8 13.4 0.88 0.20
Health 0.4 1 -0.8 0.22
Character 0 1.8 -1.61 0.07
Others 4.6 2.4 2.17 0.03
Single vs. Cooperative
Topic Chat Single t-value (n=8) p-value
Specialty 1.4 2.8 -1.18 0.13
Recipe 13.4 10 2.42 0.02
Health 1 2.4 -1.72 0.06
Character 1.8 1.6 0.12 0.45
Others 2.4 3.2 -0.70 0.25
Chat vs. Cooperative
Topic Chat Single t-value (n=8) p-value
Specialty 0.2 2.8 -3.41 0.004
Recipe 14.8 10 3.63 0.003
Health 0.4 2.4 -3.08 0.007
Character 0 1.6 -1.37 0.10
Others 4.6 3.2 1.10 0.15
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EExperimental results (2/2)xperimental results (2/2)
Evaluation of the MCI Using the Wizard of Oz Method
Non-Cooperative vs. Cooperative
Topic Non-Cooperative Cooperative t-value (n=8) p-value
Specialty 0.2 1.4 -1.26 0.12
Recipe 14.8 13.4 0.88 0.20
Health 0.4 1 -0.8 0.22
Character 0 1.8 -1.61 0.07
Others 4.6 2.4 2.17 0.03
Homogeneous vs. Heterogeneous
Topic Homogeneous Heterogeneous t-value (n=8) p-value
Specialty 1.4 2.8 -1.18 0.13
Recipe 13.4 10 2.42 0.02
Health 1 2.4 -1.72 0.06
Character 1.8 1.6 0.12 0.45
Others 2.4 3.2 -0.70 0.25
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Related workRelated 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
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ConclusionConclusion• This paper
– Propose an information integration platform called MCI– Show two application prototypes
Venus and MarsRecommendation 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