Autonomous Interface Agents Henry Lieberman Media Laboratory, MIT Presented by Sumit Taank Vishal Mishra

Outline Introduction to Agents Introduction to Agents Interface Agents Interface Agents Autonomous Agents Autonomous Agents Letizia – An Autonomous Interface Agent Letizia – An Autonomous Interface Agent Conclusion Conclusion

Introduction to Agents No universally excepted definition. No universally excepted definition. Liberman’s definition: Liberman’s definition: “Program that can be considered by the user to be acting as an assistant or helper, rather than as a tool in the manner of a conventional direct- manipulation interface.”

Properties of Agents Learning Learning Inference Inference Adaptability Adaptability Independence Independence Creativity Creativity “Delegating task rather than commanding”

Interface Agents Program that affect the objects in a direct manipulation interface, but without explicit instruction from the user. Program that affect the objects in a direct manipulation interface, but without explicit instruction from the user.

Interface Agents Likened to robots Likened to robots“Softbots” Sensors for the input Sensors for the input Effectors form the output Effectors form the output Examples Examples –Intelligent tutoring systems –Content-sensitive help systems

Autonomous Agents Program that operates in parallel with the user. Program that operates in parallel with the user. Act Independently and Concurrently Act Independently and Concurrently Importance: Time Savers, True Delegation Importance: Time Savers, True Delegation In Contrast to Conversational Interfaces In Contrast to Conversational Interfaces

Autonomous Interface Agents Interface Agent may not be Concurrent Interface Agent may not be Concurrent –Intelligent tutoring systems –Critiquing systems Autonomous agents maybe non-interactive Autonomous agents maybe non-interactive Must have Concurrency, Independence and Interactive behavior Must have Concurrency, Independence and Interactive behavior

Letizia: Concepts Autonomous interface agent for web browsing Autonomous interface agent for web browsing Watches user Web Browsing to learn Watches user Web Browsing to learn Compiles user profile by content analysis Compiles user profile by content analysis –Uses TF-IDF Searches “nearby” Web space in parallel Searches “nearby” Web space in parallel Offers recommendations independently Offers recommendations independently

Letizia: Interface Consists of 3 Netscape Windows (default) “Channel Surfing” interface style Tracks user used window, uses other

Traditional Search Conversational: User must declare interest explicitly Conversational: User must declare interest explicitly User idle during search User idle during search Search agent idle while user is using browsing interface Search agent idle while user is using browsing interface Sequential structure, no concurrency Sequential structure, no concurrency

Letizia Search Treats Web browsing as real time activity Treats Web browsing as real time activity Considers “user attention” foremost Considers “user attention” foremost Goal not to retrieve “best answer” but give reasonable recommendation Goal not to retrieve “best answer” but give reasonable recommendation “in context” search “in context” search “just in time” delivery “just in time” delivery

User’s Search Browsers tend to encourage a depth-first exploration. Browsers tend to encourage a depth-first exploration. –User can get stuck in long fruitless path

User’s & Letizia’s Search Letizia searches breadth-first interleaved with users depth-first search. Letizia searches breadth-first interleaved with users depth-first search.

User Profile TF-IDF is used to compute content of a document. TF-IDF is used to compute content of a document. –Not reliable but fast. User profile is accumulated during browsing. User profile is accumulated during browsing. Profile may be saved across sessions to make it persistent. Profile may be saved across sessions to make it persistent.

Recommendation Presents recommendations continuously Displays recommendation that matches user interest.

Main Values to User Avoiding “Dead Ends” Avoiding “Dead Ends” Avoiding “Garden Path” Avoiding “Garden Path” Noticing Serendipitous Connections Noticing Serendipitous Connections Providing ‘better than nothing guesses’ when no other source of preference Providing ‘better than nothing guesses’ when no other source of preference

Conclusions Novel idea to improve web browsing. Novel idea to improve web browsing. User profile “bag of keywords” User profile “bag of keywords” –User can search on unrelated topics in same session. Searches only nearby pages. Searches only nearby pages. –Search engines or other web resource can be used for search.

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