CHI97 Software Agents Tutorial


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Table of Contents

Software Agents


Agent Visionaries

Video Knowledge Navigator

What is an Agent?

Common Issues Studied

Types of Agents

What is a Software Agent?

How is an Agent different from other Software?

Why do we need Software Agents?

Why do we need Software Agents?

Direct Manipulation

Indirect Management/Agents

Criticisms of Software Agents (Lanier, Schneiderman)

Software Agent =? Expert System

Agents vs Expert Systems (cont.)

Types of Software Agents

Types of Software Agents

Types of Software Agents (cont.)

User-Programmed Agents

Nature of “Intelligence” (cont.)

Knowledge-Based Agents

Nature of “Intelligence” (cont.)

Learning from the User

Learning from other Agents

Example: Email Agent

Second Example: News Agent

Real example of User-Programmed Agent: OVAL (Malone, MIT Sloan)

OVAL (cont.)

PPT Slide

Other examples of User Programmed Agents

Real Example of Knowledge-BasedAgent: CHORIS (Tyler & Sullivan, Lockheed)

PPT Slide

Other examples of Knowledge Based Agents

Real Examples of Learning Agent: Maxims (MIT Media Lab)

Maxims Learning Agent (cont)

Learning from Peers: Peer-Peer model

Other examples of Learning Agents

Pros/Cons of Approaches

Pros/Cons of Approaches

Pros/Cons of Approaches

Which approach is best?

Types of Software Agents

Location of Agents

Location of Agents (cont.)

Mobility of Agents

Mobility of Agents (cont.)


Technical challenges

Software Agent <-> Application

Technical Challenges

Interface Agent <-> Other Agent

Common Language, Common Ontology

Negotiation & Commitment methods

Modeling other Agents

Authentication & Identification

Technical challenges

Interface Agent <-> User

Issue 1: Understanding

Issue 1: Understanding

Issue 2: Control

Issue 2: Control

Issue 3: Distraction

Issue 3: Distraction

Issue 4: Ease of Use

Issue 4: Ease of Use

Issue 5: Personification

Video: Persona Project (Microsoft)

Issue 5: Personification

Personified Agents Research

Roles for Software Agents

Agents as Eager Assistants

Eager (Cypher)

Video Eager

PPT Slide

Video Meeting Scheduler (MIT Media Lab)

Maxims Email Agent (MIT Media Lab)

Eager Assistant Agent (cont.)

Details of one Example

Prediction & Confidence Level Computation

Using the Prediction

Multi-agent Collaboration

Agent Interface


Video Maxims

Does it work? Results:

Discussion (cont.)

Video Schlimmer’s Note Taking Agents

Eager Assistants: Dimensions

Agents as Guides

Video Guides (Apple)

Letizia (Lieberman)

Video Letizia (Lieberman)

Feature-Based Filtering: Analyzing Documents for Relevant Keywords

Feature-Based Filtering: Updating User Profile Based on one More Datapoint Document

PPT Slide

Feature-Based Filtering: Filtering Based on User Profile

Agents as Memory Aids

Remembrance Agent (Rhodes/Starner)

PPT Slide

Remembrance Agent URL

MIT Media Lab Wearables Group

Agents as Filters/Critics

Video NewsTaylor

Technology Underlying Critics: Feature-based Filtering (FBF)

PPT Slide

PPT Slide

PPT Slide

Demo Firefly and Webhound

Complementary Technology Underlying Critics: Automated Collaborative Filtering (ACF)

ACF - Predicting a Rating

Collaborative vs Feature based

Comparison (cont.)

Some other Neat Features

Neat Features (cont.)

Other quantitative Results

Neat Features (cont.)

Neat Features (cont.)

Quantitative Results – Base case Algorithm:

Quantitative Results – HOMR/Ringo:

Agents as Matchmakers

Yenta (Foner)

Yenta applications

Yenta Methods

Yenta Algorithm

Agents for Buying/Selling

PPT Slide

PPT Slide

Bargain Finder demo

PPT Slide

PPT Slide

Fido shopping Doggie Demo

Kasbah (Chavez)

Kasbah (Chavez)

Kasbah Example

The Future of Agent Technology

Future Direction: Markets for Agents

Future Direction: Ecologies of Agents

Implications of Software Agents

Benefits to Consumer

Benefits to Marketers/Producers

Speculations: Effects of Agents on Markets

Open Questions

Further References

Author: Pattie Maes


Home Page:

Other information:
Software Agents Group MIT Media Laboratory