Incorporating Tutorial Strategies Into an Intelligent Assistant Jim R. Davies, Neal Lesh, Charles Rich, Candace L. Sidner, Abigail S. Gertner, Jeff Rickel.

Slides:



Advertisements
Similar presentations
CRC Before you can build an object-oriented system, you have to define the classes (objects) that represent the problem to be solved, how the classes relate.
Advertisements

Improving System Safety through Agent-Supported User/System Interfaces: Effects of Operator Behavior Model Charles SANTONI & Jean-Marc MERCANTINI (LSIS)
Towel: Towards an Intelligent ToDo List Ken Conley Jim Carpenter SRI International AAAI Spring Symposium 2007.
Bernd Bruegge & Allen Dutoit Object-Oriented Software Engineering: Conquering Complex and Changing Systems 1 Software Engineering September 12, 2001 Capturing.
Intelligent Profiling by Example From: “Intelligent profiling by Example”, Sybil Sherin, Henry Lieberman
Learning Agents Center Computer Science Department George Mason University Dorin Marcu IT 803 Spring 2004 – Mixed-Initiative Intelligent Systems.
Anahita Mohseni-Kabir, Sonia Chernova and Charles Rich
Task Learning in COLLAGEN The COLLAGEN Architecture; Task Learning from Demonstrations Mitshubishi Electric Research Labs Andrew Garland, Neal.
COLLAGEN: When Agents Collaborate with People Charles Rich and Candace L. Sidner Presented by Daniel Schulman.
Top-down hierarchical planning A standard Artificial Intelligence mechanism used for simulating many aspects of ‘intelligent’ behaviour Main reference.
Dialogue in Intelligent Tutoring Systems Dialogs on Dialogs Reading Group CMU, November 2002.
MokSAF: Agent-based Team Assistance for Time Critical Tasks Katia Sycara The Robotics Institute
Chapter 4 DECISION SUPPORT AND ARTIFICIAL INTELLIGENCE
Independent Research End User Design Cortney Germain Matthew Hung Mark Lewis Prazen.
Agent-based Interfaces Group 3 Topic 2 IM2044 Usability engineering Hasuk Kerai Ismael Ali.
Provisional draft 1 ICT Work Programme Challenge 2 Cognition, Interaction, Robotics NCP meeting 19 October 2006, Brussels Colette Maloney, PhD.
Interaction Styles Interface Widgets. What are Interaction Styles?  A Collection of interface objects and associated techniques from which an interaction.
Models of Human Performance Dr. Chris Baber. 2 Objectives Introduce theory-based models for predicting human performance Introduce competence-based models.
1/1/ Designing an Ontology-based Intelligent Tutoring Agent with Instant Messaging Min-Yuh Day 1,2, Chun-Hung Lu 1,3, Jin-Tan David Yang 4, Guey-Fa Chiou.
Ideas for Explainable AI
01 -1 Lecture 01 Intelligent Agents TopicsTopics –Definition –Agent Model –Agent Technology –Agent Architecture.
McGraw-Hill/Irwin ©2005 The McGraw-Hill Companies, All rights reserved ©2005 The McGraw-Hill Companies, All rights reserved McGraw-Hill/Irwin.
The role of eye tracking in usability evaluation of LMS in ODL context Mr Sam Ssemugabi Ms Jabulisiwe Mabila (Professor Helene Gelderblom) College of Science.
31 st October, 2012 CSE-435 Tashwin Kaur Khurana.
Your Project Title Here Lab Course / Praktikum: Winter Semester 2012/2013 Project Management and Software Development for Medical Applications Your Name.
AIS-IFT: An intelligent tutoring system for training initial entry helicopter pilots Demonstration (Alpha release) April, 2003.
1 USC INFORMATION SCIENCES INSTITUTE Modeling and Using Simulation Code for SCEC/IT Yolanda Gil Jihie Kim Varun Ratnakar Marc Spraragen USC/Information.
Chapter 14: Artificial Intelligence Invitation to Computer Science, C++ Version, Third Edition.
Developing Workflows with SharePoint Designer David Coe Application Development Consultant Microsoft Corporation.
Cognitive Reasoning to Respond Affectively to the Student Patrícia A. Jaques Magda Bercht Rosa M. Vicari UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL BRASIL.
1 MEDICAL ERROR REPORTING AND ANALYSIS Vijaya Gotla UmaDevi Bandaru Lavanya Gundamaraju.
CSA3212: User Adaptive Systems Dr. Christopher Staff Department of Computer Science & AI University of Malta Lecture 9: Intelligent Tutoring Systems.
© 2007 Tom Beckman Features:  Are autonomous software entities that act as a user’s assistant to perform discrete tasks, simplifying or completely automating.
Chapter 6 – System Design II: Behavioral Models. 6.1 Models Models - what do you think of? 2.
1 PLAN RECOGNITION & USER INTERFACES Sony Jacob March 4 th, 2005.
MERL 1 COLLAGEN: Middleware for Building Mixed-Initiative Problem Solving Assistants ( Neal Lesh, Andy Garland, Chris Lee, David McDonald, Egon Pasztor,
A Proposal of Application Failure Detection and Recovery in the Grid Marian Bubak 1,2, Tomasz Szepieniec 2, Marcin Radecki 2 1 Institute of Computer Science,
Copyright 2002 Prentice-Hall, Inc. Chapter 2 Object-Oriented Analysis and Design Modern Systems Analysis and Design Third Edition Jeffrey A. Hoffer Joey.
1 USC INFORMATION SCIENCES INSTITUTE CALO, 8/8/03 Acquiring advice (that may use complex expressions) and action specifications Acquiring planning advice,
Learning Agents Center George Mason University Computer Science Department Partners Day Symposium May 4, 2004 Gheorghe Tecuci, Mihai Boicu, Dorin Marcu,
User Support Chapter 8. Overview Assumption/IDEALLY: If a system is properly design, it should be completely of ease to use, thus user will require little.
Chapter 10 Analysis and Design Discipline. 2 Purpose The purpose is to translate the requirements into a specification that describes how to implement.
COMP 208/214/215/216 – Lecture 8 Demonstrations and Portfolios.
A. Mørch, , ) Empirical-Based, Evolutionary Design of FLE/Agents Background Knowledge building environments Method Empirical study and.
The Evolution of ICT-Based Learning Environments: Which Perspectives for School of the Future? Reporter: Lee Chun-Yi Advisor: Chen Ming-Puu Bottino, R.
1 USC Information Sciences Institute Yolanda GilFebruary 2001 Knowledge Acquisition as Tutorial Dialogue: Some Ideas Yolanda Gil.
Fall 2010 UVa David Evans cs2220: Engineering Software Class 28: Past and Future.
1 USC INFORMATION SCIENCES INSTITUTE CAT: Composition Analysis Tool Interactive Composition of Computational Pathways Yolanda Gil Jihie Kim Varun Ratnakar.
There are 3 ways you can use to find your Spectrum Status Report Database file. This tutorial will assume you have either previously made a database that.
ICNEE 2002 Applying RL to Take Pedagogical Decisions in Intelligent Tutoring Systems Ana Iglesias Maqueda Computer Science Department Carlos III of Madrid.
Artificial intelligence
1 USC INFORMATION SCIENCES INSTITUTE Expect: COA Critiquing PSM EXPECT: A User-Centered Environment for the Development and Adaptation of Knowledge-Based.
Class Builder Tutorial Presented By- Amit Singh & Sylendra Prasad.
The Easy Tool Training For Customer Service Representatives By : Veronica Flores
College of Science August 20, 2013 Interactive Office Hours Agenda  What is an “office hour”?  “Interactive” defined  Role of the student.  Role of.
1 USC INFORMATION SCIENCES INSTITUTE EXPECT TEMPLE: TEMPLate Extension Through Knowledge Acquisition Yolanda Gil Jim Blythe Information Sciences Institute.
W. L. Johnson and J. T Rickel. “Animated Pedagogical Agents: Face-to-Face Interaction in Interactive Learning Environments,” International Journal of Artificial.
“Intelligent User Interfaces” by Hefley and Murray.
MERL 1 COLLAGEN: Applying Collaborative Discourse Theory to Human-Computer Interaction Charles Rich Candace L. Sidner Neal Lesh Mitsubishi Electric Research.
Understanding Naturally Conveyed Explanations of Device Behavior Michael Oltmans and Randall Davis MIT Artificial Intelligence Lab.
A Speech Interface to Virtual Environment Authors Scott McGlashan and Tomas Axling Swedish Institute of Computer Science.
Bridging the Generation Gap Through Stories Aro Muttilainen Oliphant Sammander Sen.
Andrew Garland, Neal Lesh, and Charles Rich Mitsubishi Electric Research Laboratories Responding to and Recovering from Mistakes during Collaboration.
Cognitive Modeling Cogs 4961, Cogs 6967 Psyc 4510 CSCI 4960 Mike Schoelles
OO Domain Modeling With UML Class Diagrams and CRC Cards
Web-Mining Agents Cooperating Agents for Information Retrieval
Group Y Presenters: (indicate roles)
Jim Fawcett CSE776 – Design Patterns Summer 2003
Applying learner modelling for user interface assistance in simulative training systems Alexander Hörnlein, Frank Puppe Dept. for Artificial Intelligence.
Director of Industry Relations
Presentation transcript:

Incorporating Tutorial Strategies Into an Intelligent Assistant Jim R. Davies, Neal Lesh, Charles Rich, Candace L. Sidner, Abigail S. Gertner, Jeff Rickel

Organizations Involved College of Computing, Georgia Institute of Technology (Davies) Mitsubishi Electric Research Labs (Lesh, Rich, Sidner) The MITRE Corporation (Gertner) USC Information Sciences Institute (Rickel)

Motivating Example Long camping trip Someone tutors you on how to set up a tent As time passes, that tutor becomes an assistant

Research Goal To show that assisting and tutoring are two points on the same spectrum by building an agent that can transition between both behaviors.

Intellectual History Collaborative Assisting Agent (COLLAGEN) –assists with software applications COLLAGEN generated interest in learning to use applications. We are extending COLLAGEN so it can tutor and built an agent called Triton.

COLLAGEN (COLLaborative Agent) Middleware Discourse theory of collaboration Shared plan theory Rich, C. and Sidner, C. L. (1998). COLLAGEN: A collaboration manager for software interface agents. User Modeling and User-Adapted Interaction, Vol. 8, No. 3/4, 315—350.

Assistant Scenario User: Let’s plan the second leg of the trip. Assistant: Okay. User: [selects Washington] What next? Assistant: [points to San Francisco] I will select San Francisco. User: Okay. Assistant: [selects San Francisco] User: What next? Assistant: Let’s remove an airline constraint.

ITS Background Domains: physics and algebra Domain Knowledge Student Model Pedagogical knowledge Wenger, E. (1987). Artificial Intelligence and Tutoring Systems. Morgan Kaufmann Publishers, Inc. Los Altos, CA.

Tutoring Scenario (part 1) User: What next? Triton: Please specify the route of the trip. User: What next? Triton: Select Atlanta and then select Boston. User: What next? Triton: Please select Atlanta. User: What next? Triton: [points to Atlanta with cursor] Please click on the dot near Atlanta on the map.

Tutoring Scenario (cont.) User: [clicks on Atlanta] What next? Triton: Please select Boston. User: [clicks on Dallas] Triton: Oops! What you did was select Dallas; what you should have done was select Boston. I will bring you back to the previous state by clicking undo. [undo] Please select Boston. User: [clicks on Boston] Triton: Great job! User: What next? Triton: Select the dates and times of travel.

Goal of this research Add to COLLAGEN to give it tutoring behaviors Adjustable parameters Triton

Worked Examples Worked examples are kinds of recipes

The User is Not Always Right Determining when a task is completed Responding to Errors

Responding to Errors Intervene after n unrecognizable actions What the intervention looks like: –Say what the student did –Say what the student should have done –Undo to get to previous state

Tutors are not Maximally Helpful Because of learning goals Waiting for Student Initiative Suggesting actions without doing them Explaining Demonstrating Pointing

Learning Goals Usually task goals are in service of learning goals, but not always

Waiting For Student Initiative In assisting, always try to help In tutoring, get student to try herself

Suggesting Actions Without Doing Them Should you force the user to do all actions? Agent suggests doing, but doesn’t do.

Explaining

Explaining (cont.) Composite Actions –list of task descriptions Primitive Actions –application-level description of what to do on screen Stored as explanation recipes

Demonstrating Behavior: –Do a sequence of actions –Undo them Stored as explanation recipes

Pointing In assisting, point when proposing In tutoring, point when explaining a primitive

Summary of Parameters When to intervene after error detection Who defaults to do actions When to point

Contributions Middleware Use of recipes as a single representational structure for: –abstract actions –utterances –explanations –demonstrations

Conclusions This work bridges the gap between tutoring and assisting Smoothly transitions between them Based on collaborative discourse theory

Future Work Student Model Automatic Shifting between assisting and tutoring

URLs