1. Task Learning in COLLAGEN The COLLAGEN Architecture; Task Learning from Demonstrations Work @: Mitshubishi Electric Research Labs Andrew Garland, Neal Lesh, Kathy Ryall, Charles Rich, Candy Sidner Carnegie Mellon University, 2001

2. 11-04-01Modeling the cost of misunderstanding …2 Outline  The COLLAGEN Architecture:  P1: COLLAGEN: Applying Collaborative Discourse Theory to Human-Computer Interaction  Learning Task Models:  P2: Learning Task Models from Collaborative Discourse  Add refinement & regression testing:  P3: Learning Hierarchical Task Models by Defining and Refining Examples  Adding guessers:  P4: Interactively Defining Examples to be Generalized  Discussion: pros, cons, questions …

3. 11-04-01Modeling the cost of misunderstanding …3 COLLAGEN  COLLAGEN = COLLaborative AGENt  Based on SharedPlans discourse theory (Grosz & Sidner)  Not the classical dialog-system view: agent & human collaborate, and they both interact with the application  4 agents presented: VCR, SymbolEditor, GasTurbine agent, home thermostat (kind-of toy domains)

4. 11-04-01Modeling the cost of misunderstanding …4 COLLAGEN (cont’d) Dialog Management architecture  Discourse state  Focus stack (stack of goals)  Plan tree for each of them  Actions: primitive / non-primitive  Recipes = specification of goal decompositions  Partially ordered steps, parameters, constraints, pre- and post-conditions  Updating the discourse state: 5 cond…  Plan recognition

5. 11-04-01Modeling the cost of misunderstanding …5 Learning Task Models from Collaborative Discourse [2]  Starting Point: “more difficult for people to deal with abstractions in the task than to generate and discuss examples”  “Programming-by-Demonstration” approach:  Infer task models from partially-annotated examples of task behavior.  Similarities with Helpdesk Call Center …  CallCenter idea: learn from watching traffic Richly annotate traffic / recent EARS stuff… Learn task structure from annotated traffic

6. 11-04-01Modeling the cost of misunderstanding …6 Learning Task Models (cont’d)  Annotation Language:  e, S, optional, unordered, unequal Q: how powerful is this task representation ?  fully annotating would be burdensome  Learning: alignment, optionals, orders & propagators  BIAS for learning …  Alignment: Disjoint step assumption  Alignment: Step type assumption. Q: Hmm, not sure I got this…  Propagators: Suggested parameter preference bias (~ occam’s razor)

7. 11-04-01Modeling the cost of misunderstanding …7 Learning Task Models – Experiments.  How:  Start from 2 task models  Generate examples, randomize  Relearn models, see what you get…  Results:  Optional did not get much action: it figures, it’s probably the easiest to learn…  Equality seems to buy a lot; and this is good !  Learning is strongly influenced by the order of examples…  Discussion  Not adequate for direct use *  Mention of the “online” flavor

8. 11-04-01Modeling the cost of misunderstanding …8 Learning HTN by defining and refining examples  Created a development environment which integrates the learning techniques with:  Defining & Refining examples  Regression testing (needed if manual edits are allowed)  They esentially give a management process for the development of task models [fig. 3]  Q: Is there any reason for Starting Set of Actions ?  Q: The whole things looks really like a storyboard, but is there anything really new here ?

9. 11-04-01Modeling the cost of misunderstanding …9 Interactively Defining Examples to be Generalized  NEW: Guessers  Guessers suggest to the user what annotations might be helpful  Organized in committees to improve robustness;*  Knowledge sources:  Other examples *  Current generalization  The inference techniques ~ active learning  Raw data  Domain Theory  Heuristics

10. 11-04-01Modeling the cost of misunderstanding …10 So what do you think ?  Is it worth it ? When ?  Does the conjecture hold ?  How about when you collect the examples ? (ala CallCenter)