Task Learning in COLLAGEN

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. 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 … Modeling 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) Modeling 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 Modeling 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 Modeling 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) Modeling 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 Modeling 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 ? Modeling 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 Modeling 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) Modeling the cost of misunderstanding …