1. Michael T. Cox Computer Science & Engineering Department Wright State University Dayton, OH 45435 mcox@cs.wright.edu DAGSI/AFRL #HE-WSU-99-09 AFOSR #F49620-99-1-0244 Planning as Mixed-Initiative Goal Manipulation

2. State-Space Planning Problem consists of initial and goal states Domain consists of type hierarchy and action set Operator consists of set of variable bindings, preconditions and post conditions Plan consists of sequence of operators

3. Planning as Search G 1 G 2 G S 1 S 2 S 3 I

4. Planning as Goal Manipulation Planning is steering goals through task –Assigning resources to goals –Time phased priorities –Goal change Focus on Objectives Tractable planning Higher quality plans

5. Steering Goals through the Planning Process Human Plan Machine Goals

6. Goal Transformations A Goal Transformation is a minimal movement of goal in a goal space In resource limited worlds –lower (or raise) goals –rather than fail (or miss opportunity) Example: impassable (R1)  limited (R1)

7. Example Problem

8. General Form

9. Prodigy 4.0 User Interface 2.0

11. GTrans User Interface 2.1

13. Interface Evaluation Objective: To compare the user performance under the search model and the goal manipulation model Manipulate the model and problem difficulty Cluster by expertise Measure goal satisfaction and keystrokes

14. Model Comparisons

15. Cross Domain Comparisons

16. GTrans and Prodigy/Agent RMI facilitates multiple versions of GTrans Dynamic goal sub goal relationships Current goals Goal transformation process PRODIGY generated plan Human user 1 Interface Human user 2 Interface

17. Mixed-Initiative Replay

19. Conclusion Mixed-initiative computation improves (constrains) Complexity limitations Solution quality Solution speed Planning can successfully be viewed as a mixed-initiative goal manipulation task