1. RADAR February 15, 20051 RADAR /Space-Time Learning

2. RADAR February 15, 20052 Purpose of Space-Time Module Automated allocation of resources (space, equipment, time slots, etc.) in both crisis and routine situations. Optimization Customization Learning

3. RADAR February 15, 20053 Space-Time Specialist General Planning, Priority Setting Meta-level reasoning Natural Language Output Natural Language Input Processing User Interface & Dialog User Attention Manager E-mail IM User User Activity Sensors Task Manager Dispatch Event Logging, Triggering, Introspection Databases Knowledge Base Inference Engine Shared Knowledge Calendar Manager Webmaster Briefings Space/Time Planner E-Mail Manager Specialist Modules … And More Task/Info Bus

4. RADAR February 15, 20054 Motivating task Scheduling of talks at a conference, and related allocation of rooms and equipment, in a crisis situation. Initial schedule Unexpected major change in space availability; for example, closing of a building Continuous stream of minor changes; for example, schedule changes and unforeseen equipment needs

5. RADAR February 15, 20055 Architecture Info elicitorParserOptimizer Process resource requests Update conference schedule Choose and send questions Router Top-level control and learning RADAR /Space-Time Processing of e-mail messages User dialog managers Graphical user interface Administrator

6. RADAR February 15, 20056 Optimization Input: Initial conference schedule Uncertain information about the available resources, requirements, and user preferences Output: New conference schedule

7. RADAR February 15, 20057 Learning of relevant questions Learning of typical requirements and default user preferences Learning of recombinant plans and strategies Learning Information elicitation Current work Near future (Years 2–3) Years 3–5

8. RADAR February 15, 20058 The system learns most of the new knowledge during “war games” It may learn some additional knowledge during the test Learning

9. RADAR February 15, 20059 Information elicitation The system identifies critical missing knowledge, sends related questions to users, and improves the world model based on users’ answers.

10. RADAR February 15, 200510 Information elicitation Input: Uncertain information about resources, requirements, and user preferences Answers to the system’s questions Learned knowledge: Critical additional information about resources, requirements, and preferences Knowledge examples: Size of the auditorium is 1000 ± 10 square feet Size of the broom closet does not matter

11. RADAR February 15, 200511 Information elicitation User participation: User should answer some of the system’s questions about resources and preferences Benefits: Increase of the schedule quality Reduction in the related uncertainty Learned knowledge: Critical additional information about resources, requirements, and preferences

12. RADAR February 15, 200512 Missing info: Invited talk: – Projector need Poster session: – Room size – Projector need Assumptions: Invited talk: – Needs a projector Poster session: – Small room is OK – Needs no projector Example: Initial schedule Available rooms: Room num. Area (feet 2 ) Proj- ector 123123 2,000 1,000 1,000 Yes No Yes Requests: Invited talk, 9–10am: Needs a large room Poster session, 9–11am: Needs a room 1 2 3 Initial schedule: Talk Posters

13. RADAR February 15, 200513 Example: Choice of questions 1 2 3 Initial schedule: Talk Posters Candidate questions: Invited talk: Needs a projector? Poster session: Needs a larger room? Needs a projector? Requests: Invited talk, 9–10am: Needs a large room Poster session, 9–11am: Needs a room Useless info: There are no large rooms w/o a projector × Useless info: There are no unoccupied larger rooms × Potentially useful info √

14. RADAR February 15, 200514 Example: Improved schedule Requests: Invited talk, 9–10am: Needs a large room Poster session, 9–11am: Needs a room 1 2 3 Initial schedule: Talk Posters Info elicitation: System: Does the poster session need a projector? User: A projector may be useful, but not really necessary. 1 2 3 New schedule: Talk Posters

15. RADAR February 15, 200515 Choice of questions For each candidate question, estimate the probabilities of possible answers For each question, compute its expected impact on the utility, and select questions with large expected impacts For each possible answer, compute the respective change of the schedule utility

16. RADAR February 15, 200516 Learning of relevant questions Learning of typical requirements and default user preferences Information elicitation Learning in Years 2–3 Current work Near future

17. RADAR February 15, 200517 Learning of relevant questions The system analyzes old elicitation logs and creates rules for “static” generation of useful questions. These rules enable the system to ask critical questions before scheduling.

18. RADAR February 15, 200518 Learning of relevant questions Input: Log of the information elicitation Learned knowledge: Rules for question generation Knowledge examples: If the exact size of the largest room is unknown, ask about its size before scheduling Never ask about the sizes of broom closets

19. RADAR February 15, 200519 Learning of relevant questions User participation: User may optionally modify the learned rules or add new rules User should answer some questions that are based on the learned rules Benefits: Faster learning during “war games” Reduction in the number of questions Learned knowledge: Rules for question generation

20. RADAR February 15, 200520 Learning of default preferences The system analyzes known requirements and user preferences, and creates rules for generating default preferences. These rules enable the system to make reasonable assumptions about unknown requirements and preferences.

21. RADAR February 15, 200521 Learning of default preferences Input: Known requirements and preferences Answers to the system’s questions Learned knowledge: Rules for generating default requirements and preferences Knowledge examples: Regular session needs a computer projector with 99% certainty When John Smith gives keynote talks, he always uses a microphone

22. RADAR February 15, 200522 Learning of default preferences User participation: User may optionally modify the learned rules or add new rules Benefits: Increase of the schedule quality Reduction in the number of questions Learned knowledge: Rules for generating default requirements and preferences

23. RADAR February 15, 200523 Learning of control rules for high-level planning and elicitation strategies Recombinant actions sequences and plans Automated selection reasoning and learning strategies from a library Advanced mechanism for dealing with uncertainty and hypothetical scenarios Learning in Years 3–5

24. RADAR February 15, 200524 Learning plans and strategies for transfer in crisis situations Robust Seeded Version- Space Learning Abstraction/inheritance hierarchy Exponential  polynomial worst- case complexity Recombinant Episodic Learning + explanations OLD PLANS NEW PLAN Experiential learning Meta-reasoning (why each plan fragment is reused) GENERAL SPECIFIC V-space seed Abstracted Plan