RADAR May 5, RADAR /Space-Time Assistant: Crisis Allocation of Resources

RADAR May 5, Space-Time researchers Jaime Carbonell Eugene Fink Faculty Research staff Peter Jansen Students Chris Martens Ulas Bardak Scott Fahlman Steve Smith Greg Jorstad Brandon Rothrock

RADAR May 5, Outline Purpose and main challenges Demo of Space-Time Assistant Current and future learning

RADAR May 5, Purpose Automated allocation of rooms and related resources, in both crisis and routine situations.

RADAR May 5, 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 room availability; for example, closing of a building Continuous stream of minor changes; for example, schedule changes and unforeseen equipment needs

RADAR May 5, Main challenges Effective resource allocation Collaboration with the human administrator Use of uncertain knowledge Dealing with surprises Information elicitation Learning of new strategies running current work future work

RADAR May 5, Architecture Info elicitorParserOptimizer Process new info Update conference schedule Choose and send questions Top-level control and learning Graphical user interface Administrator Future Work RADAR 1.0

RADAR May 5, Outline Purpose and main challenges Demo of Space-Time Assistant Current and future learning

RADAR May 5, Outline Purpose and main challenges Demo of Space-Time Assistant Current and future learning

RADAR May 5, Information elicitation Learning current work ( RADAR 1.0) Learning of relevant questions Learning of typical requirements and default user preferences near future (Years 2–3) Years 3–5 Learning of new strategies

RADAR May 5, The system learns most of the new knowledge during “war games” It may learn some additional knowledge during the test Learning

RADAR May 5, Information elicitation The system identifies critical missing knowledge, sends related questions to users, and improves the world model based on their answers.

RADAR May 5, 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 5000 ± 50 square feet Size of the broom closet does not matter Useful when the initial knowledge includes significant uncertainty, and users are willing to answer the system’s questions.

RADAR May 5, Learning of relevant questions The system analyzes old elicitation logs and creates rules for “static” generation of useful questions, which allow asking critical questions before scheduling.

RADAR May 5, Learning of relevant questions Input: Log of the information elicitation Learned knowledge: Rules for question generation Knowledge examples: If the size of the largest room is unknown, ask about its size before scheduling Never ask about the sizes of broom closets Useful when the knowledge includes significant uncertainty, users answer the system’s questions, and “war games” provide sufficient information for learning appropriate rules.

RADAR May 5, Learning of default preferences The system analyzes known requirements and user preferences, creates rules for generating default preferences, and uses them to make assumptions about unknown preferences.

RADAR May 5, 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 projector with 99% certainty When John Smith gives keynote talks, he always uses a microphone Useful when “war games” provide sufficient information for learning appropriate defaults.

RADAR May 5, The system’s knowledge during “war games” includes significant uncertainty Users can obtain additional information in response to the system’s questions The world model and schedule properties during “war games” are similar to those during follow-up tests Effective “war games”