Automated Assistant for Crisis Management (Reflective Agent with Distributed Adaptive Reasoning) RADAR

, but also under crisis conditions Help not only in routine situations Purpose Automation of office-management tasks, such as scheduling, handling, and resource allocation

Outline Overview of RADAR Resource allocation Future challenges More information See Talk with Radar researchers

Outline Overview of RADAR Resource allocation Future challenges

PAL video Three-minute video Military-setting motivation for RADAR (Carnegie Mellon) and CALO ( SRI ).

Project size Largest research project in CMU ’s School of Computer Science. Five departments Language Technologies ( LTI ) Computer Science Department ( CSD ) Institute for Software Research International ( ISRI ) Human-Computer Interaction Institute ( HCII ) Center for Automated Learning and Discovery ( CALD ) Eighty people Twenty-nine faculty members Twenty-seven graduate students Twenty-four others Five years (2003–2008)

Project size Largest research project in CMU ’s School of Computer Science. Advantages Multiple research areas Collaboration opportunities Potential of a major impact Drawbacks Coordination challenges Frequent deliverables

Challenges Intelligent performance of office-management tasks Collaboration with a human administrator Dialog with users Continuous learning of new knowledge and strategies Integration of multiple tasks

Research areas Artificial intelligence Machine learning Natural-language processing Human-computer interaction Architecture and integration

Main components Planning and co-ordination of the system’s high-level actions.

Main components Web Master Helps create and maintain web sites.

Main components Web Master Organizer Helps filter, sort, and prioritize messages.

Main components Web Master Organizer Calendar Manager Helps keep track of appointments and negotiate meeting times among multiple users.

Main components Web Master Organizer Calendar Manager Briefing Assistant Helps compile reports based on multiple data sources.

Web Master Organizer Calendar Manager Briefing Assistant Main components Resource Allocation

Outline Overview of RADAR Resource allocation Future challenges

Purpose Automated allocation of office resources, in both routine and crisis situations. Assignment of offices Reservation of conference rooms Allocation of furniture, computers, and other office equipment

People Jaime Carbonell Resource allocation (AI and learning) Eugene Fink Resource allocation (AI and learning) Bob Frederking understanding (Natural language) Faculty Grad students Ulas Bardak Richard Wang Research staff Greg Jorstad

RADAR /Space video Six-minute video Initial system for automated assignment of offices.

Initial results A prototype system for automated allocation of offices. Effective allocation of office resources Processing of natural-language requests Interface for a human administrator

Outline Overview of RADAR Resource allocation Future challenges

Motivating task Scheduling of talks at a conference, and related allocation of rooms and equipment, in a crisis situation. Initial allocation plan 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

Automated reasoning Temporal reasoning Uncertainty tolerance Preference elicitation Collaboration with a human administrator

Learning Integrated learning of new knowledge and strategies. From experience From observation From instruction

Integration Users RADAR Calendar Manager RADAR Organizer RADAR Web Master Integrated RADAR Task manager RADAR Resource Allocation RADAR Briefing Assistant High-level planning Integrated learning

Integration Users Integrated RADAR High-level planning Integrated learning RADAR Resource Allocation Knowledge base and inferences RADAR Calendar Manager RADAR Organizer RADAR Web Master RADAR Briefing Assistant User dialog manager Natural language processing Resource allocation group

Tasks and skills Development of AI, learning, and natural-language algorithms Solving open-ended problems Implementation and integration

More information Jaime Carbonell Newell Simon Hall 4519 Eugene Fink New Simon Hall 4521 AI and learning Bob Frederking Newell Simon Hall 4617 Language