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AFOSR Computational Cognition and Machine Intelligence
Program Review, October 2020
Panos Artemiadis, UNIVERSITY OF DELAWARE
A human-machine symbiotic system for the extraction of high-level behaviors from a macroscopic view of swarms
Brett Borghetti, Air Force Institute of Technology (AFIT)
Information Acquisition Deficit Detection and Mitigation through Neurophysiological-sensed Operator Patterns
Selmer Bringsjord, RENSSELAER POLYTECHNIC
Great Computational Intelligence, Mature and Further Applied
Jerome Busemeyer, INDIANA UNIVERSITY
Applications of quantum probability theory to human-machine communication networks
Jonathan Cagan, CARNEGIE MELLON UNIVERSITY
Empowering the problem solving team through a computer-human partnership
Leon Chua, UNIVERSITY OF CALIFORNIA BERKELEY
Exploiting Memristors and The Local Activity Principle
Anca Dragan, UNIVERSITY OF CALIFORNIA BERKELEY
Implicit Communication in Human-Machine Collaboration
Edmund Durfee, UNIVERSITY OF MICHIGAN
Making and Keeping Informed Commitments in Human-Machine Systems
Chris Eliasmith, UNIVERSITY OF WATERLOO
Learning in large-scale models of biological cognition
Kenneth Forbus, NORTHWESTERN UNIVERSITY
Towards Software Apprentices that Learn in Dynamic Domains
Thomas Griffiths, PRINCETON UNIVERSITY
Discovering Optimal Strategies for Bounded Agents
Leslie Kaelbling, MASSACHUSETTS INSTITUTE OF TECHNOLOGY
Learning to Plan in Hybrid Spaces
John Laird, UNIVERSITY OF MICHIGAN
Levels of Learning in Natural and Artificial Agents
Interactive Task Learning
John Licato, UNIVERSITY OF SOUTH FLORIDA
Active Formalization in Artificial and Human Reasoners
Roger Mailler, UNIVERSITY OF TULSA
Choosing a Direction: Neural Models of Decision Making
Mark Pitt, OHIO STATE UNIVERSITY
Robust Autonomous Adaptive Experimentation
Andrea Stocco, UNIVERSITY OF WASHINGTON
Testing a Common Model for Human and Human-Like Intelligence
Katia Sycara, CARNEGIE MELLON UNIVERSITY
Flexible and Resilient Autonomous Systems
John Tsotsos, YORK UNIVERSITY
Visual Perception and Reasoning: Integrating Cognitive Programs, Working Memory, Attention Control and Visual Processing
Kagan Tumer, OREGON STATE UNIV CORVALLIS
Counterfactuals and Multiple Rewards: Inducing and Explaining Good Team Behavior for Effective Agent-Human Teaming