Cognitive Robotics, Spring 2004

Abstract:: Algorithms and paradigms for developing embedded systems that are able to operate autonomously for years at a time within harsh and uncertain environments. Focus on systems that demonstrate high levels of deduction and adaptation. Draws upon a diverse set of computational methods from artificial intelligence, operations research, software engineering, and control. Topics include: real-time deduction and search, automated planning, scheduling and execution, model-based diagnosis and failure recovery, reactive planning, hybrid systems, and agent architectures.

Cognitive Robotics, Spring 2005

Abstract:: Algorithms and paradigms for developing embedded systems that are able to operate autonomously for years at a time within harsh and uncertain environments. Focus on systems that demonstrate high levels of deduction and adaptation. Draws upon a diverse set of computational methods from artificial intelligence, operations research, software engineering, and control. Topics include: real-time deduction and search, automated planning, scheduling and execution, model-based diagnosis and failure recovery, reactive planning, hybrid systems, and agent architectures. Cognitive robotics addresses the emerging field of autonomous systems possessing artificial reasoning skills. Successfully-applied algorithms and autonomy models form the basis for study, and provide students an opportunity to design such a system as part of their class project. Theory and application are linked through discussion of real systems such as the Mars Exploration Rover.

Abstract:: This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information technology, and graduate (16.413) students.

Abstract:: A graduate-level introduction to artificial intelligence. Topics include: representation and inference in first-order logic; modern deterministic and decision-theoretic planning techniques; basic supervised learning methods; and Bayesian network inference and learning.