16.410 / 16.413 Principles of Autonomy and Decision Making, Fall 2005

Williams, Brian; Roy, Nicholas

Author(s)

Williams, Brian; Roy, Nicholas

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Alternative title

Principles of Autonomy and Decision Making

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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.

Date issued

2005-12

URI

http://hdl.handle.net/1721.1/71858

Department

Massachusetts Institute of Technology. Department of Aeronautics and Astronautics

Other identifiers

16.410-Fall2005

local: 16.410

local: 16.413

local: IMSCP-MD5-020164fd410f95bdcfc1b8e32b3cf551

Keywords

autonomy, decision, decision-making, reasoning, optimization, autonomous, autonomous systems, decision support, algorithms, artificial intelligence, a.i., operations, operations research, logic, deduction, heuristic search, constraint-based search, model-based reasoning, planning, execution, uncertainty, machine learning, linear programming, dynamic programming, integer programming, network optimization, decision analysis, decision theoretic planning, Markov decision process, scheme, propositional logic, constraints, Markov processes, computational performance, satisfaction, learning algorithms, system state, state, search treees, plan spaces, model theory, decision trees, function approximators, optimization algorithms, limitations, tradeoffs, search and reasoning, game tree search, local stochastic search, stochastic, genetic algorithms, constraint satisfaction, propositional inference, rule-based systems, rule-based, model-based diagnosis, neural nets, reinforcement learning, web-based, autonomy, search trees, 16.410, 16.413, Decision making

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