CS 516A:
Multiagent Systems
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Course
homepage Fall'98
Multiagent systems research, a subfield of artificial intelligence,
studies the interactions of computational agents. These agents can
represent real world parties, and they can have different preference
structures. A key research goal is to design open distributed systems
in a principled way that leads to globally desirable outcomes even
though every participating agent only considers its own good and may
act insincerely. The course covers relevant results in AI, game
theory, market mechanisms, voting, auctions, coalition formation, and
contracting. Effects of different computational limitations of the
agents are discussed. Software tools for multiagent systems are
presented. The course is targeted to graduate students and
senior-level undergraduates. Non-AI students are also highly welcome.
Application examples are presented in networks, operating systems,
manufacturing, and logistics. Evaluation is based on presentations,
written and programming assignments, and a final project of each
student's choice.
CS 520A: Intelligent Real-Time Systems
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Course
homepage
Fall'99
In many computer systems, it is not feasible (computationally) or
desirable (economically) to compute the "optimal" answer. This course
examines a variety of techniques that allow small quantities of
computational commodities - such as time, memory, or information - to
be traded for gains in the value of computed results. It covers both
theory and applications in such areas as combinatorial optimization,
multiagent systems, automated diagnosis and treatment, and information
gathering. Topics include: models for representing computational
limitations and tradeoffs, decision theory and rational choice, the
value of information, the deliberative vs. reactive debate, principles
of meta-reasoning, real-time search, memory-bounded search,
utility-directed search, deliberation scheduling (control of
reasoning), soft real-time, anytime algorithms, design-to-time
algorithms, dynamic planning and execution, reinforcement learning,
and evaluation of resource-bounded reasoning techniques.
Prerequisite: Basic knowledge of AI and probability theory, or permission of the instructor. Credit 3 units.
CS 511: Artificial Intelligence I
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Course
homepage Spring'00
CS 6745: Research Seminar on Artificial Intelligence