http:^^www.cs.washington.edu^education^courses^573^syllabus.htm

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Date: Mon, 02 Dec 1996 15:39:28 GMTServer: NCSA/1.4.2Content-type: text/htmlLast-modified: Thu, 10 Oct 1996 21:50:48 GMTContent-length: 1660<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN">

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<title>Syllabus:</title>
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<h1 align=center>CSE 573 Topics</h1>
<ol>
<li>Intelligent Agents<p>Percepts &amp; Actions</p>
<p>Agent Architectures</p>
<p>Environmental Properties</p>
</li>
<li>Search<p>Problem spaces</p>
<p>Brute force: depth-first, breadth-first, iterative-deepening, bi-directional</p>
<p>Constraint satisfaction</p>
<p>Heuristic search: A*, IDA*, SMA*</p>
<p>Optimization &amp; techniques from operations research </p>
</li>
<li>Knowledge Representation &amp; Reasoning<p>Propositional Logic: Syntax, Semantics, Inference, Expressiveness</p>
<p>First Order Logic: Syntax, Semantics, Inference, Expressiveness</p>
<p>The fundamental tradeoff in knowledge representation</p>
</li>
<li>Planning &amp; Acting<p>Representing actions: STRIPS, ADL, &amp; the situation calculus</p>
<p>The classical planning problem</p>
<p>Planning as search: world-states &amp; plan-states</p>
<p>The POP &amp; UCPOP algorithms</p>
<p>Operator graph optimizations</p>
<p>Incomplete information &amp; Sensing</p>
<p>Reactive approaches</p>
</li>
<li>Learning <p>Search through version space</p>
<p>PAC learning</p>
<p>Induction algorithms &amp; decision trees</p>
<p>Inductive logic programming</p>
<p>Explanation-based generalization</p>
</li>
<li>Reasoning about Uncertainty <p>Bayesian belief networks</p>
<p>Decision analysis</p>
</li>
<li>Methodology<p>The form of AI theories</p>
<p>Experimental methodology</p>
<p>Benchmarks &amp; testbeds </p>
</li>
</ol>
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