Copyright © 2006 by Gordon S. Novak Jr.
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1. CS 381K \ \ Artificial Intelligence
2. Artificial Intelligence as Science
5. Areas of Artificial Intelligence
8. Natural Language Understanding:
14. Characteristics of A.I. Programs
17. Semantic Networks / Frames
20. A.I. is the Future of Computing!
26. Tic-Tac-Toe as a State Space
28. Solving Problems Using Search
30. Basic Depth-first Search Algorithm
31. Comments on Search Algorithm
32. Recursive Depth-First Search
33. State Space Search Program
34. Notes on State Space Search Program
35. Missionaries and Cannibals
36. Missionaries/Cannibals Search Graph
37. Missionaries and Cannibals Representation
38. Functions for Missionaries and Cannibals
39. Testing Missionaries and Cannibals
46. Bounded Depth-First Search
48. Cost of Iterative Deepening
49. Using Heuristics to Guide Search
52. Use of Simulated Annealing
53. Example of Simulated Annealing
54. Behavior of Simulated Annealing
56. Breadth-First Search Algorithm
59. The Ordered Search Algorithm
62. Heuristic Search for Route Finding
63. Ordered Search for Route Finding
64. Effect of Heuristic Function
65. Admissibility of Heuristic Function
66. Informed Heuristic Functions
67. Features of Heuristic Functions
68. Heuristic Search Handles Local Maxima
69. Iterative Deepening A* (IDA*)
70. Beam Search: $MA^{*}$ and $SMA^{*}$
71. Forward vs. Backward Search
72. Search Tree vs. Search Graph
74. Simple Symbolic Differentiation
75. Simple Symbolic Differentiation ...
76. Testing and Incremental Development
77. Symbolic Simplification: Rewrite Rules
78. Symbolic Simplification Programs
81. Program vs. Pattern Matching
82. Pattern Matching Simplification
85. Rules for Pattern Matching
88. Copying and Substitution Functions
94. Problem Reduction Search: Flowchart
95. Problem Reduction Representations
97. Solution to an AND/OR Graph
102. Static Evaluation Functions
105. Implementing Alpha-Beta Search
106. Alpha-Beta Search Example
108. Samuel's Checkers Program
109. General Problem Solver (GPS)
111. Searching in Abstraction Spaces
112. Chronological Backtracking
113. Dependency Directed Backtracking
115. Outline of Genetic Algorithm
117. Waltz Filtering for Vision
124. Constraint Satisfaction Problems
125. CSP as Backtracking Search
129. Other Techniques and Kinds of CSP
132. Objectives of Eight-Puzzle Exercise
133. DENDRAL (Buchanan and Feigenbaum)
135. Ways to Reduce Search Space: Heuristics
137. Search as a Basic Technique
138. Where Search Should Fit in an AI System
139. Knowledge Representation and Reasoning
140. Representation Hypothesis
141. Computation as Simulation
142. Alternatives to the Representation Hypothesis
144. Knowledge Representation System
147. Retrieval: Matching Problem
148. Knowledge Representation Methods
149. Knowledge Representation: Hard Problems
150. Logic for Artificial Intelligence
153. Interpretation in Propositional Logic
161. Resolution for Propositional Calculus
164. Resolution for Propositional Calculus
165. Resolution Step for Propositional Calculus
167. Examples of Resolution Step
168. Example: Propositional Calculus Resolution
169. Resolution with Venn Diagrams
170. Example of Resolution with Venn Diagrams
172. Predicate Calculus (First-order Logic)
173. Interpretations in Predicate Calculus
174. Overview of Predicate Calculus Resolution
181. Resolution for Predicate Calculus
185. Unification Implementation
186. Simple Unification Algorithm
188. Soundness and Completeness
190. Resolution Strategies ...
196. Backchaining Theorem Prover
202. Planning: Situation Calculus
203. Operators in Situation Calculus
206. STRIPS Operators for Blocks World
207. STRIPS: Operator Application
208. Selection of STRIPS Operators
212. Weaknesses of A.I. Planning
213. Knowledge Rep. in Predicate Calculus
217. Importance of Backchaining
226. Predicate Calculus: Representation Language
228. Predicate Calculus as Programming Language
230. Semantic Networks and Frames
231. Property List Representation
233. Advantages of Property Lists
235. ``Frame'' Software Packages
236. Typical Features of Frames
246. Object-Oriented Programming
248. Internal Implementation is Hidden
250. Object-Oriented Programming vs. Frames
251. Unique Features of Frames
252. Unique Features of Object-Oriented Systems
256. Execution Time of Messages
258. Some Languages / Inference Engines
260. Overview of Knowledge Representation
262. Natural Language Processing (NLP)
263. Why Study Natural Language?
264. Model of Natural Language Communication
265. Minimality of Natural Language
267. Areas of Natural Language
268. Computer Language Understanding
269. Problems in Understanding Language ...
270. Outline of Natural Language Section
275. Statistical Natural Language Processing
288. Chomsky Hierarchy of Languages
291. Context Sensitive Languages
292. What Kind of Language is English?
297. Augmented Transition Networks
298. Augmented Transition Networks
299. Separability of Components
300. Problems with Separability
301. Combining Syntax and Semantics
302. How to Combine Syntax \& Semantics
303. Natural Language as an AI Problem
312. Disambiguation Using Case Frames
315. Conceptual Dependency: Examples
316. Conceptual Dependency: Evaluation
317. Work by Schank and Students
319. Deep Semantics Influences Parsing
324. How Not to do Representation
329. Simple Language Processing: ELIZA
330. Spectrum of Language Descriptions
332. Semantic Grammar: Extended Pattern Matching
333. Example Semantics for a Semantic Grammar
336. Sentence Pointer Handling
346. Natural Language Interfaces
347. Problems with NL Interfaces
348. Menu-based Natural Language
351. Machine Translation Example
352. Sentence Understanding in ISAAC
368. Rule-Based Expert Systems
369. Production Systems (OPS-5 family)
371. Production System (OPS-5)
376. Reasoning Under Uncertainty
379. Joint Probability Distribution
382. Computing with Bayesian Networks
384. EMYCIN's Certainty Factors
389. Certainty Factor Combination
390. Certainty Factor Combination
391. Summary of CF Computations
396. EMYCIN CF vs. Probability Theory
398. Expert Systems vs. Decision Trees
402. Example of Rule Induction
403. Final Decision Tree with Classifications
404. Algorithm for Rule Induction
405. Alternatives for {\tt select-feature}
406. Limitations of Rule Induction
408. Getting Knowledge From Expert
411. Advantages of Conceptual Islands
412. Expansion with Conceptual Islands
413. Orthogonal Knowledge Sources
416. Function of Brain Regions
417. Somatotopy: Sensory and Motor
419. Function from Brain Injury
420. Positron Emission Tomography
422. Hubel and Wiesel: Visual Cortex
434. Generalized Hough Transform
441. Adding Semantics to Vision
446. Driving: Detailed Architecture
448. Remote Agent: Architecture
449. Engine / Valve Configurations
450. Understanding Machines from Movies
451. Understanding Machines: Example