Copyright © 2007 by Gordon S. Novak Jr.
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1. CS 343 \ \ Artificial Intelligence
2. Artificial Intelligence as Science
7. Natural Language Understanding:
13. Characteristics of A.I. Programs
16. Semantic Networks / Frames
19. A.I. is the Future of Computing!
28. Combinations of CAR and CDR
29. Constructing List Structure
30. List Manipulation Functions
34. Building Lists Incrementally
35. Building Lists Incrementally ...
45. Examples of Recursion in Lisp
46. More Examples of Recursion
48. SUBST is COPY-TREE with Substitution
49. Executing Multiple Statements
57. Tic-Tac-Toe as a State Space
59. Solving Problems Using Search
65. Basic Depth-first Search Algorithm
66. Comments on Search Algorithm
67. Recursive Depth-First Search
68. State Space Search Program
69. Notes on State Space Search Program
70. Missionaries and Cannibals
71. Missionaries/Cannibals Search Graph
72. Missionaries and Cannibals Representation
73. Functions for Missionaries and Cannibals
74. Testing Missionaries and Cannibals
81. Bounded Depth-First Search
83. Cost of Iterative Deepening
84. Using Heuristics to Guide Search
87. Breadth-First Search Algorithm
90. The Ordered Search Algorithm
93. Heuristic Search for Route Finding
94. Ordered Search for Route Finding
95. Effect of Heuristic Function
96. Admissibility of Heuristic Function
97. Informed Heuristic Functions
98. Features of Heuristic Functions
99. Heuristic Search Handles Local Maxima
100. Iterative Deepening A* (IDA*)
101. Beam Search: $MA^{*}$ and $SMA^{*}$
102. Forward vs. Backward Search
103. Search Tree vs. Search Graph
105. Problem Reduction Search: Flowchart
106. Problem Reduction Representations
108. Solution to an AND/OR Graph
109. Search of an AND/OR Graph
113. Static Evaluation Functions
116. Implementing Alpha-Beta Search
117. Alpha-Beta Search Example
119. Samuel's Checkers Program
120. General Problem Solver (GPS)
122. Searching in Abstraction Spaces
124. Outline of Genetic Algorithm
125. Constraint Satisfaction Problems
126. CSP as Backtracking Search
130. Other Techniques and Kinds of CSP
132. DENDRAL (Buchanan and Feigenbaum)
134. Ways to Reduce Search Space: Heuristics
136. Search as a Basic Technique
137. Where Search Should Fit in an AI System
138. Knowledge Representation and Reasoning
139. Representation Hypothesis
140. Computation as Simulation
141. Alternatives to the Representation Hypothesis
143. Knowledge Representation System
146. Retrieval: Matching Problem
147. Knowledge Representation Methods
148. Knowledge Representation: Hard Problems
149. Logic for Artificial Intelligence
152. Interpretation in Propositional Logic
160. Resolution for Propositional Calculus
163. Resolution for Propositional Calculus
164. Resolution Step for Propositional Calculus
166. Examples of Resolution Step
167. Example: Propositional Calculus Resolution
169. Predicate Calculus (First-order Logic)
170. Overview of Predicate Calculus Resolution
177. Resolution for Predicate Calculus
181. Unification Implementation
182. Simple Unification Algorithm
184. Soundness and Completeness
186. Resolution Strategies ...
191. Backchaining Theorem Prover
197. Planning: Situation Calculus
198. Operators in Situation Calculus
201. STRIPS Operators for Blocks World
202. STRIPS: Operator Application
203. Selection of STRIPS Operators
207. Weaknesses of A.I. Planning
208. Knowledge Rep. in Predicate Calculus
212. Importance of Backchaining
221. Predicate Calculus: Representation Language
223. Predicate Calculus as Programming Language
225. Semantic Networks and Frames
226. Property List Representation
228. Advantages of Property Lists
230. ``Frame'' Software Packages
231. Typical Features of Frames
241. Object-Oriented Programming
243. Internal Implementation is Hidden
245. Object-Oriented Programming vs. Frames
246. Unique Features of Frames
247. Unique Features of Object-Oriented Systems
253. Some Languages / Inference Engines
254. Knowledge Bases or Ontologies
255. Overview of Knowledge Representation
257. Natural Language Processing (NLP)
258. Why Study Natural Language?
259. Model of Natural Language Communication
260. Minimality of Natural Language
262. Areas of Natural Language
263. Computer Language Understanding
264. Problems in Understanding Language ...
265. Outline of Natural Language Section
270. Statistical Natural Language Processing
283. Chomsky Hierarchy of Languages
286. Context Sensitive Languages
287. What Kind of Language is English?
292. Augmented Transition Networks
293. Augmented Transition Networks
294. Separability of Components
295. Problems with Separability
296. Combining Syntax and Semantics
297. How to Combine Syntax \& Semantics
298. Natural Language as an AI Problem
307. Disambiguation Using Case Frames
310. Deep Semantics Influences Parsing
315. How Not to do Representation
320. Simple Language Processing: ELIZA
321. Spectrum of Language Descriptions
323. Semantic Grammar: Extended Pattern Matching
324. Example Semantics for a Semantic Grammar
327. Sentence Pointer Handling
337. Natural Language Interfaces
338. Problems with NL Interfaces
339. Menu-based Natural Language
342. Machine Translation Example
343. Sentence Understanding in ISAAC
359. Rule-Based Expert Systems
360. Production Systems (OPS-5 family)
362. Production System (OPS-5)
367. Reasoning Under Uncertainty
370. Joint Probability Distribution
373. Computing with Bayesian Networks
375. EMYCIN's Certainty Factors
380. Certainty Factor Combination
381. Certainty Factor Combination
382. Summary of CF Computations
387. EMYCIN CF vs. Probability Theory
389. Expert Systems vs. Decision Trees
393. Example of Rule Induction
394. Final Decision Tree with Classifications
395. Algorithm for Rule Induction
396. Alternatives for {\tt select-feature}
397. Limitations of Rule Induction
399. Getting Knowledge From Expert
402. Advantages of Conceptual Islands
403. Expansion with Conceptual Islands
404. Orthogonal Knowledge Sources
407. Function of Brain Regions
408. Somatotopy: Sensory and Motor
410. Function from Brain Injury
411. Positron Emission Tomography
413. Hubel and Wiesel: Visual Cortex
425. Generalized Hough Transform
432. Adding Semantics to Vision
437. Driving: Detailed Architecture
439. Remote Agent: Architecture
440. Engine / Valve Configurations
441. Understanding Machines from Movies
442. Understanding Machines: Example