CS 453: Artificial Intelligence, Spring 2005
Information About the Third Test
The third and final test for this course will be given in class on Friday, April 29. It will cover the following material:
- Chapter 8.
- Chapter 10, Sections 1 and 2.
- Chapter 18, Sections 1 to 3.
- Neural nets, as covered in class and in Section 20.5.
- Handout on Knowledge Representation, sections 1 to 10.
- Handout on Natural-Language Understanding, sections 1 to 4.
- Alan Turing's paper, "Computing Machinery and Intelligence"
There will be no programming on this test. You can expect some questions on neural nets, but you do not need to know all the mathematical detail from Section 20.5. There will probably be something on knowledge representation using specific techniques such as sematic nets, predicate logic, or Lisp expressions. There will be no detailed questions about formal grammars and parsing, but you should be familiar with the general idea, including semantic grammars. Other than that, the test will consist of definitions, short-answer questions, and essay questions.
Here are some of the terms and ideas that you should be familiar with:Predicate logic natural language processing constant symbols natural vs. artificial languages function symbols problem of understanding predicates machine translation logical operators natural language interfaces for-all and there-exists acoustic level variables phonemes models for predicate logic morphemes entailment in predicate logic lexemes (words) expressing "isa" in logic syntax semantics knowledge representation pragmatics production rules grammars and parsing concept hierarchies ambiguity "isa" "times flies like an arrow" "instance of" semantic grammars "part of" semantic primitives inheritance in isa hierarchies (PTRANS, ATRANS, MTRANS...) frames using frames to represent semantics slots and fillers using semantic nets for semantics default fillers scripts semantic nets difficult issues in semantics quantity learning time and space learning versus memorizing facts knowledge and belief types of learning pronoun references supervised (training examples) conversational context unsupervised (concept discovery) reinforcement (reward/punishment) the Turing Test (aka "imitation game") learning classifications arguments against AI learning concepts the mathematical objection Ockham's Razor the argument from consciousness decision tree arguments from various disabilities IDE3 algorithm Lady Lovelace's objection argument from continuity in the nervous system neural nets argument from informality of behavior the basic neural net node inputs and output weights bias input activation function threshold functions sigmoid function McCulloch-Pitts neuron perceptron the perceptron learning rule learning rate (alpha) hidden layers back-propagation learning