CAP 4601 Fall 2004

Root View: Course Components
The course syllabus establishes course policies on grading, attendance, and exams. The syllabus should be read in detail at beginning semester.
The course calendar provides a summary temporal view of the course, including textbook coverage, assignments, and due dates. Note that each week in the calendar has a Details link where the assignment is released and any supplemental links are published.
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The FSU/Blackboard Portal, where you will find this course. The course site is the main communication resource for the class. Here you can get help, talk to other students, retrieve your grades, and generally keep up with course news and announcements.
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The textbook for the course is Artificial Intelligence, A Modern Approach, Second Edition, by Stuart Russell and Peter Norvig, Prentice Hall, 2003 (ISBN 0-13-790395-2). Cited as [AIMA].
see weekly details
Assignments will be released through the weekly Details expansion linked from the Calendar. Assignment submission instructions are below.
see weekly details
Some lecture slides have been prepared for this course by the original creator, Dr. Ernest McDuffie. These will be updated and published when appropriate.
Acknowledgments This course was designed, created, and originally taught by Dr. Ernest McDuffie, who is currently with the US Office of Naval Research. Dr. McDuffie is fondly remembered and severely missed by the CS faculty, staff, and students. We are honored to continue to enjoy his collegiality and friendship. Most of the lecture slides (with only slight modification) are from Dr. McDuffie's originals, as are all of the weekly course objectives. The format of the calendar and "weekly details" elaborations are also based on Dr. McDuffie's design (where it was a paper document).
Temporal View: Course Calendar
WeekDatesCoverageAssignmentDue Date
1 Details 8/23 - 8/29 Ch 1 Hw 1 9/5
2 Details 8/30 - 9/5 Ch 2 Hw 1 9/5
3 Details 9/6 - 9/12 Ch 3 Hw 2 9/19
4 Details 9/13 - 9/19 Ch 4 Hw 2 9/19
5 Details 9/20 - 9/26 Ch 7 Hw 3 10/10
6 Details 9/27 - 10/3 Ch 8 Hw 3 10/10
7 Details 10/4 - 10/10 Ch 9 Hw 3 10/10
8 10/11 - 10/17 Midterm Exam: See Syllabus for policy and schedule
9 Details 10/18 - 10/24 Ch 10 Hw 4 11/7
10 Details 10/25 - 10/31 Ch 11, 12 Hw 4 11/7
11 Details 11/1 - 11/7 Ch 13, 14 Hw 4 11/7
12 Details 11/8 - 11/14 Ch 16 Hw 5 11/28
13 Details 11/15 - 11/21 Ch 18 Hw 5 11/28
14 Details 11/22 - 11/28 Ch 20 Hw 5 11/28
15 Details 11/29 - 12/5 Ch 26, 27 NA NA
16 12/6 - 12/12 Final Exam: See Syllabus for policy and schedule
17 12/13 - 1/2 Semester Break: Have a wonderful holiday.
Details: Week 1
Topic:Artificial Intelligence: Introduction
  1. Characterize AI definitions according to specified dimensions.
  2. Decide whether computers can accomplish specified tasks according to the current literature.
  3. Discuss the concept of intelligence as it relates to computers.
  4. Discuss the concept of intelligence as it relates to animals.
  5. Discuss the difference between a performance measure and a utility function.
Reading:Chapter 1 of [AIMA]
Supplemental:McDuffie Lecture Slides
Assignment:Homework 1 (partial): Exercises 1.1, 1.3, 1.7, 1.10, 1.11
Items Due:No deliverables this week, but you should complete the portion of the assignment from Chapter 1.
Details: Week 2
Topic:Artificial Intelligence: Intelligent Agents
  1. Given an environment, decide which agent is most appropriate.
  2. Describe an agent that is appropriate for a familiar domain.
  3. Given a list of policies, decide which is best. Discuss how you reached the decision.
  4. Given a domain, calculate the size of the table for a look-up agent.
  5. Discuss the sequential relationship between goal formulation and problem formulation.
Reading:Chapter 2 of [AIMA]
Supplemental:McDuffie Lecture Slides
Assignment:Homework 1 (continued): Exercises 2.1, 2.2, 2.3, 2.4, 2.5
Items Due:Homework 1
Details: Week 3
Topic:Problem Solving: Solving Problems by Searching
  1. Given a problem set, determine the following: initial state, goal test, operators, and path cost function for each problem.
  2. Write and apply a version of the General-Search algorithm.
  3. Describe a search space in which iterative deepening search performs much worse than depth-first search.
  4. Given a bidirectional search, describe an alternative direction to the breadth-first search.
  5. Given a greedy search algorithm with h(n)= -g(n), describe the search that the greedy search will emulate.
Reading:Chapter 3 of AIMA
Supplemental:McDuffie Lecture Slides
Assignment: Homework 2 (partial): Exercises 3.1, 3.2, 3.5, 3.8, 3.9
Items Due:No items due, but the first half of Homework 2 has been assigned.
Details: Week 4
Topic:Problem Solving: Informed Search and Exploration
  1. Given a situation in which no good evaluation function for a problem exists, show that a comparison method is appropriate for a best-first search.
  2. Given that an admissible heuristic leads to monotonically nondecreasing f values along any path, discuss whether the implication goes the other way.
  3. Discuss features of a bidirectional A* search.
Reading:Chapters 4 of AIMA
Supplemental: McDuffie Lecture Slides, Part a   Slides only available in pdf format, the PP source has been lost
McDuffie Lecture Slides, Part b   Slides only available in pdf format, the PP source has been lost
Assignment:Homework 2 (continued): Exercises 4.1, 4.2, 4.3, 4.12
Items Due:Homework 2
Details: Week 5
Topic:Knowledge and Reasoning: Logical Agents, Propositional Logic
  1. Describe how to use a truth table to decide whether a given sentence is valid, satisfiable, or unsatisfiable.
  2. Given a set of sentences, use truth tables to show that each sentence is valid and that the equivalences hold.
  3. Given a set of sentences, decide whether each is valid, unsatisifiable, or neither using truth tables or equivalence rules.
  4. Given a series of if-then statements about a unicorn, discuss whether the following qualities of the unicorn are provable: mythical, magical, horned.
Reading:Chapter 7 of AIMA
Assignment:Homework 3 (first third): Exercises 7.1, 7.4, 7.8, 7.9
Items Due: No items due, but the first part of Homework 3 has been assigned.
Details: Week 6
Topic:Knowledge and Reasoning: First-Order Logic
  1. Given a set of sentences, represent each in first-order logic, defining and using a consistent vocabulary.
  2. Given the sentence, "All Germans speak the same languages," represent the sentence in predicate calculus.
  3. Given the blocks-world domain, formalize the domain using the situation calculus.
Reading:Chapter 8 of AIMA
Assignment: Homework 3 (middle third): Exercises 8.2, 8.6, 8.7, 8.16
Items Due: No items due, but the second part of Homework 3 has been assigned.
Details: Week 7
Topic:Logic and Reasoning: Inference in First-Order Logic
  1. Discuss how to use resolution to show that a sentence is valid or unsatisfiable
  2. Discuss forward chaining in Propositional and First-Order Logic
  3. Discuss backward chaining in Propositional and First-Order Logic
Reading:Chapter 9 of AIMA
Assignment: Homework 3 (last third): Exercises 9.4, 9.5, 9.19 (a,b,c,d,e only)
Items Due:Homework 3
Details: Week 8
Topic:Midterm Exam: See Syllabus for policy and schedule
Details: Week 9
Topic:Knowledge Representation
  1. Describe the concept of Semantic Network
Reading:Section 10.5, 10.6 of AIMA
Assignment: Homework 4 (Part 1): Exercise 10.17
Items Due:No items due this week
Details: Week 10
  1. Use the STRIPS language to represent steps in a plan
  2. Explain the relationships among POP, Planning Graphs, and GRAPHPLAN
  3. What do "STRIPS" and "POP" stand for anyway?
  4. Criticise the statement: planning = search + logic
Reading:Chapter 11, 12 (skim only) of AIMA
Assignment: Homework 4 (Part 2): Exercise 11.4, 11.12
Items Due:No items due this week
Details: Week 11
Topic:Reasoning under Uncertainty
  1. Understand basic probability, including Bayes Rule and the Product Rule
  2. State the Kolmogorov Axioms
  3. State the Product Rule
  4. State Bayes' Rule, and derive it from the Product Rule
  5. Describe Bayesian Network
Reading:Chapter 13, 14 (Bayesian Networks) of AIMA
Assignment: Homework 4 (Part 3): Exercises 13.2, 13.4, 14.1 (parts a, b, c only)
Items Due:Homework 4
Details: Week 12
Topic:Making Decisions
  1. Define or state:
    1. The expected utility of an action
    2. The principle of Maximum Expected Utility (MEU)
    3. Rational behavior in terms of MEU
    4. The axioms of utility theory
  2. Explain how the axioms of utility theory imply the existence of a utility finction
  3. Set up and evaluate a decision network for a given decision problem
  4. Describe informally how information is valued in a decision environment
Reading:Chapter 16 of AIMA (skim)
Assignment: Homework 5 (Part 1): (empty)
Items Due:No items due this week
Details: Week 13
Topic:Machine Learning
  1. Describe the general learning model. Given a common human learning environment (infant learning a language, adult learning to drive), explain how it fits into the general learning model, identifying components of the model as appropriate.
  2. Given a learning algorithm and a sample domain, implement the algorithm and assess its operation.
  3. Describe the decision tree learning algorithm
Reading:Chapter 18 of AIMA
Assignment: Homework 5 (Part 2): 18.1, 18.3, 18.4
Items Due:No items due this week
Details: Week 14
Topic:Nearest Neighbor and Neural Network Learning
  1. Describe and implement the k-nearest neighbor learning algorithm
  2. Trace the perceptron learning algorithm
  3. Construct a 2-layer neural network that computes a given binary logic function
Reading:Sections 20.4, 20.5 of AIMA
Assignment: Homework 5 (Part 3): 20.11
Items Due:Homework 5
Details: Week 15
Topic:Stepping Back
  1. Contrast the concepts weak AI and strong AI
  2. Discuss is the mind-body problem and associated dualist and monist theories
  3. Discuss 6 potential threats to society posed by AI and related technology.
  4. Discuss the potential good / risk of AI for the future in terms of the question: Should AI progress be encouraged?
Reading:Chapters 26, 27 of AIMA
Assignment:Spend some time contemplating your understanding of and views toward AI, before and after this course.
Items Due:No items due this week
Details: Week 16
Topic:Final Exam: See Syllabus for policy and schedule

Blackboard Dropbox Submission Process:

Assignments should be submitted via the Drop Box on the course web site in Blackboard no later than 11:59:59 pm on the deadline day. The time stamp placed there by the receiving server will be the determining factor. (It is currently using Eastern time.)

Note the distinction between uploading file to the dropbox and actually submitting the files. Files that are uploaded but not submitted will still have the "remove" option displayed. Such files are not visible to the instructors.

Files should be of either text or pdf format. File names should be in the form <id>hw<nn>[<clarifier>] where <id> is your Blackboard login name, <nn> is a digit for the number of the assignment, and [<clarifier>] is an optional tag to further identify a file. The appropriate suffix indicating the file type should follow the name.

Example file name: abc03hw2.txt - Homework Assignment 2 submitted by abc03 in text format.