Week

Tuesday

 Thursday 

Topics

Text

Assignments

1

Sept 2

Sept 4

Intro to AI

• What Is AI?
• The Turing Test
• Rational Agents
• Human Intelligence vs AI

Intelligent Agents

• Goals in AI
• Agents and Environments
• Rationality
• PEAS
• Environment Types
• Agent Types (Optional)

AIMA 1,2

2

Sept 9

Sept 11

• The GNU Prolog Website
• GNU Prolog vs Turbo Prolog
  
• Propositional logic
• Prolog program structure
• Clauses: facts and rules
• Queries
• Backtracking
• Variables
• Unification with variables
• Backtracking with variables
• A family DB: examples

Prolog Notes
&
Turbo Prolog

3

Sept 16

Sept 18

• I/O in Prolog
• not
• The use of ;
• The use of anonymous variables
• Bound and unbound variables
• Debugging with Turbo Prolog
• Arithmetic predicates
• Recursion
• List processing

Assignment 1
(due 9/29 at 5pm)

4

Sept 23

Sept 25

• fail
• Negation by failure (optional)
• cut
• Logical OR (;)
• Database facts and file handling
• findall
• bagof and setof (optional)
• Prolog programming style
• Features of Logic Programming

5

Sept 30

Oct 2

Problem Solving and Uninformed Search

• Problem-solving agents
• Problem types
• Tree search algorithms
• Uninformed search: BFS, uniform-cost search, DFS, iterative deepening

Informed Search

• Best-first search
  • Greedy search
  • A^* search
• Heuristics
• Local search
  • Hill-climbing
  • Simulated annealing

AIMA 3,4

Assignment 2
(due 10/13)

6

Oct 7

Oct 9
In-Class Exam (1)

Game Playing

• Games vs search
• Minimax
• a-b pruning
• Games of imperfect information

AIMA 6

7

Oct 14

Oct 16

• Expert Systems
• Knowledge Representation
• Production Systems
• CLIPS
• Reasoning under Uncertainty
• Uncertainty Notes from AIME2e Authors (optional)

KBS
&
CLIPS
&
LISP

8

Oct 21

Oct 23

9

Oct 28

Oct 30

10

Nov 4

Nov 6

Planning

• Search vs planning
• The STRIPS Language
• Partial-order planning (POP)

Planning and Acting

• Conditional planning
• Monitoring and replanning

AIMA 11,12

11

Nov 11

Nov 13
In-Class Exam (2)

AIMA 22
Lecture 2

12

Nov 18

Nov 20

Knowledge Acquisition and Machine Learning

• The knowledge bottleneck problem
• Three types of knowledge acquisition
• The knowledge engineering process
• Knowledge acquisition by interview
• Interactive knowledge transfer
• Rule Induction
• Knowledge refinement
• Learning strategies

KA&ML

13

Dec 2

Dec 4

• Induction paradigms
• Processing real-valued attributes
• Noise handling
• Deduction of induction results

ILDB

14

Dec 9

Dec 11

Preparation for the Final Exam

Week

Tuesday

 Thursday 

Topics

Text

Assignments