| INTRODUCTION |
| L#1 |
Administrivia
AI = the design of rational
agents
|
[1.1, 1.4], (1)
AI in the News
AI (the movie)
What is AI?
|
| L#2a |
Structure
of intelligent agents and environments
|
[2]
|
SEARCH-BASED
AGENTS
(Environment
is observable, deterministic, static, discrete;
... known and modeled using arbitrary code) |
| L#2b |
Problems and problem spaces
"Blind Search"
|
[3.1, 3.2, 3.3], (4)
[3.4 - 3.6]
|
| L#3 |
"Informed Search"
|
[4.1, 4.2]
AIxploratorium
A* (Wikipedia)
|
| L#4 |
Constraint satisfaction search
(CSP)
|
[3.7],
(4.7)
Wikipedia
on Constraint Satisfaction
On-Line
Guide to CP
CP Conference
URL
(EPFL)
URL (Temple)
Tutorial on
CP |
| L#5 |
"Local Search", Stochastic Algorithms
|
[4.3 - 4.4, 7.6]
Selman's page
Boolean
Satisfiability (URL)
|
| L#7 |
Game playing search
(J. Schaeffer)
(RG notes)
(DL notes)
(S Russell notes)
|
[5]
Games Group (UofA)
LA Times article
|
| L#6b |
Summary of search
|
[4.6, 5.5,6.8]
|
LOGICAL
AGENTS
(Environment
is inaccessible, deterministic, *, discrete;
... known and modeled using logical inference) |
L#6
L#8 |
Reasoning,
Logic, Wumpus world
Propositional Logic
(Semantics, Logical Entailment, Proof Process (Resolution))
|
[7],
(2 - 2.5) |
| L#9 |
Predicate Calculus Representation
(Syntax, Semantics, Expressiveness)
|
[8] (3) |
| L#10 |
Predicate Calculus Inference (Resolution)
|
[9] (5) |
| L#11 |
Implemented Systems (Tradeoffs)
|
[10]
OnLine Prolog Resources
Recent Survey
Article
|
| [Omitted] |
Situation Calculus
"Graph Plan"
|
[10.3]
[11.4]
|
| L#12 |
Planning (Simple)
|
[11]
(8)
Recent Survey Article
|
| L#12b
|
Summary of Logical Agents
|
|
DECISION-THEORETICAL
AGENTS
(Environment
is (in)accessible, nondeterministic;
.... known and modeled using belief networks) |
| L#13 |
Introduction
to Decision Theory, Probability (Belief Nets)
|
[13], [14.1] (10.1 - 10.3)
Bayes Rule Applet
|
| L#14 |
Belief Net Intro
|
[14 - 14.3]
See also Cmput652
(Probabilistic Graphical Models) Slides
B-course
RoadMap (dated!)
(Koller
slides)
Relevant URL
Book: Bayesian Networks and Decision Graphs
|
| L#15 |
Belief Net Inference
|
[14.4-14.8]
(Bucket Elimination paper [Dechter])
(Details of Bucket
Elimination)
(Auxiliary notes on Bucket Elimination)
|
| L#16 |
Dynamic Belief Nets
|
[15]
Chapter (K Murphy)
|
| L#17 |
Simple Decisions
(Utility-based Agents, Influence diagrams, Value of
Information)
|
[16], (10.4)
|
| L#18-19 |
Complex (Sequential) Decisions
MDP, Value Iteration, Policy iteration, TD(λ)
(dynamic programming)
|
[17.1- 17.3], [21.2]
Decision Theoretic Planning...[Boutilier'99]
|
| L#19b
|
POMDP, Dynamic decision networks
|
[17.4 - 17.6]
|
| L#20 |
Game Theory & Mechanism Design
|
[17.6-17.7]
URL
Book:
Thinking Strategically
|
| [Omitted] |
Applying Game Theory to Poker,
Network Traffic and Combinatorial Auctions
(R Holte)
|
Approximating Game-Theoretic Optimal Strategies for Full-scale Poker
Combinatorial Auctions, Knapsack Problems, and Hill-climbing search
Network
Routing
|
OTHER TOPICS
(Communicating Agents)) |
| L#21 |
Intro to Natural Language
(G Kondrak)
|
22 (esp. 22.2 - 22.3)
Chart Parser
(Eisner)
|
LEARNING
AGENTS
(Environment
is accessible, ... unknown) |
L#22-
L#23 |
Intro
to Machine Learning
Decision Tree Foundations, Entropy
|
[18.1-18.4], (11)
AIxploratorium
See also Cmput466 Material
|
| L#24 |
Computational Learning Theory
|
[18.5]
|