CS 440 / ECE 448: Introduction to AI
HW 0
Due: Tuesday, January 31, 2012 (in class)
Problem 1 (50 points).
Implement a SAT solver from scratch. Your solver should read a satisability problem in conjunctive normal form, in DIMACS format (see below), from
s
CS 440 / ECE 448: Introduction to AI
HW 1 Solutions
Problem 1. Consider the search tree in Figure 1. Node A is the root of the
tree and the double circled nodes are the goal states. The number inside each
node represents the value of the heuristic for tha
CS 440 / ECE 448: Introduction to AI
HW 1
Due: Tuesday, February 14, 2012 (in class)
Problem 1.(50 Points)
In this problem, you are going to use Prolog to represent facts in clausal form
and answer queries based on those facts.
Prolog is a declarative pro
Probabilistic reasoning over time
Ch. 15, 17
Probabilistic reasoning over time
So far, weve mostly dealt with episodic
environments
One exception: games with multiple moves
In particular, the Bayesian networks weve seen
so far describe static situation
Bayesian inference
A general scenario:
Query variables: X
Evidence (observed) variables and their values: E = e
Unobserved variables: Y
Inference problem: answer questions about the query
variables given the evidence variables
This can be done using the p
CS440/ECE448 Spring 2015 Final Review
1. Use the axioms of probability to prove that P(A) = 1 P(A).
Solution
Summary of axioms:
i)
P(A) 0 for all events A;
ii)
P()=1;
iii)
If AB = empty then P(A U B) = P(A) + P(B).
P(A)+P(A) = P(A U A) = P() = 1 (1st equa
Another Bayes net example
Variables: Cloudy, Sprinkler, Rain, Wet Grass
Review: Probabilistic inference
A general scenario:
Query variables: X
Evidence (observed) variables and their values: E = e
Unobserved variables: Y
Inference problem: answer questio
CS440/ECE448: Artificial Intelligence
Lecture
2:
History
and
Themes
Slides by Svetlana Lazebnik, 9/2016
Modified by Mark HasegawaJohnson, 8/2017
Last time: What is AI?
Thinking Humanly?
Examples: embodied cognition, trying to reconstruct a brain cellb
HW5 Solutions
1. a) the network is as below (1 point, 0.5 for minor errors i.e. adding 1 or 2 edges that
shouldnt exist, 0 point if the network structure is completely wrong)
b) the minimum number of parameters is (0.5 point)
1(smoking)+2(lungCancer)+2(co
Name_
CS440 Spring 2015 Final (Out of 50 points, max 52 possible)
1. Mark each statement as True or False, and write a brief (one or two sentence) explanation.
2 points each (1 for T/F, 1 for explanation)
a. For any two events A and B, P(A B) = P(A) + P
Markov Decision Processes
(Chapter 17)
Image source: P. Abbeel and D. Klein
Markov Decision Processes
In HMMs, we see a sequence of observations and
try to reason about the underlying state sequence
There are no actions involved
But what if we have to
Planning (Chapter 10)
http:/en.wikipedia.org/wiki/Rube_Goldberg_machine
Planning
Problem: Im at home and I need milk, bananas,
and a drill.
How is planning different from regular search?
States and action sequences typically have complex internal
struc
Planning (Chapter 10)
http:/en.wikipedia.org/wiki/Rube_Goldberg_machine
Planning
Problem: Im at home and I need milk, bananas,
and a drill.
How is planning different from regular search?
States and action sequences typically have complex internal
struc
Review: Probability
Random variables, events
Axioms of probability
Atomic events
Joint and marginal probability distributions
Conditional probability distributions
Product rule, chain rule
Independence and conditional independence
Bayesian inference,
Nave
5
CONSTRAINT
SATISFACTION PROBLEMS
In which we see how treating states as more than just little black boxes leads to the
invention of a range of powerful new search methods and a deeper understanding
of problem structure and complexity.
BLACK BOX
REPRESEN
Review: Bayes networks
Bayes network inference
A general scenario:



Query variables: X
Evidence (observed) variables and their values: E = e
Unobserved variables: Y
Inference problem: answer questions about the query
variables given the evidence vari
ECE 448 Lecture 3:
Rational Agents
Slides by Svetlana Lazebnik, 9/2016
Modified by Mark HasegawaJohnson, 9/2017
Contents
Agent = Performance, Environment, Actions, Sensors (PEAS)
What makes an agent Rational?
What makes an agent Autonomous?
Types of
CS440/ECE448: Artificial Intelligence
Lecture
1:
What
is
AI?
Slides by Svetlana Lazebnik, 9/2016
Modified by Mark HasegawaJohnson, 8/2017
CS440/ECE448: Artificial Intelligence
Website:
http:/courses.engr.Illinois.edu/ece448/
What is AI?
Candidate defini
Uninformed search strategies
(Sec3on 3.4)
Source: Fotolia
Uninformed search strategies
A search strategy is dened by picking the
order of node expansion
Uninformed search strategies use only the
informa3on available in
CS440/ECE448
Midterm Review
Fall 2016
Things to know
Review of topics
Review exam
Topics
1. Intro to AI
2. Search
3. Constraint Satisfaction Problems
4. Games
5. Game Theory
6. Planning
1. Intro to AI
AI = human thought, human action, rational thought,
Where are we in CS 440?
Now leaving: sequential, deterministic reasoning
Entering: probabilistic reasoning and machine learning
Probability: Review of main
concepts (Chapter 13)
Outline
Motivation: Why use probability?
Laziness, Ignorance, and Randomn
Machine learning
Image source: https:/www.coursera.org/course/ml
Machine learning
Definition
Getting a computer to do well on a task
without explicitly programming it
Improving performance on a task based on
experience
Learning for episodic tasks
We h
Homework 3
Assigned 2/7/2017
Due 2/21/2017 11:59PM
Extra Credit #1 2/14/2017
Extra Credit #2 2/19/2017
This homework contains both a machine problem and a written component.
Extra Credit #1 (due 2/14/2017)
Code for the MP + First 2 written questions
Extra
Psychoacoustic, Encrypted Symmetries for
Scatter/Gather I/O
Abstract
of this type of approach, however, is that
IPv6 and the memory bus can interact to accomplish this ambition. Certainly, though
conventional wisdom states that this quagmire is regularly
Decoupling 802.11B from Architecture in Expert
Systems
A BSTRACT
In recent years, much research has been devoted to the analysis of online algorithms; unfortunately, few have emulated the
exploration of extreme programming. Here, we disconfirm the
study o
The Turing Machine Considered Harmful
Abstract
We explore a novel heuristic for the development
of RPCs, which we call Hue. Contrarily, this solution
is largely wellreceived. It should be noted that Hue
simulates multimodal configurations. Even though
si
Big Data in Society
Despite what many might think, Big Data is well known across hundreds of nations all over the
world. Big Data has been around for several centuries and has a very important meaning in the
lives of many. It would be safe to assume that
ECE 448, Lecture 7:
Constraint Satisfaction Problems
Slides by Svetlana Lazebnik, 9/2016
Modifiedy by Mark HasegawaJohnson, 9/2017
Content
What is a CSP? Why is it search? Why is it special?
Examples: Map Task, NQueens, Crytparithmetic, Classroom
Assi
CS440/ECE448 Lecture 9:
Minimax Search
Slides by Svetlana Lazebnik 9/2016
Modified by Mark HasegawaJohnson 9/2017
Why study games?
Games are a traditional hallmark of intelligence
Games are easy to formalize
Games can be a good model of realworld com
Review: Tree search
Ini0alize the fron&er using the star&ng state
While the fron0er is not empty
Choose a fron0er node to expand according to search strategy
and take it o the fron0er
If the node contains the