CSC242: Intro to AI
Lecture 13
Planning as Satisability
Efcient Satisability Algorithms
Planning as Satisability
SATPLAN
STRIPS
problem
description
encoder
cnf
formula
length
mapping
plan
interpreter
satisfying
model
SAT
engine
Translating STRIPS
Ground
CSC242: Intro to AI
Lecture 10
Exam 1
21-30
31-40 41-50 51-60 61-70 71-80 81-90
ULW
Detailed outline due March 4
HARD DEADLINE!
One full page
Include citations to at least 3 sources
Othello
Thursday Feb 27 by 11:59pm:
One member of team (either one) uploa
CSC 242
Articial Intelligence
Henry Kautz
Spring 2014
Welcome
Instructor: Henry Kautz
Chair, Computer Science
Director, Institute for Data Science
Past President, Association for the Advancement
of Articial Intelligence
Research: combinatorial search algo
Planning 1
CSC 242 AI - Lecture 12
Exam Problem 3(d)
True or False, and explain why: It is okay to use a non-admissible heuristic that
over-estimates the distance to the goal by up to C units if you would be happy
with a solution path that is not more tha
CSC242: Homework 4.1
AIMA 18.1, 18.3
1. For each of the following types of learning, briefly describe what is learned and
what knowledge, data, or feedback the learning agent receives to help it learn.
(a) Unsupervised learning
ANSWER: In unsupervised lea
CSC242: Homework 4.3
AIMA 18.6.318.7
1. Briefly define the following terms:
(a) Decision boundary
ANSWER: A line/path (or surface in higher dimensions) that separates two
classes pf data.
(b) Linear separator
ANSWER: A linear decision boundary: a straight
CSC242: Homework 4.1
AIMA 18.2, 18.4, 18-18.6.2
1. The figure below shows a data set fit with several different function hypotheses.
y=ax3+bx2+cx+d
y=mx+b
(b)
(a)
y=ax+b+csin(x)
(c)
Briefly compare the different hypotheses according to each of the followi
CSC242: Homework 4.4
AIMA 2020.2.2
1. Assume that a probability distribution is being represented as Bayesian network
with a given topology (nodes and edges). What does it mean to learn this distribution?
ANSWER:
A Bayesian network encodes a full joint pr
CSC242: Homework 3.1
AIMA Chapter 13.013.3
1. Summarize briefly why using logic exclusively to formalize a domain like medical
diagnosis is hard.
ANSWER: Laziness (or I would say, the impracticality of writing everything down);
theoretical ignorance (no c
CSC242: Homework 3.4
AIMA 14.5
Project 3 gives you hands-on experience with both exact and approximate inference
in Bayesian Networks. These questions just complement what you learn by doing the
project.
1. Is exact inference for Bayesian networks feasibl
CSC242: Homework 3.3
AIMA Chapter 1414.2, 14.4
1. Exact inference in Bayesian Networks relies on two properties:
X
P(X | e) = P(X, e) =
P(X, e, y)
y
and
P (x1 , . . . , xn ) =
n
Y
P (xi | parents(Xi ).
i=1
Briefly explain what these equations mean and ho
CSC242: Homework 3.2
AIMA Chapter 13.413.6
1. Consider the following full joint probability distribution over the random variables
Cavity, Toothache, and Catch:
toothache
toothache
Cavity
catch
catch
catch
catch
cavity
0.108
0.012
0.072
0.008
cavity
0.016
Game Playing
Lecturer: Ji Liu
Thank Jerry Zhu for his slides
[based on slides from A. Moore http:/www.cs.cmu.edu/~awm/tutorials , C. Dyer, and J. Skrentny]
slide 1
Sadly, not these games
I am here
slide 2
Overview
two-player zero-sum discrete finite deter
CSC242: Intro to AI
Lecture 13
Recap
Elements of FirstOrder Logic
Objects (in the world)
Relations among (tuples of) objects
Mappings from (tuples of) objects to
objects (i.e., objects identied in terms of
other objects)
Connectives
Variables and
CSC242: Articial
Intelligence
Lecture 4
Local Search
Upper Level Writing
Topics due to me by next class!
First draft due Mar 4
Goal: nal paper 15 pages +/- 2 pages
12 pt font, 1.5 line spacing
Get in touch early!
Assignments
Homework 1
Will be posted on B
CSC242: Intro to AI
Lecture 16 Bayesian Networks II
Learning Bayesian
Networks from Data
Kinds of Learning
Problems
Learning the structure of the graph
Learning the numbers in the conditional
probability tables (aka parameter learning)
Kinds of Data
E
CSC242: Intro to AI
Lecture 18:
Details on Decision Trees;
Neural Networks Part I
Details on Learning
Decision Trees
Decision Tree
Each node in the tree represents a test on
a single attribute
Children of the node are labelled with the
possible values
CSC242: Intro to AI
Lecture 20
Reinforcement Learning I
A Joke
A robot walks up to a
counter
and says, Ill have a
beer
The human at the counter
says, I cant serve you a beer
The robot says, Is it because
you discriminate against robots?!
The human says, N
CSC242: Intro to AI
Lecture 19:
Neural Networks Part II
Part I Review
Reserve Readings
Articial Neural
Networks
Chapter 4 of
Machine Learning
by Tom Mitchell
Click here
Then here
is Threshold Function
Linear Classier
w 0 + w 1 x1 + w 2 x2 = 0
wx=0
All
CSC242: Intro to AI
Lecture 5
Adversarial Search
Help Sessions
Grad TA Xiaowan Dong
5:00-6:00 pm Tuesday & Wednesday, CSB 724
Undergrad TAs Sean Esterkin and Dan Scarafoni
2:30-3:30 pm Thursdays, CS Majors Lab CSB 633
5:00-6:00 p.m. Mondays, CS Majors Lab