Grad AI. 15-780
Fall, 2007
Homework 5
Homework deadline: 11:59am on December 7
Please hand in a hard copy of this homework. You need not submit any code by e-mail.
1. POMDPs (50 pts)
Figure 1: Tiger and pot-of-gold POMDP
Consider the popular tiger and p

15-780: Graduate AI
Lecture 2. Proofs & FOL
Geoff Gordon (this lecture)
Tuomas Sandholm
TAs Erik Zawadzki, Abe Othman
1
Admin
Recitations: Fri. 3PM here (GHC 4307)
Vote: useful to have one tomorrow?
would cover propositional & FO logic
Draft schedule of d

15-780 Homework 4 Deadline: 10:30 am on April 2 (Thursday) There are 100 total points: point values are listed with each question. 1. Planning (50 pts) Congratulations, your class project has just been accepted at a prestigious conference to be held in To

Constraint Satisfaction Problems
Tuomas Sandholm Carnegie Mellon University Computer Science Department [Read chapter 5 of Russell & Norvig]
Constraint satisfaction problems (CSPs)
Standard search problem: state is a "black box" any data
structure that s

15-780 Homework 5 Deadline: 10:30 am on April 30 (Thursday) There are 100 total points: point values are listed with each question. 1. Split or Steal (45 pts) This question will refer to the following video clip: http:/www.youtube.com/watch?v=p3Uos2fzIJ0

15-780 Homework 1
Deadline: 10:30 am on February 3
There are 150 total points: each component of each question is worth 10 points.
1) Consider the following statement:
The bouncers checked the IDs of everyone who entered the club who wasnt a VIP. Some men

15-780: Grad AI
Lecture 11: Optimization, Duality
Geoff Gordon (this lecture)
Tuomas Sandholm
TAs Sam Ganzfried, Byron Boots
1
Admin
2
Alexandra Marilyn Gordon
3
Project proposals
Due today
4
HW3
Questions?
5
LPs, MILPs,
and their ilk
6
Recall
Linear prog

Grad AI. 15-780
Fall, 2007
Homework 3
Homework deadline: 10:30am on November 1
Please print your code and hand it in with the hard copy of your homework. Also send a
copy of your code by e-mail to both TAs ([email protected] and [email protected]).

Grad AI. 15-780
Fall, 2007
Homework 4
Homework deadline: 10:30am on November 13
Please print your code and hand it in with the hard copy of your homework. Also send a
copy of your code by e-mail to both TAs ([email protected] and [email protected])

Grad AI. 15-780
Fall, 2007
Homework 2
Homework deadline: 10:30am on October 16
Please hand in a hard copy of this homework. You need not submit any code by e-mail.
1. First-Order Logic (40 pts)
(a) Represent the following sentences in rst-order logic, u

Grad AI. 15-780
Fall, 2007
Homework 1
Homework deadline: 10:30am on October 4
Please print your code and hand it in with the hard copy of your homework. Also send a
copy of your code by e-mail to both TAs ([email protected] and [email protected]).

Grad AI. 15-780
Fall, 2006
Homework 5
Homework deadline: 10:30am on Dec. 6
Please do one of the following two problems (Computational Game Theory or Computational Biology). (Students who do both problems will receive 40% bonus credit for
the problem on

Name:
Andrew/CS ID:
15-780 Midterm Solutions, Fall 2006
November 15, 2006
Place your name and your andrew/cs email address on the front page.
The exam is open-book, open-notes, no electronics other than calculators.
The maximum possible score on this e

Grad AI. 15-780
Fall, 2006
Homework 3
Homework deadline: 10:30am on Nov. 1
Please print your code and hand it in with the hard copy of your homework. Also send
a copy of your code by e-mail to both TAs.
1. Bayesian Networks [33 pts]. This problem involv

Grad AI. 15-780
Fall, 2006
Homework 4
Homework deadline: 10:30am on Nov 8
1. Hidden Markov Model [40 pts]. In class we dened a forward looking variable
t+1 (i) = P (O1 , , Ot+1 qt+1 = si ). We also dened a backward looking variable
t (i) = P (Ot+1 , , On

Grad AI. 15-780
Fall, 2006
Homework 1
Homework deadline: 10:30am on Oct 4
Please download Matlab helper code and data les at class website
Please print your code and hand it in with the hard copy of your homework. Also send
a copy of your code by e-mai

15-780 Homework 3 Deadline: 10:30 am on March 3 (Tuesday) There are 240 total points: point values are listed with each question. 1) Please do problems 4.1-4.7 from the Russel/Norvig textbook (20 pts each). Note: For problem 4.2 assume that h(n) is admiss

15-780 Homework 2
Deadline: 10:30 am on February 17 (Tuesday)
There are 240 total points: point values are listed with each question.
1) Suppose we are given a search problem whose search space is a tree T with branching factor b and
total depth m. Assume

Informed search methods
Tuomas Sandholm
Computer Science Department
Carnegie Mellon University
Read Section 3.5-3.7 of Russell and Norvig
Informed Search Methods
Heuristic = to find, to discover
Heuristic has many meanings in general
How to come up with

Advanced informed search
Tuomas Sandholm
Computer Science Department
Carnegie Mellon University
Read: Optimal Winner Determination Algorithms.
Sandholm, T. 2006.
Chapter 14 of the book Combinatorial Auctions,
Cramton, Shoham, and Steinberg, editors, MIT P

15-780: Grad AI
Lec. 8: Linear programs, Duality
Geoff Gordon (this lecture)
Tuomas Sandholm
TAs Erik Zawadzki, Abe Othman
Admin
Test your handin directories
/afs/cs/user/aothman/dropbox/USERID/
where USERID is your Andrew ID
Poster session:
Mon 5/2,

15-780: Grad AI
Lec. 9: Linear programs, Duality
Geoff Gordon (this lecture)
Tuomas Sandholm
TAs Erik Zawadzki, Abe Othman
Admin
Have you tested your handin directories?
/afs/cs/user/aothman/dropbox/USERID/
where USERID is your Andrew ID
Poster sessio

15-780: Graduate AI
Lecture 1. Intro & Logic
Geoff Gordon (this lecture)
Tuomas Sandholm
TAs Erik Zawadzki, Abe Othman
1
Admin
2
Website
15-780 Graduate AI
Spring 2011
Tuesdays and Thursdays from 10:30-Noon in GHC 4307.
School of Computer Science, Carnegi

Advanced informed search
Tuomas Sandholm
Computer Science Department
Carnegie Mellon University
Read: Optimal Winner Determination Algorithms.
Sandholm, T. 2006.
Chapter 14 of the book Combinatorial Auctions,
Cramton, Shoham, and Steinberg, editors, MI

Informed search methods
Tuomas Sandholm
Computer Science Department
Carnegie Mellon University
Read Chapter 4 of Russell and Norvig
Informed Search Methods
Heuristic = to find, to discover
Heuristic has many meanings in general
How to come up with mathe

15-780: Grad AI
Lecture 13: Duality; Planning
Geoff Gordon (this lecture)
Tuomas Sandholm
TAs Sam Ganzfried, Byron Boots
1
Review
2
Branch & bound (& cut)
Worked examples
Demonstrated how to simulate resolution
and therefore DPLL+CL
3
MILP examples
Path p

Search I
Tuomas Sandholm
Carnegie Mellon University
Computer Science Department
[Read Russell & Norvig Chapter 3]
Search I
Goal-based agent (problem solving agent)
Goal formulation (from preferences). Romania example, (Arad Bucharest)
Problem formulation:

15-780: Graduate AI
Lecture 4. SAT, Reductions
Geoff Gordon (this lecture)
Tuomas Sandholm
TAs Byron Boots, Sam Ganzfried
1
Admin
2
HW1
Reminder: HW1 is out
Download from course website
Well provide hardcopies on request
3
Review
4
Proofs in FOL
Proof by