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CS 440/520: Introduction to Artificial Intelligence - Fall 2014
COURSE INFO
Topics:
Instructor
TheclasswillintroducefundamentalideasthathaveemergedoverthepastfiftyyearsofAIresearch.

Course 440: Introduction To Artificial Intelligence
Lecture 8
Bayesian Networks
Abdeslam Boularias
Friday, October 28, 2016
1 / 63
Outline
We show how graphs can be used to efficiently represent joint probability
distributions with a large number of varia

Computer Science Department
CS440
IntroductiontoArtificialIntelligence
Lecture 2:
Types of Intelligent Agents
& Uninformed Search
19 January 2017
IntelligentAgents
Howdoagentswork?
InthecontextofDataScience
Data Cleaning
and Wrangling
Data as observations

CS 520: INTRODUCTION TO ARTIFICIAL INTELLIGENCE
SPRING 2017
Assignment 1
Fast Trajectory Replanning
Deadline: February 26, 11:55pm.
Perfect score: 100.
Assignment Instructions:
Teams: Assignments should be completed by teams of two students. No additional

ELEMENTARY HINDI II
Department of African, Middle Eastern and South Asian Languages and Literatures
Rutgers, The State University of New Jersey
New Brunswick Campus
Spring 2017
Course Title: Elementary Hindi II
Course Number: 01-013/505-161-02; 01-013/505

Class Information
REMINDERS
No class on Friday; recitations will be held!
SP numbers: if you still need SPN, please send
me an email request (need pre-reqs!);
I need NAME, wanted section, RUID.
In addition, put your information down on list in
front (af

Uninformed Search:
1. Which attributes are typically stored on the nodes of a search tree for uninformed
search? What additional information would you need to store for informed search?
- The nodes of a search tree for uninformed search generally hold the

HW1 Solutions
February 15, 2016
Part 0: Set up the Environments
In this step, we generate the map with Depth-First Search (DFS) approach by using random tie breaking
as required. A map is denoted by a two-dimensional array with a boolean value in each cel

CS520: Introduction to Artificial Intelligence
Assignment 2
Aravind Sivaramakrishnan, Chaitanya Mitash
Spring 2017
Problem 1.
(a)
Note: In the below proofs, while proving admissibility of h, we assume admissibility of h1 and h2 . Likewise, while proving c

Course 16 :198 :520 : Introduction To Artificial Intelligence
Lecture 2
Solving Problems by Searching
Abdeslam Boularias
Friday, September 9, 2016
1 / 43
Solving Problems by Searching (Planning agents)
How can an agent find a sequence of actions that achi

CS512 : Fundamental Algorithms
By James Abello, abello at dimacs dot rutgers dot edu
abelloj at cs dot rutgers dot edu
Pre-requisites: Calculus and Discrete Math and Ch 1, 2, 3 of the Textbook and Chapter 0 of the
reference below.
Textbook: Introduction t

Real-Time Adaptive A*
Sven Koenig
Maxim Likhachev
Computer Science Department
University of Southern California
Los Angeles, CA 90089-0781
Computer Science Department
Carnegie Mellon University
Pittsburgh, PA 15213-3891
[email protected][email protected]
A

Logistics
No more SPNs if you have not received an email from me.
TA office hours and locations
Wang, Yan: Thursdays 2-3pm, Hill 202
Zhang, Han: Thursdays 3:30-4:30pm, CBIM
If you are auditing the class and want to keep track of email
announcements, send

Course 16:198:520: Introduction To Artificial Intelligence
Lecture 10
Temporal Models
Abdeslam Boularias
Wednesday, November 23, 2016
1 / 45
Outline
In the previous lecture on Bayesian, we were concerned about problems
where the values of variables are st

Course 16 :198 :520 : Introduction To Artificial Intelligence
Lecture 6
Solving Problems by Searching:
Constraint Satisfaction Problems
Abdeslam Boularias
Wednesday, October 19, 2016
1/1
Outline
We consider problems where a state is defined as a set of co

Course 440 : Introduction To Artificial Intelligence
Lecture 5
Solving Problems by Searching:
Adversarial Search
Abdeslam Boularias
Friday, October 7, 2016
1 / 24
Outline
We examine the problems that arise when we make decisions in a world
where other age

Course 16 :198 :520 : Introduction To Articial Intelligence
Lecture 6
Solving Problems by Searching:
Constraint Satisfaction Problems
Abdeslam Boularias
Wednesday, September 16, 2015
1 / 32
Outline
We consider problems where a state is dened as a set of c

Course 16 :198 :520 : Introduction To Articial Intelligence
Lecture 4
Solving Problems by Searching:
Beyond Classical Search
Abdeslam Boularias
Monday, September 14, 2015
1 / 34
Outline
We relax the simplifying assumptions of the previous lectures, and we

Course 16 :198 :520 : Introduction To Articial Intelligence
Lecture 2
Solving Problems by Searching
Abdeslam Boularias
Tuesday, September 8, 2015
1 / 41
Solving Problems by Searching (Planning agents)
How can an agent nd a sequence of actions that achieve

Course 16 :198 :520 : Introduction To Articial Intelligence
Lecture 5
Solving Problems by Searching:
Adversarial Search
Abdeslam Boularias
Wednesday, September 16, 2015
1 / 24
Outline
We examine the problems that arise when we make decisions in a world
wh

Course 16:198:520: Introduction To Articial Intelligence
Lecture 10
Temporal Models
Abdeslam Boularias
Monday, October 26, 2015
1 / 48
Outline
In the previous lectures on Bayesian and Markov networks, we were
concerned about problems where the values of v

Course 16:198:520: Introduction To Articial Intelligence
Lecture 9
Markov Networks
Abdeslam Boularias
Monday, October 14, 2015
1 / 51
Overview
Bayesian networks, presented in the previous lecture, are one type of
graphical models.
Bayesian networks are mo

Course 16:198:520: Introduction To Articial Intelligence
Lecture 12
Kalman Filters, Dynamic Bayesian
Networks
Abdeslam Boularias
Monday, November 16, 2015
1 / 40
Example: Tracking the trajectory of a rocket
Objective: identify exactly where the rocket is

CS 520: INTRODUCTION TO ARTIFICIAL INTELLIGENCE
FALL 2015
Assignment 3
Temporal Models - Kalman Filters - Markov Decision Processes
Deadline: December 13, 11:55pm.
Perfect score: 100.
Assignment Instructions:
Teams: Assignments should be completed by team

Course 440 : Introduction To Artificial Intelligence
Lecture 4
Solving Problems by Searching:
Beyond Classical Search
Abdeslam Boularias
Wednesday, October 5, 2016
1 / 34
Outline
We relax the simplifying assumptions of the previous lectures, and we get
cl

Course 440 : Introduction To Artificial Intelligence
Lecture 7
Probabilistic Reasoning
Abdeslam Boularias
Wednesday, October 26, 2016
1 / 17
Outline
We show how to reason and act under uncertainty.
1
Decision-making under uncertainty
2
Basic probability n