HW3: Probability and Bayes Nets
Due 04/26/2012
Please indicate the CEC ID of all group members clearly on the submission.
Only one submission per group is required. Group size is not limited.
Pr
CSE 511A: Introduction to Artificial Intelligence
Particle Filters and Applications of HMMs
Instructor: Roman Garnett
Washington University in St. Louis
[These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. All CS
Announcements
Homework 3: Games
Due tonight at 11:59pm.
Project 2: Multi-Agent Pacman
Has been released, due Friday 2/21 at 5:00pm.
Optional mini-contest, due Sunday 2/23 at 11:59pm.
edX and Pandagrader grade consolidation survey
Due today.
CS 188:
CSE 511A: Introduction to Artificial Intelligence
Probability
Instructor: Roman Garnett - Washington University in St. Louis
[These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. All CS188 materials are available
CSE 511A: Introduction to Artificial Intelligence
Reinforcement Learning
Instructor: Roman Garnett
Washington University in St. Louis
[These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. All CS188 materials are a
Announcements
Homework 3: Games
In observation of Presidents Day, due Tuesday 2/18 at 11:59pm.
Project 2: Multi-Agent Pacman
Has been released, due Friday 2/21 at 5:00pm.
Optional mini-contest, due Sunday 2/23 at 11:59pm.
edX and Pandagrader grade c
CSE 511A: Introduction to Artificial Intelligence
Informed Search
Instructors: Roman Garnett
Washington University in St. Louis
[These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. All CS188 materials are availab
CSE 511A: Introduction to Artificial Intelligence
Decision Networks and Value of Perfect Information
Instructor: Roman Garnett
Washington University in St. Louis
[These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkele
CSE 511A: Introduction to Artificial Intelligence
Reinforcement Learning II
Instructor: Roman Garnett - University of California, Berkeley
[These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. All CS188 materials
Project 1 Mini-Contest
Fun Names in 11-31
^_^
L)cfw_ :|:&;:
noeffort
AimingForLast
Alex Sawai
Craig Hiller and Chris Hsu
Jason McGhee
Jinyu Oh [15]
Top-10
10 TuPAC
Kasey Moffat
9 test111
Boyi Chen
8 ILIKEBIGBOTSANDICANNOTLIE Keonhwa Song and Y. S. Kim
CSE 511A: Introduction to Artificial Intelligence
Bayes Nets: Independence
Instructor: Roman Garnett - Washington University in St. Louis
[These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. All CS188 materials a
So what is AI?
What is AI?
Views of AI fall into four categories:
Thinking humanly Thinking rationally
Acting humanly Acting rationally
The textbook advocates "acting rationally"
State of the art: Game playing
State of the art: Robots
http:/robots.stanfo
HW2: Probability and Bayes Nets
Please indicate the CEC ID of all group members clearly on the submission.
Only one submission per group is required. Group size is not limited.
Problem 1: Prob
Student Details
Name
SOLUTIONS
CEC login
Instructions
You have roughly 1 minute per point, so schedule your time accordingly.
There is only one correct answer per question.
Good luck!
Question 1. S
HW3: Probability and Bayes Nets
Due 04/26/2012
Please indicate the CEC ID of all group members clearly on the submission.
Only one submission per group is required. Group size is not limited.
Pr
Reading Material
Sections 3.3 3.5
Optimal Rectangle Packing: New Results
By R. Korf (optional)
Optimal Rectangle Packing: A Meta-CSP
Approach (optional)
Sections 4.1 4.2
Best-first search
Idea: use an evaluation function f(n) for each node
estimate o
Reading Material
Sections 3.3 3.5
Optimal Rectangle Packing: New Results
By R. Korf (optional)
Optimal Rectangle Packing: A Meta-CSP
Approach (optional)
Sections 4.1 4.2
Best-first search
Idea: use an evaluation function f(n) for each node
estimate o
Reading assignment
Chapters 1, 2
Sections 3.1 and 3.2
What is artificial intelligence
Act rationally
Integrate sub-areas in AI into intelligent
agents
A full breath of potential applications
Play games
Control space-rovers
Cure cancer
Trade stock
CSE 511A: Introduction to Artificial Intelligence
Markov Models
Instructor: Roman Garnett - Washington University in St. Louis
[These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. All CS188 materials are availabl
