Paxson
Spring 2017
CS 161
Computer Security
Homework 3
Due: Wednesday, March 8, at 11:59pm
Instructions. This homework is due Wednesday, March 8, at 11:59pm. No late
homeworks will be accepted. You mu
CS 188
Spring 2010
Final Exam
Solutions
Introduction to
Articial Intelligence
Q1. [14 pts] Search
For the following questions, please choose the best answer (only one answer per question). Assume a ni
pacman.py (original)
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pacman.py
-Licensing Information: You are free to use or extend these projects for
educational purposes provided that (1) you do not distribute or publish
Past Exam Questions: Search
1
Search and Heuristics
Imagine a car-like agent wishes to exit a maze like the one shown below:
The agent is directional and at all times faces some direction d (N, S, E,
Midterm II
Solutions
Introduction to
Articial Intelligence
CS 188
Spring 2012
Q1. [18 pts] Markov Decision Processes
(a) [4 pts] Write out the equations to be used to compute Q from R, T, Vi1 , and to
CS 188
Fall 2011
Introduction to
Articial Intelligence
Midterm Exam
INSTRUCTIONS
You have 3 hours.
The exam is closed book, closed notes except a one-page crib sheet.
Please use non-programmable ca
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pacman.py
-Licensing Information: You are free to use or extend these projects for
educational purposes provided that (1) you do not distribute or publish
solutions, (2) you re
CS 188
Spring 2012
Introduction to
Articial Intelligence
Midterm II
You have 2 hours.
The exam is closed book, closed notes except a one-page crib sheet.
Please use non-programmable calculators onl
Midterm II
Solutions
Introduction to
Articial Intelligence
CS 188
Spring 2012
Q1. [18 pts] Markov Decision Processes
(a) [4 pts] Write out the equations to be used to compute Q from R, T, Vi 1 , and t
CS 188
Spring 2011
Introduction to
Articial Intelligence
Practice Midterm
To earn the extra credit, one of the following has to hold true. Please circle and sign.
A I spent 3 or more hours on the prac
CS 188
Fall 2011
Introduction to
Articial Intelligence
Midterm Exam
INSTRUCTIONS
You have 3 hours.
The exam is closed book, closed notes except a one-page crib sheet.
Please use non-programmable ca
CS188 Fall 2014 Section 2: A* and Heuristics
1
Knights Path
A knight is a chess piece where each move takes the piece 1 square in one direction and 2 squares in an orthogonal
direction. We want to gui
CS 188: Artificial Intelligence
Spring 2011
Lecture 7: Minimax and Alpha-Beta
Search
2/9/2011
Pieter Abbeel UC Berkeley
Many slides adapted from Dan Klein
1
Announcements
W1 out and due Monday 4:59pm
Last name:_ First name:_ SID:_ Class account login:_
Collaborators: _
CS188 Spring 2011 Written 1: Search and CSPs
Due: Monday 2/14, 5:30pm either at the beginning of lecture or in 283 Soda Drop Box.
CS 188
Fall 2014
Introduction to
Articial Intelligence
Section Handout 9
MDP & RL
Policy Iteration
Consider an undiscounted MDP having three states, (1, 2, 3), with rewards 1, 2, 0, respectively. Stat
CS188 Fall 2014 Section 5: Bayesian Networks
1
Green Party President
Its election year again! In a parallel universe the Green Party is running for presidency. Pundits believe that
Green Party preside
CS188 Fall 2017 Section 10: Machine Learning
1
Naive Bayes
In this question, we will train a Naive Bayes classifier to predict class labels Y as a function of input features
A and B. Y , A, and B are
CS 188
Fall 2014
Introduction to
Articial Intelligence
Section 7 Solutions
HMMs and Particle Filtering
Q1. HMMs: Tracking a Jabberwock
You have been put in charge of a Jabberwock for your friend Lewis
CS188 Fall 2014 Section 1: Search
1
Search algorithms in action
For each of the following graph search strategies, work out the order in which states are expanded, as well
as the path returned by grap
CS 188
Introduction to
Articial Intelligence
Section Handout 8
Value Of Information
Used Car Purchase
A used car buyer can decide to carry out various tests with various costs (e.g., kick the tires, t
CS 188
Fall 2014
Introduction to
Articial Intelligence
Sec. 11 Solutions
Q1. Predicting Movie Prots
You want to predict if movies will be protable based on their screenplays. You hire two critics A an
CS 188 Section 10: Decision Trees
1
Decision Trees
In the recursive construction of decision trees, it sometimes happens that a mixed set of positive and negative
examples remains at a leaf node, even
CS188 Fall 2014 Section 6: Inference and Sampling
1
Sampling and Dynamic Bayes Nets
Many people would prefer to eat ice cream on a sunny day than on a rainy day. We can model this situation with
a Bay
CS188 Fall 2014 Section 6: Inference and Sampling
1
Sampling and Dynamic Bayes Nets
Many people would prefer to eat ice cream on a sunny day than on a rainy day. We can model this situation with
a Bay
CS 188 Section 10: Decision Trees
1
Decision Trees
In the recursive construction of decision trees, it sometimes happens that a mixed set of positive and negative
examples remains at a leaf node, even
CS 188 Fall 2017 Section 0 & 1: Search
1
n-Queens
Max Friedrich William Bezzel invented the eight queens puzzle in 1848: place 8 queens on an 8 8 chess board
such that none of them can capture any oth
CS 188
Fall 2014
Introduction to
Articial Intelligence
Section 7
HMMs and Particle Filtering
Q1. HMMs: Tracking a Jabberwock
You have been put in charge of a Jabberwock for your friend Lewis. The Jabb
CS188 Spring 2014 Section 1: Search
1
Search and Heuristics
Imagine a car-like agent wishes to exit a maze like the one shown below:
The agent is directional and at all times faces some direction d (N
CS 188
Fall 2014
Section Handout 9
MDP & RL
Introduction to
Articial Intelligence
Policy Iteration
Consider an undiscounted MDP having three states, (1, 2, 3), with rewards 1, 2, 0, respectively. Stat
CS 188
Introduction to
Articial Intelligence
Section Handout 8
Value Of Information
Used Car Purchase
A used car buyer can decide to carry out various tests with various costs (e.g., kick the tires, t
CS188 Fall 2017 Section 12: Perceptrons / Neural Networks
1
Perceptron
We would like to use a perceptron to train a classifier with 2 features per point and labels +1 or 1. Consider
the following labe
CS188 Fall 2017 Section 13: Deep Learning
1
Neural Network Representations
You are given a number of functions (a-h) of a single variable, x, which are graphed below. The computation
graphs on the fol
CS 188
Spring 2010
Introduction to
Arti_cial Intelligence
Final Exam
INSTRUCTIONS
_ You have 3 hours.
_ The exam is closed book, closed notes except a two-page crib sheet.
_ Please use non-programmabl
CS 188 Introduction to
Fall 2010 Arti_cial Intelligence
Final Exam
INSTRUCTIONS
_ You have 3 hours.
_ The exam is closed book, closed notes except a two-page crib sheet.
_ Please use non-programmable
CS 188 Introduction to
Spring 2009 Artificial Intelligence
Final Exam
INSTRUCTIONS
You have 3 hours.
The exam is closed book, closed notes except a two-page crib sheet, double-sided.
Please use non