CMPT 310 - Articial Intelligence Survey
Assignment 2
Due: 11:00 am on February 17, 2014
Instructor: Mehrdad Oveisi
Spring 2014
The centuries-old problem of Knights Tour is to nd a sequence of valid knight moves on a chessboard.
Although a standard chessbo

CMPT 310 - Articial Intelligence Survey
Assignment 4
Due: 11:00 am on April 7, 2014
Instructor: Mehrdad Oveisi
Spring 2014
Question 1 [2 marks] Consider a FOL knowledge base containing just two sentences: P (A) and P (B).
Does this knowledge base entail x

CMPT 310 - Articial Intelligence Survey
Assignment 1 Solution Key
Instructor: Mehrdad Oveisi
Spring 2014
1. [9 points] Question 2.3 from the text
(a) An agent that senses only partial information about the state cannot be perfectly rational.
False. Perfec

CMPT 310 - Articial Intelligence Survey
Assignment 1
Due: 11:00 am on January 20, 2014
Mehrdad Oveisi
Spring 2014
1. [9 points] Question 2.3 from the text
2. [11 points in total] Implement the following functions using the Racket language. You must use
re

CMPT 310, Spring 2015, SFU Burnaby
Instructor: Diana Cukierman.
Solutions Search assignment, Question 2
Figure 1 shows the grid where the nodes are numbered. The order of operators determines the
order of neighbors among them when they are rst obtained: u

CMPT 310 - Articial Intelligence Survey
Assignment 3 Solution Key
Instructor: Mehrdad Oveisi
Spring 2014
Question 1 [3 marks] Do the rst part of exercise 7.20 in the textbook
The CNF representations are as follows:
S1: (A B E) (B A) (E A).
S2: (E D).
S3:

CMPT 310, Fall 2015, SFU Burnaby
Instructor: Diana Cukierman
TAKE HOME PORTION OF MIDTERM - Due Tuesday Oct 27, 11:59 PM
This assignment includes two of the questions in the midterm. The points will count as an Assignment and will
not affect the midterm p

CMPT 310 - Articial Intelligence Survey
Assignment 3
Due: 11:00 am on March 17, 2014
Instructor: Mehrdad Oveisi
Spring 2014
Question 1 [3 marks] Do the rst part of exercise 7.20 in the textbook (i.e., the second part on DPLL
may be omitted).
Question 2 [3

/* CMPT 310
Instructor: Diana Cukierman
Assignment: logic and prolog
Prolog component*/
% search if the the first element in the potential sublist
% is present in the (second argument) list
sublist1([],[_|_]).
sublist1([X|T1],[X|T2]):-sublist2(T1,T2).
s

CMPT 310, Fall 2015, SFU Burnaby
Instructor: Diana Cukierman
MIDTERM October 21
* YOU ARE RECOMMENDED TO USE THIS MIDTEM FOR YOU TO PRACTICE.
Not to be shared with anyone outside CMPT 310 2015-3 class.
1. [8] CHOOSE AND ANSWER ONLY ONE of the following tw

Assignment 2 Solutions
Implementation (by Dong Hoon Oh):
This code was selected because it had the fastest overall performance
time, even for large n.
#lang racket
;=
=kt-best-first-h
;This is a main function that calls recursive kt-best-first-h function

CMPT 310 Fall 2015, SFU Burnaby
Instructor: Diana Cukierman
Page 1 of 4
Group Assignment. Logic and Logic Programming
Due date: Monday October 19, 11:59 PM.
General directives, requirements
Create your own group (in the set Groups for Logic assignment) of

Logical agents
Chapter 7
Outline
Topics:
Knowledge-based agents
Example domain: The Wumpus World
Logic in general
models and entailment
Propositional (Boolean) logic
Equivalence, validity, satisability
Inference rules and theorem proving
forward c

Introduction to Scheme
Jim Delgrande
CMPT 310
Introduction to Scheme: Lisp
Lisp was developed by John McCarthy in 1960, and is based
on the lambda calculus.
Primary goal of Lisp was to cover symbol manipulation.
Focus on the notion of a function and fu

31/10/2015
Search Strategies - intro
General search
Diana Cukierman
Search II strategies
1
Direction of search (Goal/Data driven)
Generic Search Algorithm
Luger, Chapter 3
Computational Intelligence, a logical
approach, by Poole, Mackworth and Goebel,
Cha

Uncertainty
Chapter 13
Outline
Uncertainty
Probability
Syntax and Semantics
Inference
Independence and Bayes Rule
Uncertainty
Lets say you want to get to the airport in time for a ight.
Let action At = leave for airport t minutes before ight
Q: Wi

Inference in rst-order logic
Chapter 9
Outline
Reducing rst-order inference to propositional inference
Unication
Generalized Modus Ponens
Forward and backward chaining
Resolution
Reducing FO inference to propositional
inference
For some reasoning ta

How much is a gamble worth?
Heres a simple but typical choice problem.
You are offered a gamble or lottery:
You have a 50% chance of winning $1,
and a 50% chance of winning nothing.
How much should you pay to play this gamble?
Suppose Translink checks oft

Search and Sequential
Action
1
CHAPTER 3
Oliver Schulte
Simon Fraser University
CMPT 310 - Blind Search
Outline
2
Problem formulation: representing sequential
problems.
Example problems.
Planning for solving sequential problems
without uncertainty.
Basic

Sequential Games and
Adversarial Search
CHAPTER 5
CMPT 310
I n t r o d u c t i o n t o A r t i fi c i a l I n t e l l i g e n c e
Simon Fraser University
Oliver Schulte
Environment Type Discussed In this
Lecture
2
Fully
Observable
yes
Turn-taking: Semi-d

Syllabus CMPT 310
May 2017
Artificial Intelligence Survey (CMPT 310)
Simon Fraser University
Instructor: Oliver Schulte
Textbook. Artificial Intelligence: A Modern Approach (3rd Edition), Stuart Russell, Peter Norvig,
Prentice Hall, 2010.
References
Comp

Articial Intelligence: Introduction
Chapter 1
Outline
We consider here:
What is AI?
A brief history
The state of the art
What is AI?
Consider the following table that can be used to classify denitions
of AI:
Systems that
think like humans
Systems that

Intelligent Agents
Chapter 2
Outline
Agents and environments
Rationality
Task environment:
PEAS:
Performance measure
Environment
Actuators
Sensors
Environment types
Agent types
Agents and Environments
An agent is anything that can be viewed as perce

Informed Search Algorithms
Chapter 4
Outline
Informed Search and Heuristic Functions
For informed search, we use problem-specic knowledge to
guide the search.
Topics:
Best-rst search
A search
Heuristics
Recall: General Tree Search
function Tree-Search

Overview of First-Order Logic
Chapter 8
Outline
Why FOL?
Syntax of FOL
Expressing Sentences in FOL
Wumpus world in FOL
Knowledge Engineering
Pros and Cons of Propositional Logic (PC)
Pros:
PC is declarative: formulas correspond to assertions.
Pros a

Uncertainty
Chapter 13
Outline
Uncertainty
Probability
Syntax and Semantics
Inference
Independence and Bayes Rule
Uncertainty
Lets say you want to get to the airport in time for a flight.
Let action At = leave for airport t minutes before flight
Q

Bayesian networks
Chapter 14.44
Outline
Syntax
Semantics
Inference
Bayesian Networks
Bayes nets allow for the compact specification of full joint
distributions
They do this by providing a simple, graphical notation for
conditional independence assert