Instructions/notes the exam is closed books/notes/devices/neighbors, and open mind 🙂 there are 8 questions, and a ¡®non-data-related¡¯ bonus there are no ¡®trick¡¯ questions, or ones with long calculations or formulae please do NOT cheat; you get a 0 if you are found to have cheated when time is up, stop your work; you get a […]
CMPT 310 Artificial Intelligence Survey Simon Fraser University Spring 2021 Instructor: Oliver Schulte Assignment 1: Chapters 1, 2, Game Theory. (Solutions) NB: The clarity of your answers was considered when grading. Chapter 1. AI Foundations. 21 points total. 1. (5 points) Consider these two statements. • “Animals can do only what their genes tell them”.
Question 11 pts Empiricism is the idea that (sense) data is the ultimate source of all knowledge and intelligence a theory that rules out innate (e.g. genetic) knowledge the view that empirical sciences (like physics) are superior to conceptual sciences (like mathematics) a method for evaluating AI systems by empirically testing their performance Flag this
CS计算机代考程序代写 Bayesian network Bayesian algorithm AI Question 11 pts Read More »
CIS 471/571: Introduction to Artificial Intelligence, Winter 2019 MIDTERM SOLUTION • You have approximately 80 minutes. • The exam is open book. First Name Last Name UID 1 Q1. Search [32 points] (a) True or False or Multiple Choices (mark your answer) [11 points] 1. [1 pt] Iterative deepening involves re-running breadth-first search repeatedly. (True
CS计算机代考程序代写 algorithm CIS 471/571: Introduction to Artificial Intelligence, Winter 2019 Read More »
CIS 471/571(Fall 2020): Introduction to Artificial Intelligence Lecture 8: MDPs (Part 1) Thanh H. Nguyen Source: http://ai.berkeley.edu/home.html Reminder §Project 2: Multi-agent Search § Deadline: Oct 27th, 2020 §Homework 2: CSPs and Games § Deadline: Oct 24th, 2020 Thanh H. Nguyen 10/22/20 2 Non-Deterministic Search Example: Grid World § A maze-like problem § The agent lives
CIS 471/571(Fall 2020): Introduction to Artificial Intelligence Lecture 17: Hidden Markov Model Thanh H. Nguyen Source: http://ai.berkeley.edu/home.html Reminder §Homework 4: Bayes Nets §Deadline: Nov 24th, 2020 Thanh H. Nguyen 11/30/20 2 Hidden Markov Model Thanh H. Nguyen 11/30/20 3 Reasoning over Time or Space §Often, we want to reason about a sequence of observations §Speech
CIS 471/571(Fall 2020): Introduction to Artificial Intelligence Lecture 6: Adversarial Search Thanh H. Nguyen Source: http://ai.berkeley.edu/home.html Reminders § Project 2: §Deadline: Oct 27th, 2020 § Written assignment 2: §Deadline: Oct 24th, 2020 Thanh H. Nguyen 10/13/20 2 Adversarial Games Types of Games §Many different kinds of games! § Axes: §Deterministic or stochastic? §One, two, or
CS计算机代考程序代写 algorithm CIS 471/571(Fall 2020): Introduction to Artificial Intelligence Read More »
CIS 471/571(Fall 2020): Introduction to Artificial Intelligence Lecture 4: Constraint Satisfaction Problems (Part 1) Thanh H. Nguyen Source: http://ai.berkeley.edu/home.html Reminder § Homework 1: Search § Deadline: Oct 10th, 2020 § Project 1: Search § Deadline: Oct 13th, 2020 Thanh H. Nguyen 10/11/20 2 Today §Constraint Satisfaction Problems §Backtracking Search §Filtering §Ordering Constraint Satisfaction Problems Constraint
CIS 471/571(Fall 2020): Introduction to Artificial Intelligence Lecture 2: Uninformed Search Thanh H. Nguyen Most slides are by Pieter Abbeel, Dan Klein, Luke Zettlemoyer, John DeNero, Stuart Russell, Andrew Moore, or Daniel Lowd Source: http://ai.berkeley.edu/home.html Announcement §Project 1 §Deadline: Oct 13th, 2020 §Written Assignment 1 §Will be posted today §Deadline: Oct 10th, 2020 Thanh H.
CIS 471/571(Fall 2020): Introduction to Artificial Intelligence Lecture 18: HMMs, Particle Filters Thanh H. Nguyen Source: http://ai.berkeley.edu/home.html Announcement §Class on Thursday, Dec 03rd §Exam review §End-of-course Survey §Open until 06:00 PM on Fri, Dec 04th Thanh H. Nguyen 11/30/20 2 Today §HMMs §Particle filters §Applications: §Robot localization / mapping Recap: Reasoning Over Time §Markov models