learning goals 1
play

Learning Goals 1 5 A* Search with the Misplaced Tile Heuristic 5 - PDF document

Jul 06, 2023 •172 likes •245 views

CS 486/686 A* Search by Zhengmin Zhang and Alice Gao 4 By the end of the exercise, you should be able to Learning Goals 1 5 A* Search with the Misplaced Tile Heuristic 5 3 A* Search with the Manhattan Distance Heuristic 2 1 Instructions

  1. CS 486/686 A* Search by Zhengmin Zhang and Alice Gao 4 By the end of the exercise, you should be able to Learning Goals 1 5 A* Search with the Misplaced Tile Heuristic 5 3 A* Search with the Manhattan Distance Heuristic 2 1 Instructions 3 2 Problem Description 2 1 Learning Goals 1 Contents • Trace the execution of the A* search algorithm using difgerent heuristic functions.

  2. CS 486/686 A* Search by Zhengmin Zhang and Alice Gao Cost function: Each move has a cost of 1. 6 7 8 The 8-puzzle as a search problem: Action: Move the blank space up, down, left or right, wherever possible. Successor function: The resulting state after taking one action. 3 4 Instructions You will trace the execution of the A* search algorithm with two difgerent heuristic functions: The Manhattan distance heuristic and the Misplaced tiles heuristic. Generate successors attempting to move the blank space in the following order: up, right, down, left. When choosing which state to remove from the frontier, if there is a tie in the heuristic value, choose the state that comes earlier in lexicographical order (by treating each state as a 9-digit 2 5 3 7 2 Problem Description An instance of the 8-puzzle consists of a 3×3 board with 8 numbered tiles and a blank space. Each tile has a number from 1 to 8. A tile adjacent to the blank space can slide into the space. The goal is to reach a specifjed goal state, such as the one shown below. Initial State 5 3 8 6 2 2 4 1 Goal State 1 number). State: Each state is given by x 00 x 10 x 20 , x 01 x 11 x 21 , x 02 x 12 x 22 where x ij is the value in the space at column i and row j . If the space is blank, the value is zero. i, j ∈ { 0 , 1 , 2 } . x ij ∈ { 0 , . . . , 8 } . Initial state: 530 , 876 , 241 Goal state: 123 , 456 , 780

  3. CS 486/686 A* Search by Zhengmin Zhang and Alice Gao 6 the frontier. state removed: 5 3 8 7 2 g = 0, h = 16, f = 0 + 16 = 16 4 1 successors: frontier at the end of this step: Step 3: Remove one state from the frontier. If the state is not a goal, add all of its successors to the frontier. Step 2: Remove one state from the frontier. If the state is not a goal, add all of its successors to 1 3 Complete the fjrst fjve steps of the A* algorithm using the Manhattan distance heuristic. 4 A* Search with the Manhattan Distance Heuristic For each state, the h value is the sum of the distances of the tiles from their goal positions (not counting the empty space as one tile). Each tile can only move horizontally or vertically. Thus, this is also called the Manhattan distance. Note: The previous version of this notes was counting the empty square as one tile. This has been changed to be consistent with the textbook. Step 1: Add the initial state to the frontier. 4 5 3 8 7 6 2 state removed: frontier at the end of this step = [((530 , 876 , 241) , 16)]

  4. CS 486/686 A* Search by Zhengmin Zhang and Alice Gao 4 successors: frontier at the end of this step: Step 4: Remove one state from the frontier. If the state is not a goal, add all of its successors to the frontier. state removed: successors: frontier at the end of this step: Step 5: Remove one state from the frontier. If the state is not a goal, add all of its successors to the frontier. state removed: successors: frontier at the end of this step:

  5. CS 486/686 A* Search by Zhengmin Zhang and Alice Gao 6 the frontier. state removed: 5 3 8 7 2 5 4 1 successors: frontier at the end of this step: Step 3: Remove one state from the frontier. If the state is not a goal, add all of its successors to the frontier. Step 2: Remove one state from the frontier. If the state is not a goal, add all of its successors to g = 0, h = 7, f = 0 + 7 = 7 1 Complete the fjrst fjve steps of the A* algorithm using the Misplaced tile heuristic. 5 A* Search with the Misplaced Tile Heuristic For each state, the h value is the number of tiles that are NOT in their goal positions (not counting the empty square as one tile). Note: The previous version of this notes was counting the empty square as one tile. This has been changed to be consistent with the textbook. Step 1: Add the initial state to the frontier. 5 3 8 7 6 2 4 state removed: frontier at the end of this step: [((530 , 876 , 241) , 7)]

  6. CS 486/686 A* Search by Zhengmin Zhang and Alice Gao 6 successors: frontier at the end of this step: Step 4: Remove one state from the frontier. If the state is not a goal, add all of its successors to the frontier. state removed: successors: frontier at the end of this step: Step 5: Remove one state from the frontier. If the state is not a goal, add all of its successors to the frontier. state removed: successors: frontier at the end of this step:

Recommend

Using Learning Goals to Inform Instruction Erin Meikle Overview What we mean by learning goals

Using Learning Goals to Inform Instruction Erin Meikle Overview What we mean by learning goals

Using Learning Goals to Inform Instruction Erin Meikle Overview What we mean by learning goals Specifying learning goals Why learning goals are important What are learning goals? e.g., Students will be able to complete the following task by

416 views • 27 slides
TIME IS POWER - QUANTUMINSERT 1 Learning Goals The Following Learning Goals are Covered in the

TIME IS POWER - QUANTUMINSERT 1 Learning Goals The Following Learning Goals are Covered in the

create your own exercise Mario Silaci, Lucas Wolf TIME IS POWER - QUANTUMINSERT 1 Learning Goals The Following Learning Goals are Covered in the Lecture PreLab Lab What are requirements to perform the QUANTUM X X X INSERT (QI)? How does

615 views • 34 slides
SERVICE LEARNING CETL WORKSHOP 4/27/18 GOALS Consistent with UIs Strategic Plan goals of

SERVICE LEARNING CETL WORKSHOP 4/27/18 GOALS Consistent with UIs Strategic Plan goals of

SERVICE LEARNING CETL WORKSHOP 4/27/18 GOALS Consistent with UIs Strategic Plan goals of outreach that transforms learning , inspires innovation and culture, and improves lives within and across a diverse local- to-global community, this

380 views • 12 slides
Machine Learning Modeling and Learning 15-110 Monday 4/13 Learning Goals Given a

Machine Learning Modeling and Learning 15-110 Monday 4/13 Learning Goals Given a

Machine Learning Modeling and Learning 15-110 Monday 4/13 Learning Goals Given a dataset, identify features which may help predict information about the data Identify common types of machine learning algorithms 2 Machine

553 views • 27 slides
Feb 20: Bayes' Rule, Expectation and Variance How we design this course 1. Learning goals 2.

Feb 20: Bayes' Rule, Expectation and Variance How we design this course 1. Learning goals 2.

Feb 20: Bayes' Rule, Expectation and Variance How we design this course 1. Learning goals 2. Homework that tests learning goals 3. Lectures and sessions that provide tools to do homework that tests learning goals if something seems hard,

482 views • 20 slides
Learning Outcomes to Your Goals Define assessment , student learning assessment , and learning

Learning Outcomes to Your Goals Define assessment , student learning assessment , and learning

Connecting Learning Outcomes to Your Goals Define assessment , student learning assessment , and learning Describe the relationship between student learning assessment and Division of Student Life goals and values Define student

238 views • 19 slides
Introduction to Machine Learning ML-Basics: Components of Supervised Learning Learning goals

Introduction to Machine Learning ML-Basics: Components of Supervised Learning Learning goals

Introduction to Machine Learning ML-Basics: Components of Supervised Learning Learning goals Know the three components of a learner: Hypothesis space, risk, 6 = ( 1.7 , 1.3 ) T optimization 4 2 Understand that defining these R emp =

415 views • 9 slides
Finland Jurisdictional Learning Opportunity April 13-16, 2015 Jurisdictional Learning Goals

Finland Jurisdictional Learning Opportunity April 13-16, 2015 Jurisdictional Learning Goals

York Region District School Board Finland Jurisdictional Learning Opportunity April 13-16, 2015 Jurisdictional Learning Goals Continue our ongoing learning partnership with Espoo, Finland Investigate Espoos Partnerships with Community Mental

159 views • 15 slides
Reasoning Under Uncertainty Over Time Alice Gao Lecture 16 Readings: R & N 15.1 to 15.3

Reasoning Under Uncertainty Over Time Alice Gao Lecture 16 Readings: R & N 15.1 to 15.3

1/25 Reasoning Under Uncertainty Over Time Alice Gao Lecture 16 Readings: R & N 15.1 to 15.3 Based on work by K. Leyton-Brown, K. Larson, and P. van Beek 2/25 Outline Learning Goals Revisiting the Learning goals 3/25 Learning Goals

293 views • 25 slides
System Unit 5, Lesson 2 Learning Goals Today we will be learning: The EA sound

System Unit 5, Lesson 2 Learning Goals Today we will be learning: The EA sound

The Respiratory System Unit 5, Lesson 2 Learning Goals Today we will be learning: The EA sound Reviewing Transition Words Ordinal Numbers The Respir iratory ry System By: Helen Frost Lets take a BIG, DEEP, BREATHE

518 views • 18 slides
Machine Learning 15-110 Wednesday 11/18 Learning Goals Identify three major categories of

Machine Learning 15-110 Wednesday 11/18 Learning Goals Identify three major categories of

Machine Learning 15-110 Wednesday 11/18 Learning Goals Identify three major categories of reasoning used with machine learning classification, regression, and clustering and decide which is the best fit for a problem Given a

651 views • 38 slides
Using Learning Huddles to Improve Teaching and Learning Goals for Todays Webinar Learn

Using Learning Huddles to Improve Teaching and Learning Goals for Todays Webinar Learn

Using Learning Huddles to Improve Teaching and Learning Goals for Todays Webinar Learn about the theory and practice of teacher learning huddles and how they reflect an improvement science "Plan Do S tudy Act" inquiry cycle.

563 views • 37 slides
Closing the Loop: Connecting Closing the Loop: Connecting 2. Apply a three part framework to

Closing the Loop: Connecting Closing the Loop: Connecting 2. Apply a three part framework to

EOS-SEI Learning Goals Workshop January 25, 2008 Workshop Learning Goals: After this workshop you should be able to 1. Use guidelines provided to justify & construct learning goals. g p j y g g Closing the Loop: Connecting Closing the

347 views • 8 slides
9/12/17 Universal Design Ron Rogers for Learning @ronbrogers Ron_Rogers@ocali.org 101 Goals

9/12/17 Universal Design Ron Rogers for Learning @ronbrogers Ron_Rogers@ocali.org 101 Goals

9/12/17 Universal Design Ron Rogers for Learning @ronbrogers Ron_Rogers@ocali.org 101 Goals Demonstrate the Origins of UDL Show the Neuroscience of Learning Convey the Benefits of this Instructional Framework Make Learning Engaging and

305 views • 8 slides
Web Mining and Recommender Systems Supervised learning Regression Learning Goals Introduce

Web Mining and Recommender Systems Supervised learning Regression Learning Goals Introduce

Web Mining and Recommender Systems Supervised learning Regression Learning Goals Introduce the concept of Supervised Learning Understand the components (inputs and outputs) of supervised learning problems Introduce linear

1.36k views • 122 slides
E-learning storyboard Tim Slade Whats the most difficult thing about storyboarding for

E-learning storyboard Tim Slade Whats the most difficult thing about storyboarding for

Elearninguncovered.com How to write your first E-learning storyboard Tim Slade Whats the most difficult thing about storyboarding for e-learning? Interactivity SME s Visual Design Business goals Performance Goals Learners Learning

813 views • 68 slides
Goals of Study Identify Common Learning Community Structures Review Academic, Social and

Goals of Study Identify Common Learning Community Structures Review Academic, Social and

How Learning Communities Affect Academic, Social, and Personal Gains 12th Annual National Learning Communities Conference Students at the Center PRESENTERS: Shimon Sarraf Julie M. Williams November 7 9, 2007 Indianapolis, Indiana Goals of Study

350 views • 15 slides
Service Learning and United Nations Global Goals Workshop Service Learning and Dr Rob Hales 1.

Service Learning and United Nations Global Goals Workshop Service Learning and Dr Rob Hales 1.

Service Learning and United Nations Global Goals Workshop Service Learning and Dr Rob Hales 1. Robs Background 2. Grand Challenges 3. Australia Track Record 4. Griffith SDG Engagement 5. Times Higher Education Ranking 6. SDG Audit

626 views • 35 slides
Introduction to Machine Learning k -Nearest Neighbors Regression Learning goals Understand the

Introduction to Machine Learning k -Nearest Neighbors Regression Learning goals Understand the

Introduction to Machine Learning k -Nearest Neighbors Regression Learning goals Understand the basic idea of k-NN Know different distance measures for different scales of feature variables Understand that k-NN has no optimization step

569 views • 13 slides
Learning Methods: Part 1 CS 760@UW-Madison Goals for the lecture you should understand the

Learning Methods: Part 1 CS 760@UW-Madison Goals for the lecture you should understand the

Evaluating Machine Learning Methods: Part 1 CS 760@UW-Madison Goals for the lecture you should understand the following concepts bias of an estimator learning curves stratified sampling cross validation confusion

684 views • 27 slides
Artificial Intelligence 15-110 Monday 11/30 Learning Goals Recognize how AIs attempt to

Artificial Intelligence 15-110 Monday 11/30 Learning Goals Recognize how AIs attempt to

Artificial Intelligence 15-110 Monday 11/30 Learning Goals Recognize how AIs attempt to achieve goals by using a perception, reasoning, and action cycle Build game decision trees to represent the possible moves of a game Use the

628 views • 31 slides
Learning Methods: Part 2 CS 760@UW-Madison Goals for the last lecture you should understand the

Learning Methods: Part 2 CS 760@UW-Madison Goals for the last lecture you should understand the

Evaluating Machine Learning Methods: Part 2 CS 760@UW-Madison Goals for the last lecture you should understand the following concepts bias of an estimator learning curves stratified sampling cross validation

542 views • 27 slides
Learning Goals 1 Practice Questions 1 3 2 The Holmes scenario 2 1 Learning Goals 1

Learning Goals 1 Practice Questions 1 3 2 The Holmes scenario 2 1 Learning Goals 1

CS 486/686 Probabilities by Xinda Li and Alice Gao 2 Given a description of a domain or a probabilistic model for the domain, determine whether two variables are unconditionally independent. Given a description of a domain or a

316 views • 13 slides
Introduction to Machine Learning Evaluation: Simple Measures for Classification Learning goals

Introduction to Machine Learning Evaluation: Simple Measures for Classification Learning goals

Introduction to Machine Learning Evaluation: Simple Measures for Classification Learning goals Know the definitions of misclassification error rate (MCE) and accuracy (ACC) Understand the entries of a confusion matrix Understand the idea of

704 views • 10 slides

More recommend