Algorithms and Path Planning
|
|
- Beverly Fisher
- 5 years ago
- Views:
Transcription
1 Algorithms and Path Planning Topics Simple Search Depth First Search Breadth First Search Dijkstra s Search Greedy Search A* Search Classes of interest ECE400: Computer Systems Programming CS4700: Foundations of Artificial Intelligence CS470: Practicum of Artificial Intelligence CS3758/MAE480: Autonomous Mobile Robots ECE 3400: Intelligent Physical Systems
2 End Game 5 weeks left till competition day 7Ups Can you explore the entire maze? 5s avg. for 6 squares 3.4min for 8 squares Unlikely, but possible.
3 Algorithms and Search Depth First Search (DFS) Advantage? Disadvantage? (0,0) (0,) (,0) (0,) (,) Search order: N, E, S, W Find a treasure (,4) (0,3) (,) (0,4) (,3) Memory grows linearly with the depth of the graph and so on y S x
4 Algorithms and Search Depth First Search (DFS) Breadth First Search (BFS) (0,) (0,) (,) (0,0) (,) Advantage? Disadvantage? (,0) (,0) Find a treasure 3 Search order: N, E, S, W (0,3) (,) (,) (,)(,) (,) (,) (3,0) 4 and so on Memory grows exponentially with the depth of the graph y S 5 x
5 BFS: Memory and Computation What do we need? Locations Example issue from last year n = state(init) frontier.append(n) while(frontier not empty) n = pull state from frontier if n = goal, return solution for all actions in n n = a(n) frontier.append(n ) nodes explored BFS: Growth of frontier Arduino no go! R depth (distance from robot) 5
6 BFS: Memory and Computation What do we need? Locations Parents Frontier size: 4 36 etc mem = 4۰3 n- (n = depth) 0000 BFS: Growth of frontier 500 BFS: Growth of frontier R Don t revisit parents/grandparents nodes explored 4000 nodes explored depth depth 6
7 BFS: Memory and Computation What do we need? Locations Parents Visited What is the maximum size of the frontier now? nodes explored BFS: Growth of frontier What is the issue with this approach? Store branches with lowest motion cost! R depth 7
8 BFS: Memory and Computation What do we need? Locations* Parents* Visited Cost* Action* n = state(init) frontier.append(n) visited.append(n) while(frontier not empty) * n = pull state from frontier if n = goal, return solution for all actions in n n = a(n) if n not visited or cost is lower frontier.append(n ) visited.append(n ) R 8
9 BFS: Memory and Computation visited? treasure robot? N,E,S,W walls? visited? treasure N,E,S,W walls? R Teams are at 75-35% capacity (5B-33B) Frontier (x,y-locations + parent + cost): 80B Visited: 8B, or 0B! Maze: 6B Maze: 8B 9
10 Maze Exploration Depth First Search Search order: Straight Left, then straight Right, then straight U-turn, then straight If stuck, find shortest path to the next frontier in the tree What treasure does the robot find first? R R 0
11 Algorithms and Search Depth First Search (DFS) Breadth First Search (BFS) Add motion cost Dijkstra s to save computation/memory (0,) (0,0) (,0) Find a treasure 3 Search order: N, E, S, W (0,) (,) (,) (,0) 4 (0,3) (,) (,) (,)(,) (,) (,) (3,0) and so on y S 5 x
12 Algorithms and Search Depth First Search (DFS) Breadth First Search (BFS) Add motion cost Dijkstra s to save computation/memory (0,) (0,0) (,0) Find a treasure 3 Search order: N, E, S, W (0,) (,) (,) (,0) 4 (0,3) (,) and so on y S 5 x
13 Maze Exploration Depth First Search Search order: Straight Left, then straight Right, then straight U-turn, then straight If stuck, find shortest path to the next frontier in the tree What treasure does the robot find first? What treasure does the robot find second? Could we be more efficient? R R R 3
14 Maze Exploration Dijkstra to find the next frontier Search order: Straight Left, then straight Right, then straight U-turn, then straight What treasure does the robot find first? Next treasure? Extra computation (Dijkstra for every square), but maybe better Better path planning? Add cost for revisiting nodes R
15 Maze Exploration Dijkstra s Search Could you be more efficient by looking ahead? (0,) (0,0) Only reasons about the cost to get there (,0) (0,) + (,) (,0) Find a treasure 5 Search order: N, E, S, W (0,3) + + (,) (,) (3,0) and so on y S 3 4 x
16 Informed Search Greedy Search (0,3) (0,4) (0,) (0,) (,) (,) (,3) (,4) 0(,3) (0,0) 4 4 (,0) Define a heuristic to target the goal Manhatten distance abs(x S -x G )+abs(y S -y G ) y Find a treasure 3 4 S x Search order: N, E, S, W
17 Informed Search Greedy Search Cause for concern? Find a treasure Search order: N, E, S, W n = state(init) frontier.append(n) while(frontier not empty) n = pull state from frontier visited.append(n) if n = goal, return solution for all actions in n n = a(n) if n not visited priority = heuristic(goal,n ) frontier.append(priority) y 3 4 S x
18 Informed Search Greedy Search Cause for concern? Faster, but does not guarantee optimal Find a treasure Search order: N, E, S, W y S x
19 Informed Search Breadth First Search Guarantee: Finds a path Searches everything A* Dijkstra s Algorithm Considers parent cost Guarantee: Finds the shortest path but it wastes time exploring in directions that aren t promising Greedy Search Considers goal Guarantee: Finds a path only explores promising directions
20 Informed Search A* ( A-star ) n = state(init) frontier.append(n) while(frontier not empty) n = pull state from frontier if n = goal, return solution for all actions in n n = a(n) if ((n not visited or (visited and n.cost < n_old.cost)) priority = heuristic(goal,n )+cost frontier.append(priority) visited.append(n ) Find a treasure S Search order: N, E, S, W
21 Informed Search A* ( A-star ) Cost and goal heuristic (0,0) (0,) (,0) (0,) (,) (,0) (0,3) (,) (,) 4 (3,0) 3 (0,4) 3 (,3) (,4) (,) (3,) (,3) goal (3,) y Search order: N, E, S, W Find a treasure S 5 x
22 Informed Search What if the heuristic is too optimistic? Estimated cost < true cost What if the heuristic is too pessimistic? Estimated cost > true cost No longer guaranteed to be optimal What if the heuristic is just right? Pre-compute the cost between all nodes Feasible for you? admissible heuristic inadmissible heuristic
23 Summary Dijkstra minimum path Greedy A* minimum path and efficient S S S 5 3
24 4
25 Game Theory Pick a whole number between and 00. The winner is the person who picks the value which is closest to two thirds of the class average. E.g. [0, 0, 60]. Class average 30. Winner: 0. The poll will close at the end of the class (.0pm 0/9 th ) 5
26 Class website: Piazza: 6
Announcements. CS 188: Artificial Intelligence
Announcements Projects: Looking for project partners? --- Come to front after lecture. Try pair programming, not divide-and-conquer Account forms available up front during break and after lecture Assignments
More informationRoute planning / Search Movement Group behavior Decision making
Game AI Where is the AI Route planning / Search Movement Group behavior Decision making General Search Algorithm Design Keep a pair of set of states: One, the set of states to explore, called the open
More informationLearning Objectives. c D. Poole and A. Mackworth 2010 Artificial Intelligence, Lecture 3.3, Page 1
Learning Objectives At the end of the class you should be able to: devise an useful heuristic function for a problem demonstrate how best-first and A search will work on a graph predict the space and time
More informationCS 343: Artificial Intelligence
CS 343: Artificial Intelligence Informed Search Prof. Scott Niekum University of Texas at Austin [These slides based on ones created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley.
More informationToday. Informed Search. Graph Search. Heuristics Greedy Search A* Search
Informed Search [These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. All CS188 materials are available at http://ai.berkeley.edu.] Today Informed Search Heuristics
More informationArtificial Intelligence Informed Search
Artificial Intelligence Informed Search Instructors: David Suter and Qince Li Course Delivered @ Harbin Institute of Technology [Many slides adapted from those created by Dan Klein and Pieter Abbeel for
More informationDIT411/TIN175, Artificial Intelligence. Peter Ljunglöf. 19 January, 2018
DIT411/TIN175, Artificial Intelligence Chapter 3: Classical search algorithms CHAPTER 3: CLASSICAL SEARCH ALGORITHMS DIT411/TIN175, Artificial Intelligence Peter Ljunglöf 19 January, 2018 1 DEADLINE FOR
More informationGraphs vs trees up front; use grid too; discuss for BFS, DFS, IDS, UCS Cut back on A* optimality detail; a bit more on importance of heuristics,
Graphs vs trees up front; use grid too; discuss for BFS, DFS, IDS, UCS Cut back on A* optimality detail; a bit more on importance of heuristics, performance data Pattern DBs? General Tree Search function
More information521495A: Artificial Intelligence
521495A: Artificial Intelligence Informed Search Lectured by Abdenour Hadid Adjunct Professor, CMVS, University of Oulu Slides adopted from http://ai.berkeley.edu Today Informed Search Heuristics Greedy
More informationCS 5522: Artificial Intelligence II
CS 5522: Artificial Intelligence II Search Algorithms Instructor: Wei Xu Ohio State University [These slides were adapted from CS188 Intro to AI at UC Berkeley.] Today Agents that Plan Ahead Search Problems
More informationCSCI 446: Artificial Intelligence
CSCI 446: Artificial Intelligence Informed Search Instructor: Michele Van Dyne [These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. All CS188 materials are available
More informationInformed search strategies (Section ) Source: Fotolia
Informed search strategies (Section 3.5-3.6) Source: Fotolia Review: Tree search Initialize the frontier using the starting state While the frontier is not empty Choose a frontier node to expand according
More informationCS 188: Artificial Intelligence
CS 188: Artificial Intelligence Today Informed Search Informed Search Heuristics Greedy Search A* Search Instructor: Marco Alvarez University of Rhode Island (These slides were created/modified by Dan
More informationInformed Search CS457 David Kauchak Fall 2011
Admin Informed Search CS57 David Kauchak Fall 011 Some material used from : Sara Owsley Sood and others Q3 mean: 6. median: 7 Final projects proposals looked pretty good start working plan out exactly
More informationInformed Search. CS 486/686: Introduction to Artificial Intelligence Fall 2013
Informed Search CS 486/686: Introduction to Artificial Intelligence Fall 2013 1 Outline Using knowledge Heuristics Bestfirst search Greedy bestfirst search A* search Variations of A* Back to heuristics
More informationInformed Search A* Algorithm
Informed Search A* Algorithm CE417: Introduction to Artificial Intelligence Sharif University of Technology Spring 2018 Soleymani Artificial Intelligence: A Modern Approach, Chapter 3 Most slides have
More informationInformed search methods
CS 2710 Foundations of AI Lecture 5 Informed search methods Milos Hauskrecht milos@pitt.edu 5329 Sennott Square Announcements Homework assignment 2 is out Due on Tuesday, September 19, 2017 before the
More informationChapter 3: Informed Search and Exploration. Dr. Daisy Tang
Chapter 3: Informed Search and Exploration Dr. Daisy Tang Informed Search Definition: Use problem-specific knowledge beyond the definition of the problem itself Can find solutions more efficiently Best-first
More informationProblem Solving and Search
Artificial Intelligence Problem Solving and Search Dae-Won Kim School of Computer Science & Engineering Chung-Ang University Outline Problem-solving agents Problem types Problem formulation Example problems
More informationSearch with Costs and Heuristic Search
Search with Costs and Heuristic Search Alan Mackworth UBC CS 322 Search 3 January 14, 2013 Textbook 3.5.3, 3.6, 3.6.1 1 Today s Lecture Recap from last lecture, combined with AIspace demo Search with costs:
More informationCS 343H: Artificial Intelligence
CS 343H: Artificial Intelligence Lecture 4: Informed Search 1/23/2014 Slides courtesy of Dan Klein at UC-Berkeley Unless otherwise noted Today Informed search Heuristics Greedy search A* search Graph search
More informationOverview. Path Cost Function. Real Life Problems. COMP219: Artificial Intelligence. Lecture 10: Heuristic Search
COMP219: Artificial Intelligence Lecture 10: Heuristic Search Overview Last time Depth-limited, iterative deepening and bi-directional search Avoiding repeated states Today Show how applying knowledge
More informationInformed/Heuristic Search
Informed/Heuristic Search Outline Limitations of uninformed search methods Informed (or heuristic) search uses problem-specific heuristics to improve efficiency Best-first A* Techniques for generating
More informationCS 188: Artificial Intelligence Fall Search Gone Wrong?
CS 188: Artificial Intelligence Fall 2009 Lecture 3: A* Search 9/3/2009 Pieter Aeel UC Berkeley Many slides from Dan Klein Search Gone Wrong? 1 Announcements Assignments: Project 0 (Python tutorial): due
More informationA.I.: Informed Search Algorithms. Chapter III: Part Deux
A.I.: Informed Search Algorithms Chapter III: Part Deux Best-first search Greedy best-first search A * search Heuristics Outline Overview Informed Search: uses problem-specific knowledge. General approach:
More informationCSE 473. Chapter 4 Informed Search. CSE AI Faculty. Last Time. Blind Search BFS UC-BFS DFS DLS Iterative Deepening Bidirectional Search
CSE 473 Chapter 4 Informed Search CSE AI Faculty Blind Search BFS UC-BFS DFS DLS Iterative Deepening Bidirectional Search Last Time 2 1 Repeated States Failure to detect repeated states can turn a linear
More informationCOMP219: Artificial Intelligence. Lecture 10: Heuristic Search
COMP219: Artificial Intelligence Lecture 10: Heuristic Search 1 Class Tests Class Test 1 (Prolog): Tuesday 8 th November (Week 7) 13:00-14:00 LIFS-LT2 and LIFS-LT3 Class Test 2 (Everything but Prolog)
More informationInformed Search. CMU Snake Robot. Administrative. Uninformed search strategies. Assignment 1 was due before class how d it go?
Informed Search S151 David Kauchak Fall 2010 MU Snake Robot http://www-cgi.cs.cmu.edu/afs/cs.cmu.edu/web/people/biorobotics/projects/ modsnake/index.html Some material borrowed from : Sara Owsley Sood
More informationHW#1 due today. HW#2 due Monday, 9/09/13, in class Continue reading Chapter 3
9-04-2013 Uninformed (blind) search algorithms Breadth-First Search (BFS) Uniform-Cost Search Depth-First Search (DFS) Depth-Limited Search Iterative Deepening Best-First Search HW#1 due today HW#2 due
More informationCOMP219: Artificial Intelligence. Lecture 10: Heuristic Search
COMP219: Artificial Intelligence Lecture 10: Heuristic Search 1 Class Tests Class Test 1 (Prolog): Friday 17th November (Week 8), 15:00-17:00. Class Test 2 (Everything but Prolog) Friday 15th December
More informationArtificial Intelligence
Artificial Intelligence Informed Search and Exploration Chapter 4 (4.1 4.2) A General Search algorithm: Chapter 3: Search Strategies Task : Find a sequence of actions leading from the initial state to
More informationHeuristic Search and Advanced Methods
Heuristic Search and Advanced Methods Computer Science cpsc322, Lecture 3 (Textbook Chpt 3.6 3.7) May, 15, 2012 CPSC 322, Lecture 3 Slide 1 Course Announcements Posted on WebCT Assignment1 (due on Thurs!)
More informationCS 771 Artificial Intelligence. Informed Search
CS 771 Artificial Intelligence Informed Search Outline Review limitations of uninformed search methods Informed (or heuristic) search Uses problem-specific heuristics to improve efficiency Best-first,
More informationInformed Search and Exploration for Agents
Informed Search and Exploration for Agents R&N: 3.5, 3.6 Michael Rovatsos University of Edinburgh 29 th January 2015 Outline Best-first search Greedy best-first search A * search Heuristics Admissibility
More informationCS 106X, Lecture 23 Dijkstra and A* Search
CS 106X, Lecture 23 Dijkstra and A* Search reading: Programming Abstractions in C++, Chapter 18 This document is copyright (C) Stanford Computer Science and Nick Troccoli, licensed under Creative Commons
More informationSearching. Assume goal- or utilitybased. Next task to achieve is to determine the best path to the goal
Searching Assume goal- or utilitybased agents: state information ability to perform actions goals to achieve Next task to achieve is to determine the best path to the goal CSC384 Lecture Slides Steve Engels,
More informationCS 520: Introduction to Artificial Intelligence. Lectures on Search
CS 520: Introduction to Artificial Intelligence Prof. Louis Steinberg Lecture : uninformed search uninformed search Review Lectures on Search Formulation of search problems. State Spaces Uninformed (blind)
More informationNotes. Video Game AI: Lecture 5 Planning for Pathfinding. Lecture Overview. Knowledge vs Search. Jonathan Schaeffer this Friday
Notes Video Game AI: Lecture 5 Planning for Pathfinding Nathan Sturtevant COMP 3705 Jonathan Schaeffer this Friday Planning vs localization We cover planning today Localization is just mapping a real-valued
More informationCPS 170: Artificial Intelligence Search
CPS 170: Artificial Intelligence http://www.cs.duke.edu/courses/spring09/cps170/ Search Instructor: Vincent Conitzer Search We have some actions that can change the state of the world Change resulting
More informationProblem Solving & Heuristic Search
190.08 Artificial 2016-Spring Problem Solving & Heuristic Search Byoung-Tak Zhang School of Computer Science and Engineering Seoul National University 190.08 Artificial (2016-Spring) http://www.cs.duke.edu/courses/fall08/cps270/
More informationPAC-MAN is one of the most popular game
SCHOOL OF DATA SCIENCE 1 Assignment1. Search in Pacman Project Report Shihan Ran - 15307130424 Abstract This project is aimed at designing a intelligent Pacman agent that is able to find optimal paths
More informationDiscrete Motion Planning
RBE MOTION PLANNING Discrete Motion Planning Jane Li Assistant Professor Mechanical Engineering & Robotics Engineering http://users.wpi.edu/~zli11 Announcement Homework 1 is out Due Date - Feb 1 Updated
More informationOutline. Best-first search
Outline Best-first search Greedy best-first search A* search Heuristics Admissible Heuristics Graph Search Consistent Heuristics Local search algorithms Hill-climbing search Beam search Simulated annealing
More informationCMU-Q Lecture 2: Search problems Uninformed search. Teacher: Gianni A. Di Caro
CMU-Q 15-381 Lecture 2: Search problems Uninformed search Teacher: Gianni A. Di Caro RECAP: ACT RATIONALLY Think like people Think rationally Agent Sensors? Actuators Percepts Actions Environment Act like
More informationCS 188: Artificial Intelligence. Recap Search I
CS 188: Artificial Intelligence Review of Search, CSPs, Games DISCLAIMER: It is insufficient to simply study these slides, they are merely meant as a quick refresher of the high-level ideas covered. You
More informationCSE 40171: Artificial Intelligence. Informed Search: A* Search
CSE 40171: Artificial Intelligence Informed Search: A* Search 1 Homework #1 has been released. It is due at 11:59PM on 9/10. 2 Quick Recap: Search Quick Recap: Search Search problem: States (configurations
More informationMotion Planning, Part III Graph Search, Part I. Howie Choset
Motion Planning, Part III Graph Search, Part I Howie Choset Happy President s Day The Configuration Space What it is A set of reachable areas constructed from knowledge of both the robot and the world
More informationOutline for today s lecture. Informed Search I. One issue: How to search backwards? Very briefly: Bidirectional search. Outline for today s lecture
Outline for today s lecture Informed Search I Uninformed Search Briefly: Bidirectional Search (AIMA 3.4.6) Uniform Cost Search (UCS) Informed Search Introduction to Informed search Heuristics 1 st attempt:
More informationCE 473: Artificial Intelligence. Autumn 2011
CE 473: Artificial Intelligence Autumn 2011 A* Search Luke Zettlemoyer Based on slides from Dan Klein Multiple slides from Stuart Russell or Andrew Moore Today A* Search Heuristic Design Graph search Recap:
More informationPathfinding. Advaith Siddharthan
Pathfinding Advaith Siddharthan Context What is Intelligence? Rational? Search Optimisation Reasoning Impulsive? Quicker response Less predictable Personality/Emotions: Angry/Bored/Curious Overview The
More informationINTRODUCTION TO HEURISTIC SEARCH
INTRODUCTION TO HEURISTIC SEARCH What is heuristic search? Given a problem in which we must make a series of decisions, determine the sequence of decisions which provably optimizes some criterion. What
More informationSearch. CS 3793/5233 Artificial Intelligence Search 1
CS 3793/5233 Artificial Intelligence 1 Basics Basics State-Space Problem Directed Graphs Generic Algorithm Examples Uninformed is finding a sequence of actions that achieve a goal from an initial state.
More informationInformed search algorithms. Chapter 3 (Based on Slides by Stuart Russell, Dan Klein, Richard Korf, Subbarao Kambhampati, and UW-AI faculty)
Informed search algorithms Chapter 3 (Based on Slides by Stuart Russell, Dan Klein, Richard Korf, Subbarao Kambhampati, and UW-AI faculty) Intuition, like the rays of the sun, acts only in an inflexibly
More informationArtificial Intelligence
Artificial Intelligence Search Marc Toussaint University of Stuttgart Winter 2015/16 (slides based on Stuart Russell s AI course) Outline Problem formulation & examples Basic search algorithms 2/100 Example:
More informationLecture 4: Search 3. Victor R. Lesser. CMPSCI 683 Fall 2010
Lecture 4: Search 3 Victor R. Lesser CMPSCI 683 Fall 2010 First Homework 1 st Programming Assignment 2 separate parts (homeworks) First part due on (9/27) at 5pm Second part due on 10/13 at 5pm Send homework
More informationArtificial Intelligence
Artificial Intelligence Jeff Clune Assistant Professor Evolving Artificial Intelligence Laboratory AI Challenge One Transform to graph Explore the graph, looking for? dust unexplored squares Some good
More informationArtificial Intelligence (Heuristic Search)
Artificial Intelligence (Heuristic Search) KR Chowdhary, Professor & Head Email: kr.chowdhary@acm.org Department of Computer Science and Engineering MBM Engineering College, Jodhpur kr chowdhary heuristic
More informationSpanning trees. Suppose you have a connected undirected graph
Spanning Trees Spanning trees Suppose you have a connected undirected graph Connected: every node is reachable from every other node Undirected: edges do not have an associated direction...then a spanning
More informationCPSC 436D Video Game Programming
CPSC 436D Video Game Programming Strategy & Adversarial Strategy Strategy Given current state, determine BEST next move Short term: best among immediate options Long term: what brings something closest
More informationBasic Motion Planning Algorithms
Basic Motion Planning Algorithms Sohaib Younis Intelligent Robotics 7 January, 2013 1 Outline Introduction Environment Map Dijkstra's algorithm A* algorithm Summary 2 Introduction Definition: Motion Planning
More informationInformed search. Soleymani. CE417: Introduction to Artificial Intelligence Sharif University of Technology Spring 2016
Informed search CE417: Introduction to Artificial Intelligence Sharif University of Technology Spring 2016 Soleymani Artificial Intelligence: A Modern Approach, Chapter 3 Outline Best-first search Greedy
More informationDFS. Depth-limited Search
DFS Completeness? No, fails in infinite depth spaces or spaces with loops Yes, assuming state space finite. Time complexity? O(b m ), terrible if m is much bigger than d. can do well if lots of goals Space
More informationUninformed Search Methods
Uninformed Search Methods Search Algorithms Uninformed Blind search Breadth-first uniform first depth-first Iterative deepening depth-first Bidirectional Branch and Bound Informed Heuristic search Greedy
More informationAnnouncements. Project 0: Python Tutorial Due last night
Announcements Project 0: Python Tutorial Due last night HW1 officially released today, but a few people have already started on it Due Monday 2/6 at 11:59 pm P1: Search not officially out, but some have
More informationSearch: Advanced Topics and Conclusion
Search: Advanced Topics and Conclusion CPSC 322 Lecture 8 January 24, 2007 Textbook 2.6 Search: Advanced Topics and Conclusion CPSC 322 Lecture 8, Slide 1 Lecture Overview 1 Recap 2 Branch & Bound 3 A
More informationInteractive Comparison of Path Finding Algorithms. December 18, 2014
CS 4701 - FOUNDATIONS OF ARTIFICIAL INTELLIGENCE PRACTICUM Interactive Comparison of Path Finding Algorithms Jasmine Chan (jjc297), Jonya Chen (jc957), Ava Tan (ajt222) December 18, 2014 1 INTRODUCTION
More informationAI: Week 2. Tom Henderson. Fall 2014 CS 5300
AI: Week 2 Tom Henderson Fall 2014 What s a Problem? Initial state Actions Transition model Goal Test Path Cost Does this apply to: Problem: Get A in CS5300 Solution: action sequence from initial to goal
More informationCS510 \ Lecture Ariel Stolerman
CS510 \ Lecture02 2012-10-03 1 Ariel Stolerman Midterm Evan will email about that after the lecture, at least 2 lectures from now. The exam will be given in a regular PDF (not an online form). We will
More informationSolving problems by searching
Solving problems by searching Chapter 3 Some slide credits to Hwee Tou Ng (Singapore) Outline Problem-solving agents Problem types Problem formulation Example problems Basic search algorithms Heuristics
More informationCS 4700: Foundations of Artificial Intelligence. Bart Selman. Search Techniques R&N: Chapter 3
CS 4700: Foundations of Artificial Intelligence Bart Selman Search Techniques R&N: Chapter 3 Outline Search: tree search and graph search Uninformed search: very briefly (covered before in other prerequisite
More information6.141: Robotics systems and science Lecture 10: Motion Planning III
6.141: Robotics systems and science Lecture 10: Motion Planning III Lecture Notes Prepared by N. Roy and D. Rus EECS/MIT Spring 2012 Reading: Chapter 3, and Craig: Robotics http://courses.csail.mit.edu/6.141/!
More informationA* optimality proof, cycle checking
A* optimality proof, cycle checking CPSC 322 Search 5 Textbook 3.6 and 3.7.1 January 21, 2011 Taught by Mike Chiang Lecture Overview Recap Admissibility of A* Cycle checking and multiple path pruning Slide
More informationBranch & Bound (B&B) and Constraint Satisfaction Problems (CSPs)
Branch & Bound (B&B) and Constraint Satisfaction Problems (CSPs) Alan Mackworth UBC CS 322 CSP 1 January 25, 2013 P&M textbook 3.7.4 & 4.0-4.2 Lecture Overview Recap Branch & Bound Wrap up of search module
More information4 Search Problem formulation (23 points)
4 Search Problem formulation (23 points) Consider a Mars rover that has to drive around the surface, collect rock samples, and return to the lander. We want to construct a plan for its exploration. It
More informationLecture 4: Informed/Heuristic Search
Lecture 4: Informed/Heuristic Search Outline Limitations of uninformed search methods Informed (or heuristic) search uses problem-specific heuristics to improve efficiency Best-first A* RBFS SMA* Techniques
More informationArtificial Intelligence
Artificial Intelligence Lesson 1 1 About Lecturer: Prof. Sarit Kraus TA: Galit Haim: haimga@cs.biu.ac.il (almost) All you need can be found on the course website: http://u.cs.biu.ac.il/~haimga/teaching/ai/
More informationCS 4700: Foundations of Artificial Intelligence
CS 4700: Foundations of Artificial Intelligence Bart Selman selman@cs.cornell.edu Module: Informed Search Readings R&N - Chapter 3: 3.5 and 3.6 Search Search strategies determined by choice of node (in
More informationRecap A Search Optimality of A. Search: A. CPSC 322 Search 5. Textbook 3.6. Search: A CPSC 322 Search 5, Slide 1
Search: A CPSC 322 Search 5 Textbook 3.6 Search: A CPSC 322 Search 5, Slide 1 Lecture Overview 1 Recap 2 A Search 3 Optimality of A Search: A CPSC 322 Search 5, Slide 2 Search with Costs Sometimes there
More informationSearch: Advanced Topics and Conclusion
Search: Advanced Topics and Conclusion CPSC 322 Lecture 8 January 20, 2006 Textbook 2.6 Search: Advanced Topics and Conclusion CPSC 322 Lecture 8, Slide 1 Lecture Overview Recap Branch & Bound A Tricks
More informationCS 4700: Foundations of Artificial Intelligence
CS 4700: Foundations of Artificial Intelligence Bart Selman selman@cs.cornell.edu Informed Search Readings R&N - Chapter 3: 3.5 and 3.6 Search Search strategies determined by choice of node (in queue)
More informationSearch in discrete and continuous spaces
UNSW COMP3431: Robot Architectures S2 2006 1 Overview Assignment #1 Answer Sheet Search in discrete and continuous spaces Due: Start of Lab, Week 6 (1pm, 30 August 2006) The goal of this assignment is
More informationAssignment 1 is out! Due: 9 Sep 23:59! Can work in a group of 2-3 students.! NO cheating!!!! Submit in turnitin! Code + report!
Assignment 1 is out! Due: 9 Sep 23:59! Submit in turnitin! Code + report! Can work in a group of 2-3 students.! Please register your group in the website linked from the assignment description before tomorrow
More informationCAP 4630 Artificial Intelligence
CAP 4630 Artificial Intelligence Instructor: Sam Ganzfried sganzfri@cis.fiu.edu 1 http://www.ultimateaiclass.com/ https://moodle.cis.fiu.edu/ 2 Solving problems by search 3 8-puzzle 4 8-queens 5 Search
More informationMEM380 Applied Autonomous Robots Fall Depth First Search A* and Dijkstra s Algorithm
MEM380 Applied Autonomous Robots Fall Breadth First Search Depth First Search A* and Dijkstra s Algorithm Admin Stuff Course Website: http://robotics.mem.drexel.edu/mhsieh/courses/mem380i/ Instructor:
More informationDownloaded from ioenotes.edu.np
Chapter- 3: Searching - Searching the process finding the required states or nodes. - Searching is to be performed through the state space. - Search process is carried out by constructing a search tree.
More informationCOMP219: Artificial Intelligence. Lecture 7: Search Strategies
COMP219: Artificial Intelligence Lecture 7: Search Strategies 1 Overview Last time basic ideas about problem solving; state space; solutions as paths; the notion of solution cost; the importance of using
More informationThe big picture: from Perception to Planning to Control
The big picture: from Perception to Planning to Control Perception Location, Map Signals: video, inertial, range Sensors Real World 1 Planning vs Control 0. In control we go from to A to B in free space
More informationOutline for today s lecture. Informed Search. Informed Search II. Review: Properties of greedy best-first search. Review: Greedy best-first search:
Outline for today s lecture Informed Search II Informed Search Optimal informed search: A* (AIMA 3.5.2) Creating good heuristic functions Hill Climbing 2 Review: Greedy best-first search: f(n): estimated
More informationSpanning Trees, greedy algorithms. Lecture 22 CS2110 Fall 2017
1 Spanning Trees, greedy algorithms Lecture 22 CS2110 Fall 2017 1 We demo A8 Your space ship is on earth, and you hear a distress signal from a distance Planet X. Your job: 1. Rescue stage: Fly your ship
More information10/31/18. About A6, Prelim 2. Spanning Trees, greedy algorithms. Facts about trees. Undirected trees
//8 About A, Prelim Spanning Trees, greedy algorithms Lecture CS Fall 8 Prelim : Thursday, November. Visit exams page of course website and read carefully to find out when you take it (: or 7:) and what
More informationSpanning Trees, greedy algorithms. Lecture 20 CS2110 Fall 2018
1 Spanning Trees, greedy algorithms Lecture 20 CS2110 Fall 2018 1 About A6, Prelim 2 Prelim 2: Thursday, 15 November. Visit exams page of course website and read carefully to find out when you take it
More informationCIS 192: Artificial Intelligence. Search and Constraint Satisfaction Alex Frias Nov. 30 th
CIS 192: Artificial Intelligence Search and Constraint Satisfaction Alex Frias Nov. 30 th What is AI? Designing computer programs to complete tasks that are thought to require intelligence 4 categories
More informationDiscrete search algorithms
Robot Autonomy (16-662, S13) Lecture#08 (Monday February 11) Discrete search algorithms Lecturer: Siddhartha Srinivasa Scribes: Kavya Suresh & David Matten I. INTRODUCTION These notes contain a detailed
More informationCS 4700: Artificial Intelligence
CS 4700: Foundations of Artificial Intelligence Fall 2017 Instructor: Prof. Haym Hirsh Lecture 7 Extra Credit Opportunity: Lecture Today 4:15pm Gates G01 Learning to See Without a Teacher Phillip Isola
More informationCS Algorithms and Complexity
CS 350 - Algorithms and Complexity Graph Theory, Midterm Review Sean Anderson 2/6/18 Portland State University Table of contents 1. Graph Theory 2. Graph Problems 3. Uninformed Exhaustive Search 4. Informed
More informationCS 380/480 Foundations of Artificial Intelligence Winter 2007 Assignment 2 Solutions to Selected Problems
CS 380/480 Foundations of Artificial Intelligence Winter 2007 Assignment 2 Solutions to Selected Problems 1. Search trees for the state-space graph given below: We only show the search trees corresponding
More informationHeuristic Search: A* CPSC 322 Search 4 January 19, Textbook 3.6 Taught by: Vasanth
Heuristic Search: A* CPSC 322 Search 4 January 19, 2011 Textbook 3.6 Taught by: Vasanth 1 Lecture Overview Recap Search heuristics: admissibility and examples Recap of BestFS Heuristic search: A* 2 Example
More informationHeuristic (Informed) Search
Heuristic (Informed) Search (Where we try to choose smartly) R&N: Chap. 4, Sect. 4.1 3 1 Recall that the ordering of FRINGE defines the search strategy Search Algorithm #2 SEARCH#2 1. INSERT(initial-node,FRINGE)
More informationClass Overview. Introduction to Artificial Intelligence COMP 3501 / COMP Lecture 2: Search. Problem Solving Agents
Class Overview COMP 3501 / COMP 4704-4 Lecture 2: Search Prof. 1 2 Problem Solving Agents Problem Solving Agents: Assumptions Requires a goal Assume world is: Requires actions Observable What actions?
More informationSpanning Trees 4/19/17. Prelim 2, assignments. Undirected trees
/9/7 Prelim, assignments Prelim is Tuesday. See the course webpage for details. Scope: up to but not including today s lecture. See the review guide for details. Deadline for submitting conflicts has passed.
More information