Search exploring the consequences of possible actions.

Slides:

Advertisements

Similar presentations

Heuristic Search techniques

Advertisements

Artificial Intelligence By Mr. Ejaz CIIT Sahiwal.

Greedy best-first search Use the heuristic function to rank the nodes Search strategy –Expand node with lowest h-value Greedily trying to find the least-cost.

Traveling Salesperson Problem

State Space 3 Chapter 4 Heuristic Search. Three Algorithms Backtrack Depth First Breadth First All work if we have well-defined: Goal state Start state.

GRAPHS Lecture 18 CS2110 – Fall Graph Algorithms 2 Search –depth-first search –breadth-first search Shortest paths –Dijkstra's algorithm Minimum.

CPSC 322, Lecture 5Slide 1 Uninformed Search Computer Science cpsc322, Lecture 5 (Textbook Chpt 3.4) January, 14, 2009.

CS 484 – Artificial Intelligence1 Announcements Department Picnic: today, after class Lab 0 due today Homework 2 due Tuesday, 9/18 Lab 1 due Thursday,

Implementation and Evaluation of Search Methods. Use a stack data structure. 1. Push root node of tree on the stack. 2. Pop the top node off the stack.

CSC 423 ARTIFICIAL INTELLIGENCE

Artificial Intelligence (CS 461D)

CS171 Introduction to Computer Science II Graphs Strike Back.

Lecture 3 Note: Some slides and/or pictures are adapted from Lecture slides / Books of Dr Zafar Alvi. Text Book - Aritificial Intelligence Illuminated.

SE Last time: Problem-Solving Problem solving: Goal formulation Problem formulation (states, operators) Search for solution Problem formulation:

Problem Solving by Searching Copyright, 1996 © Dale Carnegie & Associates, Inc. Chapter 3 Spring 2004.

1 Using Search in Problem Solving Part II. 2 Basic Concepts Basic concepts: Initial state Goal/Target state Intermediate states Path from the initial.

Toy Problem: Missionaries and Cannibals

Using Search in Problem Solving

Problem Solving and Search in AI Heuristic Search

Using Search in Problem Solving

1 Tree Searching Strategies. 2 The procedure of solving many problems may be represented by trees. Therefore the solving of these problems becomes a tree.

CS 561, Session 6 1 Last time: Problem-Solving Problem solving: Goal formulation Problem formulation (states, operators) Search for solution Problem formulation:

State-Space Searches. 2 State spaces A state space consists of –A (possibly infinite) set of states The start state represents the initial problem Each.

For Friday Finish chapter 3 Homework: –Chapter 3, exercise 6 –May be done in groups. –Clarification on part d: an “action” must be running the program.

State-Space Searches.

Artificial Intelligence Course outline Introduction Problem solving Generic algorithms Knowledge Representation and Reasoning Expert Systems Uncertainty.

Heuristic Search Heuristic - a “rule of thumb” used to help guide search often, something learned experientially and recalled when needed Heuristic Function.

Busby, Dodge, Fleming, and Negrusa. Backtracking Algorithm Is used to solve problems for which a sequence of objects is to be selected from a set such.

Problem Solving and Search Andrea Danyluk September 11, 2013.

Graphs II Robin Burke GAM 376. Admin Skip the Lua topic.

Introduction to search Chapter 3. Why study search? §Search is a basis for all AI l search proposed as the basis of intelligence l inference l all learning.

Problem Solving by Searching Search Methods : informed (Heuristic) search.

State-Space Searches. 2 State spaces A state space consists of A (possibly infinite) set of states The start state represents the initial problem Each.

George F Luger ARTIFICIAL INTELLIGENCE 5th edition Structures and Strategies for Complex Problem Solving HEURISTIC SEARCH Luger: Artificial Intelligence,

Search exploring the consequences of possible actions.

For Friday Finish reading chapter 4 Homework: –Lisp handout 4.

For Monday Read chapter 4, section 1 No homework..

Lecture 3: Uninformed Search

Search CPSC 386 Artificial Intelligence Ellen Walker Hiram College.

Lecture 3: 18/4/1435 Searching for solutions. Lecturer/ Kawther Abas 363CS – Artificial Intelligence.

For Friday Read chapter 4, sections 1 and 2 Homework –Chapter 3, exercise 7 –May be done in groups.

Basic Search Procedure 1. Start with the start node (root of the search tree) and place in on the queue 2. Remove the front node in the queue and If the.

1 Solving problems by searching Chapter 3. Depth First Search Expand deepest unexpanded node The root is examined first; then the left child of the root;

Search (continued) CPSC 386 Artificial Intelligence Ellen Walker Hiram College.

Informed Search Reading: Chapter 4.5 HW #1 out today, due Sept 26th.

Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley. Ver Chapter 13: Graphs Data Abstraction & Problem Solving with C++

© 2006 Pearson Addison-Wesley. All rights reserved 14 A-1 Chapter 14 Graphs.

Searching for Solutions

Ch. 3 – Search Supplemental slides for CSE 327 Prof. Jeff Heflin.

3.5 Informed (Heuristic) Searches This section show how an informed search strategy can find solution more efficiently than uninformed strategy. Best-first.

CS621: Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 3: Search, A*

Best-first search is a search algorithm which explores a graph by expanding the most promising node chosen according to a specified rule.

For Monday Read chapter 4 exercise 1 No homework.

© 2006 Pearson Addison-Wesley. All rights reserved14 B-1 Chapter 14 (continued) Graphs.

Chapter 3.5 Heuristic Search. Learning Objectives Heuristic search strategies –Best-first search –A* algorithm Heuristic functions.

Lecture 3 Problem Solving through search Uninformed Search

Breadth First and Depth First

Last time: Problem-Solving

Artificial Intelligence Problem solving by searching CSC 361

Maze Implementation, Analysis and Design to find Shortest Paths

SOLVING PROBLEMS BY SEARCHING

Depth-First Searches Introduction to AI.

Blay Whitby 2003 Search Blay Whitby 2003

State-Space Searches.

State-Space Searches.

Reading: Chapter 4.5 HW#2 out today, due Oct 5th

State-Space Searches.

Supplemental slides for CSE 327 Prof. Jeff Heflin

Depth-First Searches.

Presentation transcript:

search exploring the consequences of possible actions

search context good for simple problem solving leads to... game playing planning expert-systems...etc...

jargon state (node) a static problem state state space all possible problem states operator/move Fn to generate one state from another legal move generator Fn to generate all successor states from a given state search strategy approach to exploring state space

a basic search algorithm search( start, goal ) put the start state onto ready until goal is found or ready is empty do select a state S from ready remove S from ready if S is a goal then finished ! else add all unvisited successor states of S to ready add S to visited

issues what kind of result is result preferred? how can paths between states be represented? what is the strategy for state selection? how is the legal move generator specified? can it be of a standard form? what about different search strategies? efficiency / costs / etc

strategies 1 breadth-first layer by layer through search tree ready is a queue depth first exhausting one limb of tree before going to next ready is a stack best first explore from least cost (maps.google.co.uk)

strategies 2 heuristic (?) explore from closest to goal heuristic (ok) explore from least (cost + closeness to goal) admissible searches will find solutions if they exist optimal searches will find solutions with minimal effort