Supplemental slides for CSE 327 Prof. Jeff Heflin

Slides:



Advertisements
Similar presentations
Artificial Intelligent
Advertisements

Search Search plays a key role in many parts of AI. These algorithms provide the conceptual backbone of almost every approach to the systematic exploration.
Solving problems by searching Chapter 3. Outline Problem-solving agents Problem types Problem formulation Example problems Basic search algorithms.
Additional Topics ARTIFICIAL INTELLIGENCE
Artificial Intelligence By Mr. Ejaz CIIT Sahiwal.
Problem Solving by Searching Copyright, 1996 © Dale Carnegie & Associates, Inc. Chapter 3 Spring 2007.
January 26, 2003AI: Chapter 3: Solving Problems by Searching 1 Artificial Intelligence Chapter 3: Solving Problems by Searching Michael Scherger Department.
Artificial Intelligence Problem Solving Eriq Muhammad Adams
Blind Search1 Solving problems by searching Chapter 3.
May 12, 2013Problem Solving - Search Symbolic AI: Problem Solving E. Trentin, DIISM.
Artificial Intelligence for Games Uninformed search Patrick Olivier
UNINFORMED SEARCH Problem - solving agents Example : Romania  On holiday in Romania ; currently in Arad.  Flight leaves tomorrow from Bucharest.
14 Jan 2004CS Blind Search1 Solving problems by searching Chapter 3.
Problem Solving by Searching Copyright, 1996 © Dale Carnegie & Associates, Inc. Chapter 3 Spring 2004.
Toy Problem: Missionaries and Cannibals
Artificial Intelligence Methods Rao Vemuri Searching - 2.
Informed Search Idea: be smart about what paths to try.
Review: Search problem formulation Initial state Actions Transition model Goal state (or goal test) Path cost What is the optimal solution? What is the.
Solving Problems by Searching CPS Outline Problem-solving agents Example problems Basic search algorithms.
Problem Solving and Search Andrea Danyluk September 11, 2013.
Ch. 3 – Search Supplemental slides for CSE 327 Prof. Jeff Heflin.
AI in game (II) 권태경 Fall, outline Problem-solving agent Search.
Search CPSC 386 Artificial Intelligence Ellen Walker Hiram College.
G5BAIM Artificial Intelligence Methods Graham Kendall Searching.
Problem solving by search Department of Computer Science & Engineering Indian Institute of Technology Kharagpur.
Supplemental slides for CSE 327 Prof. Jeff Heflin
Goal-based Problem Solving Goal formation Based upon the current situation and performance measures. Result is moving into a desirable state (goal state).
Solving problems by searching 1. Outline Problem formulation Example problems Basic search algorithms 2.
Ch. 3 – Search Supplemental slides for CSE 327 Prof. Jeff Heflin.
Introduction to Artificial Intelligence CS 438 Spring 2008 Today –AIMA, Ch. 3 –Problem Solving Agents State space search –Programming Assignment Thursday.
Ch. 3 – Search Supplemental slides for CSE 327 Prof. Jeff Heflin.
Solving problems by searching A I C h a p t e r 3.
Ch. 4 – Informed Search Supplemental slides for CSE 327 Prof. Jeff Heflin.
Blind Search Russell and Norvig: Chapter 3, Sections 3.4 – 3.6 CS121 – Winter 2003.
Chapter 3 Solving problems by searching. Search We will consider the problem of designing goal-based agents in observable, deterministic, discrete, known.
Lecture 3 Problem Solving through search Uninformed Search
Lecture 3: Uninformed Search
ARTIFICIAL INTELLIGENCE
EE562 ARTIFICIAL INTELLIGENCE FOR ENGINEERS
Last time: Problem-Solving
Ch. 2 – Intelligent Agents
Introduction to Artificial Intelligence
Supplemental slides for CSE 327 Prof. Jeff Heflin
Uninformed Search Chapter 3.4.
Problem Solving by Searching
Uninformed Search Strategies
Problem Solving as Search
Problem solving and search
Artificial Intelligence (CS 370D)
AI I: problem-solving and search
Search Tuomas Sandholm Read Russell & Norvig Sections
Artificial Intelligence
Russell and Norvig: Chapter 3, Sections 3.4 – 3.6
Solving problems by searching
Problem Solving and Searching
What to do when you don’t know anything know nothing
Artificial Intelligence
Searching for Solutions
Breadth-First Searches
Artificial Intelligence
Problem Solving and Searching
Depth-First Searches Introduction to AI.
G5BAIM Artificial Intelligence Methods
Informed Search Idea: be smart about what paths to try.
ARTIFICIAL INTELLIGENCE
Artificial Intelligence: Theory and Practice Ch. 3. Search
Informed Search Idea: be smart about what paths to try.
Supplemental slides for CSE 327 Prof. Jeff Heflin
Depth-First Searches.
Presentation transcript:

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

Problem Solving Agent Algorithm function SIMPLE-PROBLEM-SOLVING-AGENT(percept) returns an action static: seq, an action sequence, initially empty state, some description of the current world state goal, a goal initially null problem, a problem formulation state  UPDATE-STATE(state,percept) if seq is empty then do goal  FORMULATE-GOAL(state) problem FORMULATE-PROBLEM(state,goal) seq  SEARCH(problem) action  FIRST(seq) seq  REST(seq) return action From Figure 3.1, p. 61

8-puzzle Successor Function 7 2 4 5 6 8 3 1 blank-right blank-left blank-up blank-down 7 2 4 5 6 8 3 1 7 2 4 5 6 8 3 1 7 4 5 2 6 8 3 1 7 2 4 5 3 6 8 1

8-puzzle Search Tree initial state 7 2 4 5 8 6 3 1 7 2 4 8 6 5 3 1 7 2

Tree Search Algorithm function TREE-SEARCH(problem,fringe) returns a solution, or failure fringe  INSERT(MAKE-NODE(INITIAL-STATE[problem],fringe) loop do if EMPTY?(fringe) then return failure node  REMOVE-FIRST(fringe) if GOAL-TEST[problem] applied to STATE[node] succeeds then return SOLUTION(node) fringe  INSERT-ALL(EXPAND(node,problem),fringe) Notes: 1. fringe argument should be an empty queue. The type of the queue (e.g., LIFO, FIFO, etc.) will affect the order of the search 2. INITIAL-STATE[problem] is just syntax for accessing an object’s data (think problem.initialState in C++/Java) From Figure 3.9, p. 72

Depth-first Search A not generated on fringe B C in memory deleted D E 1 A not generated on fringe 2 7 B C in memory deleted 3 6 8 9 D E F G blue = in memory, green = on frontier, red = out of memory, clear = not generated 4 5 H I

Breadth-first Search A not generated on fringe B C in memory deleted D 1 A not generated on fringe 2 3 B C in memory deleted 4 5 6 7 D E F G 8 9 H I

Uninformed Search Summary depth-first breadth-first uniform cost queuing add to front (LIFO) add to back (FIFO) by path cost complete? no yes yes, if all step costs are greater than 0 optimal? yes, if identical step costs time expensive space modest

Water Jug Problem state: <J12, J8, J3> initial state: <0, 0, 0> goal test: <1, x, y> x and y can be any value path cost: 1 per solution step? 1 per gallon of water moved? actions/successor function let C12=12, C8=8, C3=3 fill-jug-i if Ji < Ci then Ji=Ci empty-jug-i-into-jug-j if Ji <= Cj – Jj then Jj’ = Jj + Ji, Ji’=0 fill-jug-i-from-jug-j if Jj >= Ci – Ji then Jj’ = Jj – (Ci – Ji), Ji’=Ci