Uninformed Search strategies. AIMA sections 3.4,3.5
|
|
- Judith Farmer
- 5 years ago
- Views:
Transcription
1 AIMA sections 3.4,3.5
2 search use only the information available in the problem denition Breadth-rst search Uniform-cost search Depth-rst search Depth-limited search Iterative deepening search
3 Breadth-rst search Expand shallowest unexpanded node Implementation: fringe is a FIFO queue, i.e., new successors go at the end
4 Breadth-rst search Expand shallowest unexpanded node Implementation: fringe is a FIFO queue, i.e., new successors go at the end
5 Breadth-rst search Expand shallowest unexpanded node Implementation: fringe is a FIFO queue, i.e., new successors go at the end
6 Breadth-rst search Expand shallowest unexpanded node Implementation: fringe is a FIFO queue, i.e., new successors go at the end
7 Properties of breadth-rst search Complete??
8 Properties of breadth-rst search Complete?? Yes (if b is nite) Time??
9 Properties of breadth-rst search Complete?? Yes (if b is nite) Time?? 1 + b + b 2 + b b d + b(b d 1) = O(b d+1 ), i.e., exp. in d Space??
10 Properties of breadth-rst search Complete?? Yes (if b is nite) Time?? 1 + b + b 2 + b b d + b(b d 1) = O(b d+1 ), i.e., exp. in d Space?? O(b d+1 ) (keeps every node in memory) Optimal??
11 Properties of breadth-rst search Complete?? Yes (if b is nite) Time?? 1 + b + b 2 + b b d + b(b d 1) = O(b d+1 ), i.e., exp. in d Space?? O(b d+1 ) (keeps every node in memory) Optimal?? Yes (if cost = 1 per step); not optimal in general
12 Properties of breadth-rst search Complete?? Yes (if b is nite) Time?? 1 + b + b 2 + b b d + b(b d 1) = O(b d+1 ), i.e., exp. in d Space?? O(b d+1 ) (keeps every node in memory) Optimal?? Yes (if cost = 1 per step); not optimal in general Space is the big problem; can easily generate nodes at 100MB/sec so 24hrs = 8640GB.
13 Uniform cost search Expand least-cost unexpanded node (i.e., minimum step cost) Implementation: fringe = queue ordered by path cost, lowest rst Equivalent to breadth-rst if step costs all equal Complete?? Yes, if step cost ɛ Time?? # of nodes with g cost of optimal solution, O(b C /ɛ ) where C is the cost of the optimal solution Space?? # of nodes with g cost of optimal solution, O(b C /ɛ ) Optimal?? Yesnodes expanded in increasing order of g(n)
14 Depth-rst search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front
15 Depth-rst search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front
16 Depth-rst search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front
17 Depth-rst search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front
18 Depth-rst search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front
19 Depth-rst search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front
20 Depth-rst search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front
21 Depth-rst search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front
22 Depth-rst search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front
23 Depth-rst search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front
24 Depth-rst search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front
25 Depth-rst search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front
26 Properties of depth-rst search Complete??
27 Properties of depth-rst search Complete?? No: fails in innite-depth spaces, spaces with loops Modify to avoid repeated states along path complete in nite spaces Time??
28 Properties of depth-rst search Complete?? No: fails in innite-depth spaces, spaces with loops Modify to avoid repeated states along path complete in nite spaces Time?? O(b m ): terrible if m is much larger than d but if solutions are dense, may be much faster than breadth-rst Space??
29 Properties of depth-rst search Complete?? No: fails in innite-depth spaces, spaces with loops Modify to avoid repeated states along path complete in nite spaces Time?? O(b m ): terrible if m is much larger than d but if solutions are dense, may be much faster than breadth-rst Space?? O(bm), i.e., linear space! Optimal??
30 Properties of depth-rst search Complete?? No: fails in innite-depth spaces, spaces with loops Modify to avoid repeated states along path complete in nite spaces Time?? O(b m ): terrible if m is much larger than d but if solutions are dense, may be much faster than breadth-rst Space?? O(bm), i.e., linear space! Optimal?? No!
31 Depth-limited search DFS + depth limit l: nodes at depth l have no successors Recursive implementation: function Depth-Limited-( problem, limit) returns soln/fail/cuto Recursive-DLS(Make-Node(Initial-State[problem]), problem, limit) function Recursive-DLS(node, problem, limit) returns soln/fail/cuto cuto-occurred? false if Goal-Test(problem, State[node]) then return node else if Depth[node] = limit then return cuto else for each successor in Expand(node, problem) do result Recursive-DLS(successor, problem, limit) if result = cuto then cuto-occurred? true else if result failure then return result if cuto-occurred? then return cuto else return failure
32 Iterative deepening search function Iterative-Deepening-( problem) returns a solution inputs: problem, a problem for depth 0 to do result Depth-Limited-( problem, depth) end if result cuto then return result
33 Iterative deepening search
34 Iterative deepening search
35 Iterative deepening search
36 Iterative deepening search
37 Properties of iterative deepening search Complete??
38 Properties of iterative deepening search Complete?? Yes Time??
39 Properties of iterative deepening search Complete?? Yes Time?? (d + 1)b 0 + db 1 + (d 1)b b d = O(b d ) Space??
40 Properties of iterative deepening search Complete?? Yes Time?? (d + 1)b 0 + db 1 + (d 1)b b d = O(b d ) Space?? O(bd) Optimal??
41 Properties of iterative deepening search Complete?? Yes Time?? (d + 1)b 0 + db 1 + (d 1)b b d = O(b d ) Space?? O(bd) Optimal?? Yes, if step cost = 1 Can be modied to explore uniform-cost tree
42 BFS Vs IDS Numerical comparison for b = 10 and d = 5, solution at far right leaf: N(IDS) = , , , 000 = 123, 456 N(BFS) = , , , , 990 = 1, 111, 101 IDS does better because other nodes at depth d are not expanded BFS can be modied to apply goal test when a node is generated
43 Summary of algorithms Criterion BF UC DF DL ID Complete? Yes Yes, No Yes, if l d Yes Time b d+1 b C /ɛ b m b l b d Space b d+1 b C /ɛ bm bl bd Optimal? Yes Yes No Yes, if l d Yes *: complete if branching factor is nite : complete if step cost is ɛ : optimal if step costs are all identical
44 Repeated states Failure to detect repeated states can turn a linear problem into an exponential one!
45 Graph search function Graph-( problem, fringe) returns a solution, or failure closed an empty set fringe Insert(Make-Node(Initial-State[problem]), fringe) loop do end if fringe is empty then return failure node Remove-Front(fringe) if Goal-Test(problem, State[node]) then return node if State[node] is not in closed then add State[node] to closed fringe InsertAll(Expand(node, problem), fringe)
46 Summary Variety of uninformed search Iterative deepening search uses only linear space and not much more time than other uninformed algorithms Graph search can be exponentially more ecient than tree search
47 Exercise: Space Dimension BFS vs IDS Assume: i) a well balanced search tree; ii) the goal state is the last one to be expanded in its level (e.g., the rightmost). if the branching factor is 3, the shallowest goal state is at depth 3 (root has depth 0) and we proceed breadth rst how many nodes are generated? if the branching factor is 4, the shallowest goal state is at depth 3 (root has depth 0) we proceed with an iterative deepening approach, how many nodes are generated? what happens if goal test is performed when inserting in the fringe instead of when removing (as it is in tree-search)?
48 Exercise: formalizing and solving problem through search The Wolf Sheep and Cabbage Problem A man owns a wolf, a sheep and a cabbage: He is on a river bank with a boat that can carry him with only one of his goodies at a time. The man wants to reach the other bank with his wolf, sheep and cabbage, but he knows that wolves eat sheeps, and sheeps eat cabbages, so he cannot leave them alone on a bank. Formalize the WSC problem as a search problem Use breadth rst to nd a solution
49 Exercise: formalizing and solving problem through search The Missionaries and Cannibals Three missionaries and three cannibals are on the same river bank, and want to cross it. They have a boat that can carry two people at most. Cannibals should never outnumber missionaries, on any bank, as they could eat them. Formalize the MC problem as a search problem Give a solution
50 Exercise: Optimality for Graph Dierences between dierent search Consider the state space graph in the gure, all moves cost 1. S1 R S2 R S3 G U S D Answer to the following questions: S0 State whether a Graph version of BFS woul always return the optimal solution for this problem, if not provide an execution where this is not the case. State whether a Graph version of IDS woul always return the optimal solution for this problem, if not provide an execution where this is not the case.
Problem solving and search
Problem solving and search Chapter 3 Chapter 3 1 Problem formulation & examples Basic search algorithms Outline Chapter 3 2 On holiday in Romania; currently in Arad. Flight leaves tomorrow from Bucharest
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 informationProblem solving and search
Problem solving and search hapter 3 hapter 3 1 Outline Problem-solving agents Problem types Problem formulation Example problems asic search algorithms hapter 3 3 Restricted form of general agent: Problem-solving
More informationArtificial Intelligence Problem Solving and Uninformed Search
Artificial Intelligence Problem Solving and Uninformed Search Maurizio Martelli, Viviana Mascardi {martelli, mascardi}@disi.unige.it University of Genoa Department of Computer and Information Science AI,
More informationKI-Programmierung. Basic Search Algorithms
KI-Programmierung Basic Search Algorithms Bernhard Beckert UNIVERSITÄT KOBLENZ-LANDAU Winter Term 2007/2008 B. Beckert: KI-Programmierung p.1 Example: Travelling in Romania Scenario On holiday in Romania;
More informationMultiagent Systems Problem Solving and Uninformed Search
Multiagent Systems Problem Solving and Uninformed Search Viviana Mascardi viviana.mascardi@unige.it MAS, University of Genoa, DIBRIS Classical AI 1 / 36 Disclaimer This presentation may contain material
More informationArtificial Intelligence, CS, Nanjing University Spring, 2018, Yang Yu. Lecture 2: Search 1.
rtificial Intelligence, S, Nanjing University Spring, 2018, Yang Yu Lecture 2: Search 1 http://lamda.nju.edu.cn/yuy/course_ai18.ashx Problem in the lecture 7 2 4 51 2 3 5 6 4 5 6 8 3 1 7 8 Start State
More informationArtificial Intelligence Uninformed search
Artificial Intelligence Uninformed search A.I. Uninformed search 1 The symbols&search hypothesis for AI Problem-solving agents A kind of goal-based agent Problem types Single state (fully observable) Search
More informationArtificial Intelligence: Search Part 1: Uninformed graph search
rtificial Intelligence: Search Part 1: Uninformed graph search Thomas Trappenberg January 8, 2009 ased on the slides provided by Russell and Norvig, hapter 3 Search outline Part 1: Uninformed search (tree
More informationAI: problem solving and search
: problem solving and search Stefano De Luca Slides mainly by Tom Lenaerts Outline Problem-solving agents A kind of goal-based agent Problem types Single state (fully observable) Search with partial information
More informationSolving problems by searching. Chapter 3
Solving problems by searching Chapter 3 Outline Problem-solving agents Problem types Problem formulation Example problems Basic search algorithms 2 Example: Romania On holiday in Romania; currently in
More informationArtificial Intelligence
Artificial Intelligence CSC348 Unit 3: Problem Solving and Search Syedur Rahman Lecturer, CSE Department North South University syedur.rahman@wolfson.oxon.org Artificial Intelligence: Lecture Notes The
More informationProblem solving and search
Problem solving and search Chapter 3 TB Artificial Intelligence Slides from AIMA http://aima.cs.berkeley.edu 1 /1 Outline Problem-solving agents Problem types Problem formulation Example problems Basic
More informationUninformed Search Strategies AIMA 3.4
Uninformed Search Strategies AIMA 3.4 CIS 391-2015 1 The Goat, Cabbage, Wolf Problem (From xkcd.com) CIS 391-2015 2 But First: Missionaries & Cannibals Three missionaries and three cannibals come to a
More informationCS:4420 Artificial Intelligence
S:4420 rtificial Intelligence Spring 2018 Uninformed Search esare Tinelli The University of Iowa opyright 2004 18, esare Tinelli and Stuart Russell a a These notes were originally developed by Stuart Russell
More informationProblem solving and search
Problem solving and search Chapter 3 Chapter 3 1 Outline Problem-solving agents Problem types Problem formulation Example problems Uninformed search algorithms Informed search algorithms Chapter 3 2 Restricted
More informationArtificial Intelligence
Artificial Intelligence Dr Ahmed Rafat Abas Computer Science Dept, Faculty of Computers and Informatics, Zagazig University arabas@zu.edu.eg http://www.arsaliem.faculty.zu.edu.eg/ Solving problems by searching
More informationProblem solving and search
Problem solving and search Chapter 3 Chapter 3 1 How to Solve a (Simple) Problem 7 2 4 1 2 5 6 3 4 5 8 3 1 6 7 8 Start State Goal State Chapter 3 2 Introduction Simple goal-based agents can solve problems
More informationProblem solving and search
Problem solving and search hapter 3 hapter 3 1 Outline Problem-solving agents Problem types Problem formulation Example problems asic search algorithms hapter 3 3 Example: omania On holiday in omania;
More informationProblem Solving. Russell and Norvig: Chapter 3
Problem Solving Russell and Norvig: Chapter 3 Example: Route finding Example: 8-puzzle 8 2 3 4 7 6 5 6 Initial state 7 8 Goal state Example: 8-puzzle 8 2 7 8 2 3 4 5 6 3 4 7 5 6 8 2 8 2 3 4 7 3 4 7 5 6
More informationA2 Uninformed Search
2 Uninformed Search Hantao Zhang http://www.cs.uiowa.edu/ hzhang/c145 The University of Iowa Department of omputer Science rtificial Intelligence p.1/82 Example: The 8-puzzle 2 8 3 1 7 6 4 5 It can be
More informationExample: The 8-puzzle. A2 Uninformed Search. It can be generalized to 15-puzzle, 24-puzzle, or. (n 2 1)-puzzle for n 6. Department of Computer Science
2 Uninformed Search Hantao Zhang http://www.cs.uiowa.edu/ hzhang/c145 The University of Iowa Department of omputer Science rtificial Intelligence p.1/82 Example: The 8-puzzle 2 1 7 8 3 6 4 5 It can be
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 informationLecture 3. Uninformed Search
Lecture 3 Uninformed Search 1 Uninformed search strategies Uninformed: While searching you have no clue whether one non-goal state is better than any other. Your search is blind. You don t know if your
More informationSolving problems by searching
Solving problems by searching Chapter 3 CS 2710 1 Outline Problem-solving agents Problem formulation Example problems Basic search algorithms CS 2710 - Blind Search 2 1 Goal-based Agents Agents that take
More informationCS 8520: Artificial Intelligence
CS 8520: Artificial Intelligence Solving Problems by Searching Paula Matuszek Spring, 2013 Slides based on Hwee Tou Ng, aima.eecs.berkeley.edu/slides-ppt, which are in turn based on Russell, aima.eecs.berkeley.edu/slides-pdf.
More informationProblem solving and search
Problem solving and search hapter 3 hapter 3 1 eminders ssignment 0 due midnight Thursday 9/8 ssignment 1 posted, due 9/20 (online or in box in 283) hapter 3 2 Outline Problem-solving agents Problem types
More informationProblem solving and search: Chapter 3, Sections 1 5
Problem solving and search: hapter 3, Sections 1 5 1 Outline Problem-solving agents Problem types Problem formulation Example problems asic search algorithms 2 Problem-solving agents estricted form of
More informationProblem solving and search: Chapter 3, Sections 1 5
Problem solving and search: hapter 3, Sections 1 5 S 480 2 Outline Problem-solving agents Problem types Problem formulation Example problems asic search algorithms Problem-solving agents estricted form
More informationUninformed search strategies (Section 3.4) Source: Fotolia
Uninformed search strategies (Section 3.4) Source: Fotolia Uninformed search strategies A search strategy is defined by picking the order of node expansion Uninformed search strategies use only the information
More informationProblem solving and search
Problem solving and search hapter 3 hapter 3 1 Outline Problem-solving agents Problem types Problem formulation Example problems asic search algorithms hapter 3 2 estricted form of general agent: Problem-solving
More informationARTIFICIAL INTELLIGENCE. Pathfinding and search
INFOB2KI 2017-2018 Utrecht University The Netherlands ARTIFICIAL INTELLIGENCE Pathfinding and search Lecturer: Silja Renooij These slides are part of the INFOB2KI Course Notes available from www.cs.uu.nl/docs/vakken/b2ki/schema.html
More informationChapter3. Problem-Solving Agents. Problem Solving Agents (cont.) Well-defined Problems and Solutions. Example Problems.
Problem-Solving Agents Chapter3 Solving Problems by Searching Reflex agents cannot work well in those environments - state/action mapping too large - take too long to learn Problem-solving agent - is one
More informationReminders. Problem solving and search. Problem-solving agents. Outline. Assignment 0 due 5pm today
ssignment 0 due 5pm today eminders Problem solving and search ssignment 1 posted, due 2/9 ection 105 will move to 9-10am starting next week hapter 3 hapter 3 1 hapter 3 2 Outline Problem-solving agents
More informationProblem-solving agents. Problem solving and search. Example: Romania. Reminders. Example: Romania. Outline. Chapter 3
Problem-solving agents Problem solving and search hapter 3 function imple-problem-olving-gent( percept) returns an action static: seq, an action sequence, initially empty state, some description of the
More informationProblem solving by search
Problem solving by search based on tuart ussel s slides (http://aima.cs.berkeley.edu) February 27, 2017, E533KUI - Problem solving by search 1 Outline Problem-solving agents Problem types Problem formulation
More informationOutline. Problem solving and search. Problem-solving agents. Example: Romania. Example: Romania. Problem types. Problem-solving agents.
Outline Problem-solving agents Problem solving and search hapter 3 Problem types Problem formulation Example problems asic search algorithms hapter 3 1 hapter 3 3 estricted form of general agent: Problem-solving
More informationIntroduction to Artificial Intelligence (G51IAI) Dr Rong Qu. Blind Searches
Introduction to Artificial Intelligence (G51IAI) Dr Rong Qu Blind Searches Blind Searches Function GENERAL-SEARCH (problem, QUEUING-FN) returns a solution or failure nodes = MAKE-QUEUE(MAKE-NODE(INITIAL-STATE[problem]))
More informationSolving Problems by Searching
Solving Problems by Searching 1 Terminology State State Space Initial State Goal Test Action Step Cost Path Cost State Change Function State-Space Search 2 Formal State-Space Model Problem = (S, s, A,
More informationProblem Solving and Search. Chapter 3
Problem olving and earch hapter 3 Outline Problem-solving agents Problem formulation Example problems asic search algorithms In the simplest case, an agent will: formulate a goal and a problem; Problem-olving
More informationSolving problems by searching
Solving problems by searching Chapter 3 Systems 1 Outline Problem-solving agents Problem types Problem formulation Example problems Basic search algorithms Systems 2 Problem-solving agents Systems 3 Example:
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 informationUninformed Search Strategies AIMA
Uninformed Search Strategies AIMA 3.3-3.4 CIS 421/521 - Intro to AI - Fall 2017 1 Review: Formulating search problems Formulate search problem States: configurations of the puzzle (9! configurations) Actions:
More informationChapter 2. Blind Search 8/19/2017
Chapter 2 1 8/19/2017 Problem-solving agents Problem types Problem formulation Example problems Basic search algorithms 8/19/2017 2 8/19/2017 3 On holiday in Romania; currently in Arad. Flight leaves tomorrow
More informationCS 380: Artificial Intelligence Lecture #3
CS 380: Artificial Intelligence Lecture #3 William Regli Outline Problem-solving agents Problem types Problem formulation Example problems Basic search algorithms 1 Problem-solving agents Example: Romania
More informationSearch Algorithms. Uninformed Blind search. Informed Heuristic search. Important concepts:
Uninformed Search Search Algorithms Uninformed Blind search Breadth-first uniform first depth-first Iterative deepening depth-first Bidirectional Branch and Bound Informed Heuristic search Greedy search,
More informationSolving problems by searching
Solving problems by searching 1 C H A P T E R 3 Problem-solving agents Problem types Problem formulation Example problems Basic search algorithms Outline 2 Problem-solving agents 3 Note: this is offline
More informationITCS 6150 Intelligent Systems. Lecture 3 Uninformed Searches
ITCS 6150 Intelligent Systems Lecture 3 Uninformed Searches Outline Problem Solving Agents Restricted form of general agent Problem Types Fully vs. partially observable, deterministic vs. stochastic Problem
More informationUninformed search strategies
Uninformed search strategies A search strategy is defined by picking the order of node expansion Uninformed search strategies use only the informa:on available in the problem defini:on Breadth- first search
More informationUninformed (also called blind) search algorithms
Uninformed (also called blind) search algorithms First Lecture Today (Thu 30 Jun) Read Chapters 18.6.1-2, 20.3.1 Second Lecture Today (Thu 30 Jun) Read Chapter 3.1-3.4 Next Lecture (Tue 5 Jul) Chapters
More information521495A: Artificial Intelligence
521495A: Artificial Intelligence Search Lectured by Abdenour Hadid Associate Professor, CMVS, University of Oulu Slides adopted from http://ai.berkeley.edu Agent An agent is an entity that perceives the
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 informationCS 771 Artificial Intelligence. Problem Solving by Searching Uninformed search
CS 771 Artificial Intelligence Problem Solving by Searching Uninformed search Complete architectures for intelligence? Search? Solve the problem of what to do. Learning? Learn what to do. Logic and inference?
More informationUninformed Search B. Navigating through a search tree. Navigating through a search tree. Navigating through a search tree
Uninformed Search Russell and Norvig chap. 3 D E 1 Unexpanded s: the fringe Tree search nitial state t every point in the search process we keep track of a list of s that haven t been expanded yet: the
More informationOutline. Solving problems by searching. Problem-solving agents. Example: Romania
Outline Solving problems by searching Chapter 3 Problem-solving agents Problem types Problem formulation Example problems Basic search algorithms Systems 1 Systems 2 Problem-solving agents Example: Romania
More informationSolving Problem by Searching. Chapter 3
Solving Problem by Searching Chapter 3 Outline Problem-solving agents Problem formulation Example problems Basic search algorithms blind search Heuristic search strategies Heuristic functions Problem-solving
More informationEE562 ARTIFICIAL INTELLIGENCE FOR ENGINEERS
EE562 ARTIFICIAL INTELLIGENCE FOR ENGINEERS Lecture 3, 4/6/2005 University of Washington, Department of Electrical Engineering Spring 2005 Instructor: Professor Jeff A. Bilmes 4/6/2005 EE562 1 Today: Basic
More informationLogistics. u AI Nugget presentations. u Project Team Wikis, pages. u PolyLearn: Does everybody have access? u Lab and Homework Assignments.
Logistics u AI Nugget presentations u Section 1: Thomas Soria, Gagandeep Singh Kohli, Alex Ledwith u Section 3: Martin Silverio u Project Team Wikis, pages u project description refined: v Features, Requirements,
More informationSolving Problems: Blind Search
Solving Problems: Blind Search Instructor: B. John Oommen Chancellor s Professor Fellow: IEEE ; Fellow: IAPR School of Computer Science, Carleton University, Canada The primary source of these notes are
More informationProblem Solving Agents
Problem Solving Agents Well-defined Problems Solutions (as a sequence of actions). Examples Search Trees Uninformed Search Algorithms Well-defined Problems 1. State Space, S An Initial State, s 0 S. A
More informationChapter 4. Uninformed Search Strategies
Chapter 4. Uninformed Search Strategies An uninformed (a.k.a. blind, brute-force) search algorithm generates the search tree without using any domain specific knowledge. The two basic approaches differ
More informationPlanning and search. Lecture 1: Introduction and Revision of Search. Lecture 1: Introduction and Revision of Search 1
Planning and search Lecture 1: Introduction and Revision of Search Lecture 1: Introduction and Revision of Search 1 Lecturer: Natasha lechina email: nza@cs.nott.ac.uk ontact and web page web page: http://www.cs.nott.ac.uk/
More informationUninformed Search. Reading: Chapter 4 (Tuesday, 2/5) HW#1 due next Tuesday
Uninformed Search Reading: Chapter 4 (Tuesday, 2/5) HW#1 due next Tuesday 1 Uninformed Search through the space of possible solutions Use no knowledge about which path is likely to be best Exception: uniform
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 informationProblem Solving as Search. CMPSCI 383 September 15, 2011
Problem Solving as Search CMPSCI 383 September 15, 2011 1 Today s lecture Problem-solving as search Uninformed search methods Problem abstraction Bold Claim: Many problems faced by intelligent agents,
More informationBasic Search. Fall Xin Yao. Artificial Intelligence: Basic Search
Basic Search Xin Yao Fall 2017 Fall 2017 Artificial Intelligence: Basic Search Xin Yao Outline Motivating Examples Problem Formulation From Searching to Search Tree Uninformed Search Methods Breadth-first
More informationChapter 3. A problem-solving agent is a kind of goal-based agent. It decide what to do by finding sequences of actions that lead to desirable states.
Chapter 3 A problem-solving agent is a kind of goal-based agent. It decide what to do by finding sequences of actions that lead to desirable states. A problem can be defined by four components : 1. The
More informationUninformed Search. CS171, Winter 2018 Introduction to Artificial Intelligence Prof. Richard Lathrop. Reading: R&N
Uninformed Search CS171, Winter 2018 Introduction to Artificial Intelligence Prof. Richard Lathrop Reading: R&N 3.1-3.4 Uninformed search strategies Uninformed (blind): You have no clue whether one non-goal
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 informationUninformed Search B. Navigating through a search tree. Navigating through a search tree. Navigating through a search tree
Uninformed Search Russell and Norvig chap. 3 Following this, the pong paddle went on a mission to destroy tari headquarters and, due to a mixup, found himself inside the game The Matrix Reloaded. oy, was
More informationArtificial Intelligence
Artificial Intelligence hapter 1 hapter 1 1 Iterative deepening search function Iterative-Deepening-Search( problem) returns a solution inputs: problem, a problem for depth 0 to do result Depth-Limited-Search(
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 informationIntroduction to Intelligent Systems
Problem Solving by Search Objectives Well-Defined Problems Tree search Uninformed search: BFS, DFS, DLS, and IDS Heuristic search: GBFS and A* Reference Russell & Norvig: Chapter 3 Y. Xiang, CIS 3700,
More informationSolving Problems by Searching
INF5390 Kunstig intelligens Solving Problems by Searching Roar Fjellheim Outline Problem-solving agents Example problems Search programs Uninformed search Informed search Summary AIMA Chapter 3: Solving
More informationUNINFORMED SEARCH. What to do if teammates drop? Still have 3 or more? No problem keep going. Have two or fewer and want to be merged?
UNINFORMED SEARCH EECS492 January 14, 2010 Administrative What to do if teammates drop? Still have 3 or more? No problem keep going. Have two or fewer and want to be merged? We ll do what we can. Submitting
More informationRobot Programming with Lisp
6. Search Algorithms Gayane Kazhoyan (Stuart Russell, Peter Norvig) Institute for University of Bremen Contents Problem Definition Uninformed search strategies BFS Uniform-Cost DFS Depth-Limited Iterative
More informationCSC 2114: Artificial Intelligence Search
CSC 2114: Artificial Intelligence Search Ernest Mwebaze emwebaze@cit.ac.ug Office: Block A : 3 rd Floor [Slide Credit Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. Reference materials
More informationGoal-Based Agents Problem solving as search. Outline
Goal-Based Agents Problem solving as search Vasant Honavar Bioinformatics and Computational Biology Program Center for Computational Intelligence, Learning, & Discovery honavar@cs.iastate.edu www.cs.iastate.edu/~honavar/
More informationSolving Problems by Searching
INF5390 Kunstig intelligens Sony Vaio VPC-Z12 Solving Problems by Searching Roar Fjellheim Outline Problem-solving agents Example problems Search programs Uninformed search Informed search Summary AIMA
More informationCS 151: Intelligent Agents, Problem Formulation and Search
CS 151: Intelligent Agents, Problem Formulation and Search How do we make a computer "smart?" Computer, clean the house! Um OK?? This one's got no chance How do we represent this problem? Hmmm where to
More informationCS 331: Artificial Intelligence Uninformed Search. Real World Search Problems
S 331: rtificial Intelligence Uninformed Search 1 Real World Search Problems 2 1 Simpler Search Problems 3 Static Observable Discrete Deterministic Single-agent ssumptions bout Our Environment 4 2 Search
More informationCS 331: Artificial Intelligence Uninformed Search. Real World Search Problems
S 331: rtificial Intelligence Uninformed Search 1 Real World Search Problems 2 1 Simpler Search Problems 3 ssumptions bout Our Environment Fully Observable Deterministic Sequential Static Discrete Single-agent
More informationIntelligent Agents. Foundations of Artificial Intelligence. Problem-Solving as Search. A Simple Reflex Agent. Agent with Model and Internal State
Intelligent s Foundations of Artificial Intelligence Problem-Solving as Search S7 Fall 007 Thorsten Joachims : Anything that can be viewed as perceiving its environment through sensors and acting upon
More informationARTIFICIAL INTELLIGENCE SOLVING PROBLEMS BY SEARCHING. Chapter 3
ARTIFICIAL INTELLIGENCE SOLVING PROBLEMS BY SEARCHING Chapter 3 1 PROBLEM SOLVING We want: To automatically solve a problem We need: A representation of the problem Algorithms that use some strategy to
More informationCITS3001. Algorithms, Agents and Artificial Intelligence. Semester 1, 2015
CITS3001 Algorithms, Agents and Artificial Intelligence Semester 1, 2015 Wei Liu School of Computer Science & Software Eng. The University of Western Australia 6. Uninformed search algorithms AIMA, Ch.
More informationSolving Problems by Searching
Solving Problems by Searching CS486/686 University of Waterloo Sept 11, 2008 1 Outline Problem solving agents and search Examples Properties of search algorithms Uninformed search Breadth first Depth first
More informationCS486/686 Lecture Slides (c) 2015 P.Poupart
1 2 Solving Problems by Searching [RN2] Sec 3.1-3.5 [RN3] Sec 3.1-3.4 CS486/686 University of Waterloo Lecture 2: May 7, 2015 3 Outline Problem solving agents and search Examples Properties of search algorithms
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 informationCS486/686 Lecture Slides (c) 2014 P.Poupart
1 2 1 Solving Problems by Searching [RN2] Sec 3.1-3.5 [RN3] Sec 3.1-3.4 CS486/686 University of Waterloo Lecture 2: January 9, 2014 3 Outline Problem solving agents and search Examples Properties of search
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 informationChapter 3: Solving Problems by Searching
Chapter 3: Solving Problems by Searching Prepared by: Dr. Ziad Kobti 1 Problem-Solving Agent Reflex agent -> base its actions on a direct mapping from states to actions. Cannot operate well in large environments
More informationSolving Problems by Searching
Solving Problems by Searching Agents, Goal-Based Agents, Problem-Solving Agents Search Problems Blind Search Strategies Agents sensors environment percepts actions? agent effectors Definition. An agent
More informationSolving Problems by Searching
Solving Problems by Searching Agents, Goal-Based Agents, Problem-Solving Agents Search Problems Blind Search Strategies Agents sensors environment percepts actions? agent effectors Definition. An agent
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 informationAGENTS AND ENVIRONMENTS. What is AI in reality?
AGENTS AND ENVIRONMENTS What is AI in reality? AI is our attempt to create a machine that thinks (or acts) humanly (or rationally) Think like a human Cognitive Modeling Think rationally Logic-based Systems
More informationPengju
Introduction to AI Chapter03 Solving Problems by Uninformed Searching(3.1~3.4) Pengju Ren@IAIR Outline Problem-solving agents Problem types Problem formulation Search on Trees and Graphs Uninformed algorithms
More informationARTIFICIAL INTELLIGENCE (CSC9YE ) LECTURES 2 AND 3: PROBLEM SOLVING
ARTIFICIAL INTELLIGENCE (CSC9YE ) LECTURES 2 AND 3: PROBLEM SOLVING BY SEARCH Gabriela Ochoa http://www.cs.stir.ac.uk/~goc/ OUTLINE Problem solving by searching Problem formulation Example problems Search
More informationPengju XJTU 2016
Introduction to AI Chapter03 Solving Problems by Uninformed Searching(3.1~3.4) Pengju Ren@IAIR Outline Problem-solving agents Problem types Problem formulation Search on Trees and Graphs Uninformed algorithms
More informationArtificial Intelligence Search: summary&exercises. Peter Antal
Artificial Intelligence Search: summary&exercises Peter Antal antal@mit.bme.hu 1 A problem is defined by: An initial state, e.g. Arad Successor function S(X)= set of action-state pairs e.g. S(Arad)={
More informationCS 331: Artificial Intelligence Uninformed Search. Leftovers from last time
S 331: rtificial Intelligence Uninformed Search 1 Leftovers from last time Discrete/ontinuous Discrete: finite number of values eg. Rating can be thumbs up or down ontinuous: infinite continuum of values
More information