SlideShare una empresa de Scribd logo
1 de 87
Dr. Mustafa Jarrar [email_address]   University of Birzeit Chapter 3 Uni-Informed Search Advanced Artificial Intelligence  (SCOM7341) Lecture Notes,  Advanced Artificial Intelligence (SCOM7341)  University of Birzeit 2 nd  Semester, 2011
Example: Romania You are in Arad and want to go to Bucharest    How to design an  intelligent agent  to find the way between 2 cities?
Example: The 8-puzzle    How can we design an  intelligent agent  to solve the 8-puzzel?
Search as Problem-Solving Strategy ,[object Object],[object Object],[object Object]
Search as Problem-Solving Strategy ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Motivation ,[object Object],[object Object],[object Object]
Problem Formulation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Problem Formulation (The Romania Example) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Problem Formulation (The 8- Puzzle Example) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Problem Formulation (Real-life Applications) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Car Navigation Routing in Computer networks  Military operation planning  Airline travel planning
Problem Formulation (Real-life Applications) ,[object Object],   What is the state space for each of them? A set of places with links between them, which have been visited
Problem Formulation (Real-life Applications) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Problem Formulation (Real-life Applications) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Problem Formulation (Real-life Applications) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Problem Formulation (Real-life Applications) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Searching for Solutions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Search Terminology ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example: Graph Search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],S3 A4 C2 D3 E1 B2 G0 1 1 1 3 1 3 3 4 5 1 2
Traversing a Graph as Tree S3 5 A4 D3 1 1 3 3 4 2 C2 D3 G0 G0 G0 E1 G0 1 1 3 3 4 2 C2 D3 G0 G0 E1 G0 1 3 B2 1 3 C2 D3 G0 G0 E1 G0 1 3 4 E1 G0 2 4 3 2 4 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],2 S3 A4 C2 D3 E1 B2 G0 1 1 1 3 1 3 3 4 5 1 2
Discussion Who can give examples of tree search strategies? Why a search strategy is better than another?
Example: Breadth First Search S3 5 A4 D3 1 1 3 3 4 2 C2 D3 G0 G0 G0 E1 G0 1 1 3 3 4 2 C2 D3 G0 G0 E1 G0 1 3 B2 1 3 C2 D3 G0 G0 E1 G0 1 3 4 E1 G0 2 4 3 2 4 S3 A4 C2 D3 E1 B2 G0 1 1 1 3 1 3 3 4 5 1 2
Example: Greedy Search S3 5 A4 D3 1 1 3 3 4 2 C2 D3 G0 G0 G0 E1 G0 1 1 3 3 4 2 C2 D3 G0 G0 E1 G0 1 3 B2 1 3 C2 D3 G0 G0 E1 G0 1 3 4 E1 G0 2 4 3 2 4 S3 A4 C2 D3 E1 B2 G0 1 1 1 3 1 3 3 4 5 1 2
Example: A* Search S3 5 A4 D3 1 1 3 3 4 2 C2 D3 G0 G0 G0 E1 G0 1 1 3 3 4 2 C2 D3 G0 G0 E1 G0 1 3 B2 1 3 C2 D3 G0 G0 E1 G0 1 3 4 E1 G0 2 4 3 2 4 S3 A4 C2 D3 E1 B2 G0 1 1 1 3 1 3 3 4 5 1 2
General Tree Search ,[object Object],[object Object],[object Object],[object Object],[object Object]
Search Strategies ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Evaluation of Search Strategies ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
1: Breadth-First Search
[object Object],Breadth-First Search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Breadth-First Snapshot 1 Initial Visited Fringe Current Visible Goal 1 2 3 Fringe: []  + [2,3]
Breadth-First Snapshot 2 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 Fringe: [3]  + [4,5]
Breadth-First Snapshot 3 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 Fringe: [4,5]  + [6,7]
Breadth-First Snapshot 4 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 Fringe: [5,6,7]  + [8,9]
Breadth-First Snapshot 5 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 Fringe: [6,7,8,9]  + [10,11]
Breadth-First Snapshot 6 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 Fringe: [7,8,9,10,11]  + [12,13]
Breadth-First Snapshot 7 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Fringe: [8,9.10,11,12,13]  + [14,15]
Breadth-First Snapshot 8 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Fringe: [9,10,11,12,13,14,15]  + [16,17]
Breadth-First Snapshot 9 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Fringe: [10,11,12,13,14,15,16,17]  + [18,19]
Breadth-First Snapshot 10 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Fringe: [11,12,13,14,15,16,17,18,19]  + [20,21]
Breadth-First Snapshot 11 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Fringe: [12, 13, 14, 15, 16, 17, 18, 19, 20, 21]  + [22,23]
Breadth-First Snapshot 12 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Fringe: [13,14,15,16,17,18,19,20,21]  + [22,23] Note:  The goal node is “visible” here, but we can not perform the goal test yet.
Breadth-First Snapshot 13 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Fringe: [14,15,16,17,18,19,20,21,22,23,24,25]  + [26,27]
Breadth-First Snapshot 14 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Fringe: [15,16,17,18,19,20,21,22,23,24,25,26,27]  + [28,29]
Breadth-First Snapshot 15 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fringe: [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]  + [30,31]
Breadth-First Snapshot 16 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fringe: [17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]
Breadth-First Snapshot 17 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fringe: [18,19,20,21,22,23,24,25,26,27,28,29,30,31]
Breadth-First Snapshot 18 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fringe: [19,20,21,22,23,24,25,26,27,28,29,30,31]
Breadth-First Snapshot 19 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fringe: [20,21,22,23,24,25,26,27,28,29,30,31]
Breadth-First Snapshot 20 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fringe: [21,22,23,24,25,26,27,28,29,30,31]
Breadth-First Snapshot 21 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fringe: [22,23,24,25,26,27,28,29,30,31]
Breadth-First Snapshot 22 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fringe: [23,24,25,26,27,28,29,30,31]
Breadth-First Snapshot 23 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fringe: [24,25,26,27,28,29,30,31]
Breadth-First Snapshot 24 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fringe: [25,26,27,28,29,30,31]  Note:  The goal test is positive for this node, and a solution is found in 24 steps.
Properties of Breadth-First Search (BFS) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],b Branching Factor d The  depth of the goal
2- Uniform-Cost -First
[object Object],[object Object],[object Object],Uniform-Cost -First
Uniform-Cost Search (UCS)
Uniform-Cost Search (UCS)
Uniform-Cost Search (UCS)
Uniform-Cost Search (UCS)
Uniform-Cost Search (UCS)
Uniform-Cost Search (UCS)
Uniform-Cost Search (UCS)
Uniform-Cost Search (UCS)
Properties of Uniform-cost Search (UCS) ,[object Object],[object Object],[object Object],[object Object],[object Object],b Branching Factor d Depth of the goal/tree
Breadth-First vs. Uniform-Cost ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
3- Depth-First Search
Depth-First Search ,[object Object],[object Object],[object Object],L M N O P G Q H J I K F E D B C A
Depth-First Search ,[object Object],[object Object],[object Object],[object Object],[object Object]
Recursive Depth-First Search ,[object Object],[object Object],[object Object],[object Object],print node and print c and
Properties of Depth-First Search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Depth-First vs. Breadth-First ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
4- Depth-Limited Search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],b branching factor l depth limit
5- Iterative Deepening Depth-First Search
[object Object],[object Object],[object Object],Iterative Deepening Depth-First Search
Iterative deepening search l =0
Iterative deepening search l =1
Iterative deepening search l =2
Iterative deepening search l =3
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Iterative Deepening Depth-First Search
Iterative Deepening Search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Iterative Deepening Search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Properties of Iterative Deepening Search ,[object Object],[object Object],[object Object],[object Object],b branching factor d Tree/goal depth
6- Bi-directional Search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],b branching factor d tree depth Time Complexity b d/2 Space Complexity b d/2 Completeness yes (b finite, breadth-first for both directions) Optimality yes (all step costs identical, breadth-first for both directions)
Summary ,[object Object],[object Object],Iterative deepening search uses only linear space and not much more time than other uninformed algorithms
[object Object],[object Object],[object Object],[object Object],Summary
Summary (When to use what) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Improving Search Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Más contenido relacionado

La actualidad más candente

Ch 9-1.Machine Learning: Symbol-based
Ch 9-1.Machine Learning: Symbol-basedCh 9-1.Machine Learning: Symbol-based
Ch 9-1.Machine Learning: Symbol-basedbutest
 
International Refereed Journal of Engineering and Science (IRJES)
International Refereed Journal of Engineering and Science (IRJES)International Refereed Journal of Engineering and Science (IRJES)
International Refereed Journal of Engineering and Science (IRJES)irjes
 
Analytical learning
Analytical learningAnalytical learning
Analytical learningswapnac12
 
Sample prac exam2013
Sample prac exam2013Sample prac exam2013
Sample prac exam2013hccit
 
Inductive analytical approaches to learning
Inductive analytical approaches to learningInductive analytical approaches to learning
Inductive analytical approaches to learningswapnac12
 
L06 stemmer and edit distance
L06 stemmer and edit distanceL06 stemmer and edit distance
L06 stemmer and edit distanceananth
 
Reasoning with optional and preferred requirements
Reasoning with optional and preferred requirementsReasoning with optional and preferred requirements
Reasoning with optional and preferred requirementsNeil Ernst
 
Designing of an efficient algorithm for identifying Abbreviation definitions ...
Designing of an efficient algorithm for identifying Abbreviation definitions ...Designing of an efficient algorithm for identifying Abbreviation definitions ...
Designing of an efficient algorithm for identifying Abbreviation definitions ...ijcsit
 
Conceptual Fixture Design Method Based On Petri Net
Conceptual Fixture Design Method Based On Petri NetConceptual Fixture Design Method Based On Petri Net
Conceptual Fixture Design Method Based On Petri NetIJRES Journal
 
Probabilistic Models of Novel Document Rankings for Faceted Topic Retrieval
Probabilistic Models of Novel Document Rankings for Faceted Topic RetrievalProbabilistic Models of Novel Document Rankings for Faceted Topic Retrieval
Probabilistic Models of Novel Document Rankings for Faceted Topic RetrievalYI-JHEN LIN
 
Machine Learning Unit 1 Semester 3 MSc IT Part 2 Mumbai University
Machine Learning Unit 1 Semester 3  MSc IT Part 2 Mumbai UniversityMachine Learning Unit 1 Semester 3  MSc IT Part 2 Mumbai University
Machine Learning Unit 1 Semester 3 MSc IT Part 2 Mumbai UniversityMadhav Mishra
 
MachineLearning.ppt
MachineLearning.pptMachineLearning.ppt
MachineLearning.pptbutest
 
Amit ppt
Amit pptAmit ppt
Amit pptamitp26
 
FUZZY STATISTICAL DATABASE AND ITS PHYSICAL ORGANIZATION
FUZZY STATISTICAL DATABASE AND ITS PHYSICAL ORGANIZATIONFUZZY STATISTICAL DATABASE AND ITS PHYSICAL ORGANIZATION
FUZZY STATISTICAL DATABASE AND ITS PHYSICAL ORGANIZATIONijdms
 

La actualidad más candente (18)

Ch 9-1.Machine Learning: Symbol-based
Ch 9-1.Machine Learning: Symbol-basedCh 9-1.Machine Learning: Symbol-based
Ch 9-1.Machine Learning: Symbol-based
 
International Refereed Journal of Engineering and Science (IRJES)
International Refereed Journal of Engineering and Science (IRJES)International Refereed Journal of Engineering and Science (IRJES)
International Refereed Journal of Engineering and Science (IRJES)
 
Analytical learning
Analytical learningAnalytical learning
Analytical learning
 
Sample prac exam2013
Sample prac exam2013Sample prac exam2013
Sample prac exam2013
 
Inductive analytical approaches to learning
Inductive analytical approaches to learningInductive analytical approaches to learning
Inductive analytical approaches to learning
 
L06 stemmer and edit distance
L06 stemmer and edit distanceL06 stemmer and edit distance
L06 stemmer and edit distance
 
Reasoning with optional and preferred requirements
Reasoning with optional and preferred requirementsReasoning with optional and preferred requirements
Reasoning with optional and preferred requirements
 
Designing of an efficient algorithm for identifying Abbreviation definitions ...
Designing of an efficient algorithm for identifying Abbreviation definitions ...Designing of an efficient algorithm for identifying Abbreviation definitions ...
Designing of an efficient algorithm for identifying Abbreviation definitions ...
 
R01741124127
R01741124127R01741124127
R01741124127
 
Conceptual Fixture Design Method Based On Petri Net
Conceptual Fixture Design Method Based On Petri NetConceptual Fixture Design Method Based On Petri Net
Conceptual Fixture Design Method Based On Petri Net
 
Lecture7
Lecture7Lecture7
Lecture7
 
Probabilistic Models of Novel Document Rankings for Faceted Topic Retrieval
Probabilistic Models of Novel Document Rankings for Faceted Topic RetrievalProbabilistic Models of Novel Document Rankings for Faceted Topic Retrieval
Probabilistic Models of Novel Document Rankings for Faceted Topic Retrieval
 
Ai popular search algorithms
Ai   popular search algorithmsAi   popular search algorithms
Ai popular search algorithms
 
Machine Learning Unit 1 Semester 3 MSc IT Part 2 Mumbai University
Machine Learning Unit 1 Semester 3  MSc IT Part 2 Mumbai UniversityMachine Learning Unit 1 Semester 3  MSc IT Part 2 Mumbai University
Machine Learning Unit 1 Semester 3 MSc IT Part 2 Mumbai University
 
MachineLearning.ppt
MachineLearning.pptMachineLearning.ppt
MachineLearning.ppt
 
icpr_2012
icpr_2012icpr_2012
icpr_2012
 
Amit ppt
Amit pptAmit ppt
Amit ppt
 
FUZZY STATISTICAL DATABASE AND ITS PHYSICAL ORGANIZATION
FUZZY STATISTICAL DATABASE AND ITS PHYSICAL ORGANIZATIONFUZZY STATISTICAL DATABASE AND ITS PHYSICAL ORGANIZATION
FUZZY STATISTICAL DATABASE AND ITS PHYSICAL ORGANIZATION
 

Destacado

Jarrar.lecture notes.aai.2011s.ch8.fol.introduction
Jarrar.lecture notes.aai.2011s.ch8.fol.introductionJarrar.lecture notes.aai.2011s.ch8.fol.introduction
Jarrar.lecture notes.aai.2011s.ch8.fol.introductionPalGov
 
Jarrar.lecture notes.aai.2011s.descriptionlogic
Jarrar.lecture notes.aai.2011s.descriptionlogicJarrar.lecture notes.aai.2011s.descriptionlogic
Jarrar.lecture notes.aai.2011s.descriptionlogicPalGov
 
Jarrar.lecture notes.aai.2011s.ch4.informedsearch
Jarrar.lecture notes.aai.2011s.ch4.informedsearchJarrar.lecture notes.aai.2011s.ch4.informedsearch
Jarrar.lecture notes.aai.2011s.ch4.informedsearchPalGov
 
Jarrar.lecture notes.aai.2011s.ontology part5_egovernmentcasestudy
Jarrar.lecture notes.aai.2011s.ontology part5_egovernmentcasestudyJarrar.lecture notes.aai.2011s.ontology part5_egovernmentcasestudy
Jarrar.lecture notes.aai.2011s.ontology part5_egovernmentcasestudyPalGov
 
Jarrar.lecture notes.aai.2011s.ch2.intelligentagents
Jarrar.lecture notes.aai.2011s.ch2.intelligentagentsJarrar.lecture notes.aai.2011s.ch2.intelligentagents
Jarrar.lecture notes.aai.2011s.ch2.intelligentagentsPalGov
 
Richardson Eyres - Three Peaks Challenge Competition
Richardson Eyres - Three Peaks Challenge Competition Richardson Eyres - Three Peaks Challenge Competition
Richardson Eyres - Three Peaks Challenge Competition Richardson Eyres
 
De cuong báo cáo thuc tap khoá luận tot nghiep
De cuong báo cáo thuc tap   khoá luận tot nghiepDe cuong báo cáo thuc tap   khoá luận tot nghiep
De cuong báo cáo thuc tap khoá luận tot nghiepDoan Tran Ngocvu
 

Destacado (7)

Jarrar.lecture notes.aai.2011s.ch8.fol.introduction
Jarrar.lecture notes.aai.2011s.ch8.fol.introductionJarrar.lecture notes.aai.2011s.ch8.fol.introduction
Jarrar.lecture notes.aai.2011s.ch8.fol.introduction
 
Jarrar.lecture notes.aai.2011s.descriptionlogic
Jarrar.lecture notes.aai.2011s.descriptionlogicJarrar.lecture notes.aai.2011s.descriptionlogic
Jarrar.lecture notes.aai.2011s.descriptionlogic
 
Jarrar.lecture notes.aai.2011s.ch4.informedsearch
Jarrar.lecture notes.aai.2011s.ch4.informedsearchJarrar.lecture notes.aai.2011s.ch4.informedsearch
Jarrar.lecture notes.aai.2011s.ch4.informedsearch
 
Jarrar.lecture notes.aai.2011s.ontology part5_egovernmentcasestudy
Jarrar.lecture notes.aai.2011s.ontology part5_egovernmentcasestudyJarrar.lecture notes.aai.2011s.ontology part5_egovernmentcasestudy
Jarrar.lecture notes.aai.2011s.ontology part5_egovernmentcasestudy
 
Jarrar.lecture notes.aai.2011s.ch2.intelligentagents
Jarrar.lecture notes.aai.2011s.ch2.intelligentagentsJarrar.lecture notes.aai.2011s.ch2.intelligentagents
Jarrar.lecture notes.aai.2011s.ch2.intelligentagents
 
Richardson Eyres - Three Peaks Challenge Competition
Richardson Eyres - Three Peaks Challenge Competition Richardson Eyres - Three Peaks Challenge Competition
Richardson Eyres - Three Peaks Challenge Competition
 
De cuong báo cáo thuc tap khoá luận tot nghiep
De cuong báo cáo thuc tap   khoá luận tot nghiepDe cuong báo cáo thuc tap   khoá luận tot nghiep
De cuong báo cáo thuc tap khoá luận tot nghiep
 

Similar a Jarrar.lecture notes.aai.2011s.ch3.uniformedsearch

Jarrar: Un-informed Search
Jarrar: Un-informed SearchJarrar: Un-informed Search
Jarrar: Un-informed SearchMustafa Jarrar
 
Artificial Intelligence Searching Techniques
Artificial Intelligence Searching TechniquesArtificial Intelligence Searching Techniques
Artificial Intelligence Searching TechniquesDr. C.V. Suresh Babu
 
Popular search algorithms
Popular search algorithmsPopular search algorithms
Popular search algorithmsMinakshi Atre
 
Artificial intelligent Lec 3-ai chapter3-search
Artificial intelligent Lec 3-ai chapter3-searchArtificial intelligent Lec 3-ai chapter3-search
Artificial intelligent Lec 3-ai chapter3-searchTaymoor Nazmy
 
AI_03_Solving Problems by Searching.pptx
AI_03_Solving Problems by Searching.pptxAI_03_Solving Problems by Searching.pptx
AI_03_Solving Problems by Searching.pptxYousef Aburawi
 
(Radhika) presentation on chapter 2 ai
(Radhika) presentation on chapter 2 ai(Radhika) presentation on chapter 2 ai
(Radhika) presentation on chapter 2 aiRadhika Srinivasan
 
problem solve and resolving in ai domain , probloms
problem solve and resolving in ai domain , problomsproblem solve and resolving in ai domain , probloms
problem solve and resolving in ai domain , problomsSlimAmiri
 
AI_Session 3 Problem Solving Agent and searching for solutions.pptx
AI_Session 3 Problem Solving Agent and searching for solutions.pptxAI_Session 3 Problem Solving Agent and searching for solutions.pptx
AI_Session 3 Problem Solving Agent and searching for solutions.pptxAsst.prof M.Gokilavani
 
CptS 440 / 540 Artificial Intelligence
CptS 440 / 540 Artificial IntelligenceCptS 440 / 540 Artificial Intelligence
CptS 440 / 540 Artificial Intelligencebutest
 
Artificial intelligence(04)
Artificial intelligence(04)Artificial intelligence(04)
Artificial intelligence(04)Nazir Ahmed
 

Similar a Jarrar.lecture notes.aai.2011s.ch3.uniformedsearch (20)

Jarrar: Un-informed Search
Jarrar: Un-informed SearchJarrar: Un-informed Search
Jarrar: Un-informed Search
 
Artificial Intelligence Searching Techniques
Artificial Intelligence Searching TechniquesArtificial Intelligence Searching Techniques
Artificial Intelligence Searching Techniques
 
Popular search algorithms
Popular search algorithmsPopular search algorithms
Popular search algorithms
 
Artificial intelligent Lec 3-ai chapter3-search
Artificial intelligent Lec 3-ai chapter3-searchArtificial intelligent Lec 3-ai chapter3-search
Artificial intelligent Lec 3-ai chapter3-search
 
AI_03_Solving Problems by Searching.pptx
AI_03_Solving Problems by Searching.pptxAI_03_Solving Problems by Searching.pptx
AI_03_Solving Problems by Searching.pptx
 
(Radhika) presentation on chapter 2 ai
(Radhika) presentation on chapter 2 ai(Radhika) presentation on chapter 2 ai
(Radhika) presentation on chapter 2 ai
 
Lecture 3 Problem Solving.pptx
Lecture 3 Problem Solving.pptxLecture 3 Problem Solving.pptx
Lecture 3 Problem Solving.pptx
 
AI Lesson 04
AI Lesson 04AI Lesson 04
AI Lesson 04
 
Lec#2
Lec#2Lec#2
Lec#2
 
problem solve and resolving in ai domain , probloms
problem solve and resolving in ai domain , problomsproblem solve and resolving in ai domain , probloms
problem solve and resolving in ai domain , probloms
 
AI_Session 3 Problem Solving Agent and searching for solutions.pptx
AI_Session 3 Problem Solving Agent and searching for solutions.pptxAI_Session 3 Problem Solving Agent and searching for solutions.pptx
AI_Session 3 Problem Solving Agent and searching for solutions.pptx
 
CptS 440 / 540 Artificial Intelligence
CptS 440 / 540 Artificial IntelligenceCptS 440 / 540 Artificial Intelligence
CptS 440 / 540 Artificial Intelligence
 
Ai1.pdf
Ai1.pdfAi1.pdf
Ai1.pdf
 
state-spaces29Sep06.ppt
state-spaces29Sep06.pptstate-spaces29Sep06.ppt
state-spaces29Sep06.ppt
 
AI_Lecture2.pptx
AI_Lecture2.pptxAI_Lecture2.pptx
AI_Lecture2.pptx
 
chapter3part1.ppt
chapter3part1.pptchapter3part1.ppt
chapter3part1.ppt
 
artifical intelligence final paper
artifical intelligence final paperartifical intelligence final paper
artifical intelligence final paper
 
AI: AI & Problem Solving
AI: AI & Problem SolvingAI: AI & Problem Solving
AI: AI & Problem Solving
 
AI: AI & problem solving
AI: AI & problem solvingAI: AI & problem solving
AI: AI & problem solving
 
Artificial intelligence(04)
Artificial intelligence(04)Artificial intelligence(04)
Artificial intelligence(04)
 

Más de PalGov

Jarrar.lecture notes.aai.2011s.ch9.fol.inference
Jarrar.lecture notes.aai.2011s.ch9.fol.inferenceJarrar.lecture notes.aai.2011s.ch9.fol.inference
Jarrar.lecture notes.aai.2011s.ch9.fol.inferencePalGov
 
Jarrar.lecture notes.aai.2011s.ch2.intelligentagents
Jarrar.lecture notes.aai.2011s.ch2.intelligentagentsJarrar.lecture notes.aai.2011s.ch2.intelligentagents
Jarrar.lecture notes.aai.2011s.ch2.intelligentagentsPalGov
 
Jarrar.lecture notes.aai.2011s.ontology part4_methodologies
Jarrar.lecture notes.aai.2011s.ontology part4_methodologiesJarrar.lecture notes.aai.2011s.ontology part4_methodologies
Jarrar.lecture notes.aai.2011s.ontology part4_methodologiesPalGov
 
Jarrar.lecture notes.aai.2011s.ontology part3_double-articulation
Jarrar.lecture notes.aai.2011s.ontology part3_double-articulationJarrar.lecture notes.aai.2011s.ontology part3_double-articulation
Jarrar.lecture notes.aai.2011s.ontology part3_double-articulationPalGov
 
Jarrar.lecture notes.aai.2011s.ontology part2_whatisontology
Jarrar.lecture notes.aai.2011s.ontology part2_whatisontologyJarrar.lecture notes.aai.2011s.ontology part2_whatisontology
Jarrar.lecture notes.aai.2011s.ontology part2_whatisontologyPalGov
 
Jarrar.lecture notes.aai.2011s.ontology part1_introduction
Jarrar.lecture notes.aai.2011s.ontology part1_introductionJarrar.lecture notes.aai.2011s.ontology part1_introduction
Jarrar.lecture notes.aai.2011s.ontology part1_introductionPalGov
 
Jarrar.lecture notes.aai.2011s.ch9.fol.inference
Jarrar.lecture notes.aai.2011s.ch9.fol.inferenceJarrar.lecture notes.aai.2011s.ch9.fol.inference
Jarrar.lecture notes.aai.2011s.ch9.fol.inferencePalGov
 
Jarrar.lecture notes.aai.2011s.ch7.p logic
Jarrar.lecture notes.aai.2011s.ch7.p logicJarrar.lecture notes.aai.2011s.ch7.p logic
Jarrar.lecture notes.aai.2011s.ch7.p logicPalGov
 
Jarrar.lecture notes.aai.2011s.ch6.games
Jarrar.lecture notes.aai.2011s.ch6.gamesJarrar.lecture notes.aai.2011s.ch6.games
Jarrar.lecture notes.aai.2011s.ch6.gamesPalGov
 

Más de PalGov (9)

Jarrar.lecture notes.aai.2011s.ch9.fol.inference
Jarrar.lecture notes.aai.2011s.ch9.fol.inferenceJarrar.lecture notes.aai.2011s.ch9.fol.inference
Jarrar.lecture notes.aai.2011s.ch9.fol.inference
 
Jarrar.lecture notes.aai.2011s.ch2.intelligentagents
Jarrar.lecture notes.aai.2011s.ch2.intelligentagentsJarrar.lecture notes.aai.2011s.ch2.intelligentagents
Jarrar.lecture notes.aai.2011s.ch2.intelligentagents
 
Jarrar.lecture notes.aai.2011s.ontology part4_methodologies
Jarrar.lecture notes.aai.2011s.ontology part4_methodologiesJarrar.lecture notes.aai.2011s.ontology part4_methodologies
Jarrar.lecture notes.aai.2011s.ontology part4_methodologies
 
Jarrar.lecture notes.aai.2011s.ontology part3_double-articulation
Jarrar.lecture notes.aai.2011s.ontology part3_double-articulationJarrar.lecture notes.aai.2011s.ontology part3_double-articulation
Jarrar.lecture notes.aai.2011s.ontology part3_double-articulation
 
Jarrar.lecture notes.aai.2011s.ontology part2_whatisontology
Jarrar.lecture notes.aai.2011s.ontology part2_whatisontologyJarrar.lecture notes.aai.2011s.ontology part2_whatisontology
Jarrar.lecture notes.aai.2011s.ontology part2_whatisontology
 
Jarrar.lecture notes.aai.2011s.ontology part1_introduction
Jarrar.lecture notes.aai.2011s.ontology part1_introductionJarrar.lecture notes.aai.2011s.ontology part1_introduction
Jarrar.lecture notes.aai.2011s.ontology part1_introduction
 
Jarrar.lecture notes.aai.2011s.ch9.fol.inference
Jarrar.lecture notes.aai.2011s.ch9.fol.inferenceJarrar.lecture notes.aai.2011s.ch9.fol.inference
Jarrar.lecture notes.aai.2011s.ch9.fol.inference
 
Jarrar.lecture notes.aai.2011s.ch7.p logic
Jarrar.lecture notes.aai.2011s.ch7.p logicJarrar.lecture notes.aai.2011s.ch7.p logic
Jarrar.lecture notes.aai.2011s.ch7.p logic
 
Jarrar.lecture notes.aai.2011s.ch6.games
Jarrar.lecture notes.aai.2011s.ch6.gamesJarrar.lecture notes.aai.2011s.ch6.games
Jarrar.lecture notes.aai.2011s.ch6.games
 

Último

ARTERIAL BLOOD GAS ANALYSIS........pptx
ARTERIAL BLOOD  GAS ANALYSIS........pptxARTERIAL BLOOD  GAS ANALYSIS........pptx
ARTERIAL BLOOD GAS ANALYSIS........pptxAneriPatwari
 
MS4 level being good citizen -imperative- (1) (1).pdf
MS4 level   being good citizen -imperative- (1) (1).pdfMS4 level   being good citizen -imperative- (1) (1).pdf
MS4 level being good citizen -imperative- (1) (1).pdfMr Bounab Samir
 
Beauty Amidst the Bytes_ Unearthing Unexpected Advantages of the Digital Wast...
Beauty Amidst the Bytes_ Unearthing Unexpected Advantages of the Digital Wast...Beauty Amidst the Bytes_ Unearthing Unexpected Advantages of the Digital Wast...
Beauty Amidst the Bytes_ Unearthing Unexpected Advantages of the Digital Wast...DhatriParmar
 
Transaction Management in Database Management System
Transaction Management in Database Management SystemTransaction Management in Database Management System
Transaction Management in Database Management SystemChristalin Nelson
 
Blowin' in the Wind of Caste_ Bob Dylan's Song as a Catalyst for Social Justi...
Blowin' in the Wind of Caste_ Bob Dylan's Song as a Catalyst for Social Justi...Blowin' in the Wind of Caste_ Bob Dylan's Song as a Catalyst for Social Justi...
Blowin' in the Wind of Caste_ Bob Dylan's Song as a Catalyst for Social Justi...DhatriParmar
 
How to Manage Buy 3 Get 1 Free in Odoo 17
How to Manage Buy 3 Get 1 Free in Odoo 17How to Manage Buy 3 Get 1 Free in Odoo 17
How to Manage Buy 3 Get 1 Free in Odoo 17Celine George
 
Unraveling Hypertext_ Analyzing Postmodern Elements in Literature.pptx
Unraveling Hypertext_ Analyzing  Postmodern Elements in  Literature.pptxUnraveling Hypertext_ Analyzing  Postmodern Elements in  Literature.pptx
Unraveling Hypertext_ Analyzing Postmodern Elements in Literature.pptxDhatriParmar
 
Tree View Decoration Attribute in the Odoo 17
Tree View Decoration Attribute in the Odoo 17Tree View Decoration Attribute in the Odoo 17
Tree View Decoration Attribute in the Odoo 17Celine George
 
ICS 2208 Lecture Slide Notes for Topic 6
ICS 2208 Lecture Slide Notes for Topic 6ICS 2208 Lecture Slide Notes for Topic 6
ICS 2208 Lecture Slide Notes for Topic 6Vanessa Camilleri
 
Mythology Quiz-4th April 2024, Quiz Club NITW
Mythology Quiz-4th April 2024, Quiz Club NITWMythology Quiz-4th April 2024, Quiz Club NITW
Mythology Quiz-4th April 2024, Quiz Club NITWQuiz Club NITW
 
4.11.24 Poverty and Inequality in America.pptx
4.11.24 Poverty and Inequality in America.pptx4.11.24 Poverty and Inequality in America.pptx
4.11.24 Poverty and Inequality in America.pptxmary850239
 
Narcotic and Non Narcotic Analgesic..pdf
Narcotic and Non Narcotic Analgesic..pdfNarcotic and Non Narcotic Analgesic..pdf
Narcotic and Non Narcotic Analgesic..pdfPrerana Jadhav
 
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptxDecoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptxDhatriParmar
 
Q-Factor General Quiz-7th April 2024, Quiz Club NITW
Q-Factor General Quiz-7th April 2024, Quiz Club NITWQ-Factor General Quiz-7th April 2024, Quiz Club NITW
Q-Factor General Quiz-7th April 2024, Quiz Club NITWQuiz Club NITW
 
4.16.24 Poverty and Precarity--Desmond.pptx
4.16.24 Poverty and Precarity--Desmond.pptx4.16.24 Poverty and Precarity--Desmond.pptx
4.16.24 Poverty and Precarity--Desmond.pptxmary850239
 
Grade Three -ELLNA-REVIEWER-ENGLISH.pptx
Grade Three -ELLNA-REVIEWER-ENGLISH.pptxGrade Three -ELLNA-REVIEWER-ENGLISH.pptx
Grade Three -ELLNA-REVIEWER-ENGLISH.pptxkarenfajardo43
 
ClimART Action | eTwinning Project
ClimART Action    |    eTwinning ProjectClimART Action    |    eTwinning Project
ClimART Action | eTwinning Projectjordimapav
 
4.9.24 School Desegregation in Boston.pptx
4.9.24 School Desegregation in Boston.pptx4.9.24 School Desegregation in Boston.pptx
4.9.24 School Desegregation in Boston.pptxmary850239
 

Último (20)

ARTERIAL BLOOD GAS ANALYSIS........pptx
ARTERIAL BLOOD  GAS ANALYSIS........pptxARTERIAL BLOOD  GAS ANALYSIS........pptx
ARTERIAL BLOOD GAS ANALYSIS........pptx
 
MS4 level being good citizen -imperative- (1) (1).pdf
MS4 level   being good citizen -imperative- (1) (1).pdfMS4 level   being good citizen -imperative- (1) (1).pdf
MS4 level being good citizen -imperative- (1) (1).pdf
 
Beauty Amidst the Bytes_ Unearthing Unexpected Advantages of the Digital Wast...
Beauty Amidst the Bytes_ Unearthing Unexpected Advantages of the Digital Wast...Beauty Amidst the Bytes_ Unearthing Unexpected Advantages of the Digital Wast...
Beauty Amidst the Bytes_ Unearthing Unexpected Advantages of the Digital Wast...
 
Transaction Management in Database Management System
Transaction Management in Database Management SystemTransaction Management in Database Management System
Transaction Management in Database Management System
 
Blowin' in the Wind of Caste_ Bob Dylan's Song as a Catalyst for Social Justi...
Blowin' in the Wind of Caste_ Bob Dylan's Song as a Catalyst for Social Justi...Blowin' in the Wind of Caste_ Bob Dylan's Song as a Catalyst for Social Justi...
Blowin' in the Wind of Caste_ Bob Dylan's Song as a Catalyst for Social Justi...
 
How to Manage Buy 3 Get 1 Free in Odoo 17
How to Manage Buy 3 Get 1 Free in Odoo 17How to Manage Buy 3 Get 1 Free in Odoo 17
How to Manage Buy 3 Get 1 Free in Odoo 17
 
Unraveling Hypertext_ Analyzing Postmodern Elements in Literature.pptx
Unraveling Hypertext_ Analyzing  Postmodern Elements in  Literature.pptxUnraveling Hypertext_ Analyzing  Postmodern Elements in  Literature.pptx
Unraveling Hypertext_ Analyzing Postmodern Elements in Literature.pptx
 
Tree View Decoration Attribute in the Odoo 17
Tree View Decoration Attribute in the Odoo 17Tree View Decoration Attribute in the Odoo 17
Tree View Decoration Attribute in the Odoo 17
 
ICS 2208 Lecture Slide Notes for Topic 6
ICS 2208 Lecture Slide Notes for Topic 6ICS 2208 Lecture Slide Notes for Topic 6
ICS 2208 Lecture Slide Notes for Topic 6
 
Mythology Quiz-4th April 2024, Quiz Club NITW
Mythology Quiz-4th April 2024, Quiz Club NITWMythology Quiz-4th April 2024, Quiz Club NITW
Mythology Quiz-4th April 2024, Quiz Club NITW
 
4.11.24 Poverty and Inequality in America.pptx
4.11.24 Poverty and Inequality in America.pptx4.11.24 Poverty and Inequality in America.pptx
4.11.24 Poverty and Inequality in America.pptx
 
Narcotic and Non Narcotic Analgesic..pdf
Narcotic and Non Narcotic Analgesic..pdfNarcotic and Non Narcotic Analgesic..pdf
Narcotic and Non Narcotic Analgesic..pdf
 
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptxDecoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
 
Faculty Profile prashantha K EEE dept Sri Sairam college of Engineering
Faculty Profile prashantha K EEE dept Sri Sairam college of EngineeringFaculty Profile prashantha K EEE dept Sri Sairam college of Engineering
Faculty Profile prashantha K EEE dept Sri Sairam college of Engineering
 
Q-Factor General Quiz-7th April 2024, Quiz Club NITW
Q-Factor General Quiz-7th April 2024, Quiz Club NITWQ-Factor General Quiz-7th April 2024, Quiz Club NITW
Q-Factor General Quiz-7th April 2024, Quiz Club NITW
 
INCLUSIVE EDUCATION PRACTICES FOR TEACHERS AND TRAINERS.pptx
INCLUSIVE EDUCATION PRACTICES FOR TEACHERS AND TRAINERS.pptxINCLUSIVE EDUCATION PRACTICES FOR TEACHERS AND TRAINERS.pptx
INCLUSIVE EDUCATION PRACTICES FOR TEACHERS AND TRAINERS.pptx
 
4.16.24 Poverty and Precarity--Desmond.pptx
4.16.24 Poverty and Precarity--Desmond.pptx4.16.24 Poverty and Precarity--Desmond.pptx
4.16.24 Poverty and Precarity--Desmond.pptx
 
Grade Three -ELLNA-REVIEWER-ENGLISH.pptx
Grade Three -ELLNA-REVIEWER-ENGLISH.pptxGrade Three -ELLNA-REVIEWER-ENGLISH.pptx
Grade Three -ELLNA-REVIEWER-ENGLISH.pptx
 
ClimART Action | eTwinning Project
ClimART Action    |    eTwinning ProjectClimART Action    |    eTwinning Project
ClimART Action | eTwinning Project
 
4.9.24 School Desegregation in Boston.pptx
4.9.24 School Desegregation in Boston.pptx4.9.24 School Desegregation in Boston.pptx
4.9.24 School Desegregation in Boston.pptx
 

Jarrar.lecture notes.aai.2011s.ch3.uniformedsearch

  • 1. Dr. Mustafa Jarrar [email_address] University of Birzeit Chapter 3 Uni-Informed Search Advanced Artificial Intelligence (SCOM7341) Lecture Notes, Advanced Artificial Intelligence (SCOM7341) University of Birzeit 2 nd Semester, 2011
  • 2. Example: Romania You are in Arad and want to go to Bucharest  How to design an intelligent agent to find the way between 2 cities?
  • 3. Example: The 8-puzzle  How can we design an intelligent agent to solve the 8-puzzel?
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20. Discussion Who can give examples of tree search strategies? Why a search strategy is better than another?
  • 21. Example: Breadth First Search S3 5 A4 D3 1 1 3 3 4 2 C2 D3 G0 G0 G0 E1 G0 1 1 3 3 4 2 C2 D3 G0 G0 E1 G0 1 3 B2 1 3 C2 D3 G0 G0 E1 G0 1 3 4 E1 G0 2 4 3 2 4 S3 A4 C2 D3 E1 B2 G0 1 1 1 3 1 3 3 4 5 1 2
  • 22. Example: Greedy Search S3 5 A4 D3 1 1 3 3 4 2 C2 D3 G0 G0 G0 E1 G0 1 1 3 3 4 2 C2 D3 G0 G0 E1 G0 1 3 B2 1 3 C2 D3 G0 G0 E1 G0 1 3 4 E1 G0 2 4 3 2 4 S3 A4 C2 D3 E1 B2 G0 1 1 1 3 1 3 3 4 5 1 2
  • 23. Example: A* Search S3 5 A4 D3 1 1 3 3 4 2 C2 D3 G0 G0 G0 E1 G0 1 1 3 3 4 2 C2 D3 G0 G0 E1 G0 1 3 B2 1 3 C2 D3 G0 G0 E1 G0 1 3 4 E1 G0 2 4 3 2 4 S3 A4 C2 D3 E1 B2 G0 1 1 1 3 1 3 3 4 5 1 2
  • 24.
  • 25.
  • 26.
  • 28.
  • 29. Breadth-First Snapshot 1 Initial Visited Fringe Current Visible Goal 1 2 3 Fringe: [] + [2,3]
  • 30. Breadth-First Snapshot 2 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 Fringe: [3] + [4,5]
  • 31. Breadth-First Snapshot 3 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 Fringe: [4,5] + [6,7]
  • 32. Breadth-First Snapshot 4 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 Fringe: [5,6,7] + [8,9]
  • 33. Breadth-First Snapshot 5 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 Fringe: [6,7,8,9] + [10,11]
  • 34. Breadth-First Snapshot 6 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 Fringe: [7,8,9,10,11] + [12,13]
  • 35. Breadth-First Snapshot 7 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Fringe: [8,9.10,11,12,13] + [14,15]
  • 36. Breadth-First Snapshot 8 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Fringe: [9,10,11,12,13,14,15] + [16,17]
  • 37. Breadth-First Snapshot 9 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Fringe: [10,11,12,13,14,15,16,17] + [18,19]
  • 38. Breadth-First Snapshot 10 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Fringe: [11,12,13,14,15,16,17,18,19] + [20,21]
  • 39. Breadth-First Snapshot 11 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Fringe: [12, 13, 14, 15, 16, 17, 18, 19, 20, 21] + [22,23]
  • 40. Breadth-First Snapshot 12 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Fringe: [13,14,15,16,17,18,19,20,21] + [22,23] Note: The goal node is “visible” here, but we can not perform the goal test yet.
  • 41. Breadth-First Snapshot 13 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Fringe: [14,15,16,17,18,19,20,21,22,23,24,25] + [26,27]
  • 42. Breadth-First Snapshot 14 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Fringe: [15,16,17,18,19,20,21,22,23,24,25,26,27] + [28,29]
  • 43. Breadth-First Snapshot 15 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fringe: [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29] + [30,31]
  • 44. Breadth-First Snapshot 16 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fringe: [17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]
  • 45. Breadth-First Snapshot 17 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fringe: [18,19,20,21,22,23,24,25,26,27,28,29,30,31]
  • 46. Breadth-First Snapshot 18 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fringe: [19,20,21,22,23,24,25,26,27,28,29,30,31]
  • 47. Breadth-First Snapshot 19 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fringe: [20,21,22,23,24,25,26,27,28,29,30,31]
  • 48. Breadth-First Snapshot 20 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fringe: [21,22,23,24,25,26,27,28,29,30,31]
  • 49. Breadth-First Snapshot 21 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fringe: [22,23,24,25,26,27,28,29,30,31]
  • 50. Breadth-First Snapshot 22 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fringe: [23,24,25,26,27,28,29,30,31]
  • 51. Breadth-First Snapshot 23 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fringe: [24,25,26,27,28,29,30,31]
  • 52. Breadth-First Snapshot 24 Initial Visited Fringe Current Visible Goal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fringe: [25,26,27,28,29,30,31] Note: The goal test is positive for this node, and a solution is found in 24 steps.
  • 53.
  • 55.
  • 64.
  • 65.
  • 67.
  • 68.
  • 69.
  • 70.
  • 71.
  • 72.
  • 73. 5- Iterative Deepening Depth-First Search
  • 74.
  • 79.
  • 80.
  • 81.
  • 82.
  • 83.
  • 84.
  • 85.
  • 86.
  • 87.

Notas del editor

  1. anecdote, demonstration, example to informally introduce the topic evoke the participants’ interest and curiosity set the stage for the more formal introduction make students more comfortable