SlideShare ist ein Scribd-Unternehmen logo
1 von 36
Planning Chapter 11.1-11.3
Planning problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Planning vs. problem solving ,[object Object],[object Object],[object Object],[object Object],[object Object]
Typical assumptions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Blocks world ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],A B C TABLE
Major approaches ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Basic representations for planning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Operator/action representation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Go(there) At(here) ,Path(here,there) At(there) , ~At(here)
Blocks world operators ,[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]
Blocks world operators II ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],operator(unstack(X,Y),  [on(X,Y), clear(X), handempty], [holding(X),clear(Y)], [handempty,clear(X),on(X,Y)], [X=Y,Y=table,X=table]). operator(putdown(X),  [holding(X)], [ontable(X),handempty,clear(X)], [holding(X)], [X=table]).
STRIPS planning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Typical BW planning problem ,[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],A B C A B C
Another BW planning problem ,[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],[object Object],A B C A B C
Goal interaction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Initial state A B C A B C Goal state
Sussman Anomaly Initial state Goal state Achieve on(a,b) via stack(a,b) with preconds: [holding(a),clear(b)] |Achieve holding(a) via pickup(a) with preconds: [ontable(a),clear(a),handempty] ||Achieve clear(a) via unstack(_1584,a) with preconds: [on(_1584,a),clear(_1584),handempty] ||Applying unstack(c,a)  ||Achieve handempty via putdown(_2691) with preconds: [holding(_2691)] ||Applying putdown(c)  |Applying pickup(a)  Applying stack(a,b)  Achieve on(b,c) via stack(b,c) with preconds: [holding(b),clear(c)] |Achieve holding(b) via pickup(b) with preconds: [ontable(b),clear(b),handempty] ||Achieve clear(b) via unstack(_5625,b) with preconds: [on(_5625,b),clear(_5625),handempty] ||Applying unstack(a,b)  ||Achieve handempty via putdown(_6648) with preconds: [holding(_6648)] ||Applying putdown(a)  |Applying pickup(b)  Applying stack(b,c)  Achieve on(a,b) via stack(a,b) with preconds: [holding(a),clear(b)] |Achieve holding(a) via pickup(a) with preconds: [ontable(a),clear(a),handempty] |Applying pickup(a)  Applying stack(a,b)  From [clear(b),clear(c),ontable(a),ontable(b),on(c,a),handempty] To [on(a,b),on(b,c),ontable(c)] Do: unstack(c,a) putdown(c) pickup(a) stack(a,b) unstack(a,b) putdown(a) pickup(b) stack(b,c) pickup(a) stack(a,b) A B C A B C
State-space planning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Plan-space planning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Partial-order planning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Least commitment ,[object Object],[object Object],[object Object],[object Object],[object Object]
Non-linear plan ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The initial plan ,[object Object],S1:Start S2:Finish Initial  State Goal  State
Trivial example ,[object Object],[object Object],[object Object],[object Object],[object Object],Steps: {S1:[Op(Action:Start)], S2:[Op(Action:Finish,   Pre: RightShoeOn^LeftShoeOn)]} Links: {} Orderings: {S1<S2} S1:Start S2:Finish RightShoeOn  ^ LeftShoeOn
Solution Start Left Sock Right Sock Right Shoe Left Shoe Finish
POP constraints and search heuristics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],c
 
Partial-order planning example ,[object Object]
 
 
 
 
Resolving threats Threat Demotion Promotion
 
 
http://www.cs.ubc.ca/labs/lci/CIspace/ Applets to illustrate graph search, CSP, decision trees, belief networks, neural networks, deductive reasoning, planning and robot control.  You can run them via the web or download them to your own computer. The planning applet uses the STRIPS representation to demonstrate the STRIPS, regression, and partial order planners.
 
 

Weitere ähnliche Inhalte

Was ist angesagt?

Artificial Intelligence 1 Planning In The Real World
Artificial Intelligence 1 Planning In The Real WorldArtificial Intelligence 1 Planning In The Real World
Artificial Intelligence 1 Planning In The Real Worldahmad bassiouny
 
Unit3:Informed and Uninformed search
Unit3:Informed and Uninformed searchUnit3:Informed and Uninformed search
Unit3:Informed and Uninformed searchTekendra Nath Yogi
 
Knowledge Representation & Reasoning
Knowledge Representation & ReasoningKnowledge Representation & Reasoning
Knowledge Representation & ReasoningSajid Marwat
 
Heuristc Search Techniques
Heuristc Search TechniquesHeuristc Search Techniques
Heuristc Search TechniquesJismy .K.Jose
 
Artificial Intelligence (AI) | Prepositional logic (PL)and first order predic...
Artificial Intelligence (AI) | Prepositional logic (PL)and first order predic...Artificial Intelligence (AI) | Prepositional logic (PL)and first order predic...
Artificial Intelligence (AI) | Prepositional logic (PL)and first order predic...Ashish Duggal
 
Artificial Intelligence Notes Unit 1
Artificial Intelligence Notes Unit 1 Artificial Intelligence Notes Unit 1
Artificial Intelligence Notes Unit 1 DigiGurukul
 
Agents in Artificial intelligence
Agents in Artificial intelligence Agents in Artificial intelligence
Agents in Artificial intelligence Lalit Birla
 
I. AO* SEARCH ALGORITHM
I. AO* SEARCH ALGORITHMI. AO* SEARCH ALGORITHM
I. AO* SEARCH ALGORITHMvikas dhakane
 
Problem solving agents
Problem solving agentsProblem solving agents
Problem solving agentsMegha Sharma
 
Unification and Lifting
Unification and LiftingUnification and Lifting
Unification and LiftingMegha Sharma
 
Lecture 2 agent and environment
Lecture 2   agent and environmentLecture 2   agent and environment
Lecture 2 agent and environmentVajira Thambawita
 
I.BEST FIRST SEARCH IN AI
I.BEST FIRST SEARCH IN AII.BEST FIRST SEARCH IN AI
I.BEST FIRST SEARCH IN AIvikas dhakane
 
First Order Logic resolution
First Order Logic resolutionFirst Order Logic resolution
First Order Logic resolutionAmar Jukuntla
 

Was ist angesagt? (20)

Artificial Intelligence 1 Planning In The Real World
Artificial Intelligence 1 Planning In The Real WorldArtificial Intelligence 1 Planning In The Real World
Artificial Intelligence 1 Planning In The Real World
 
Unit3:Informed and Uninformed search
Unit3:Informed and Uninformed searchUnit3:Informed and Uninformed search
Unit3:Informed and Uninformed search
 
Knowledge Representation & Reasoning
Knowledge Representation & ReasoningKnowledge Representation & Reasoning
Knowledge Representation & Reasoning
 
Heuristc Search Techniques
Heuristc Search TechniquesHeuristc Search Techniques
Heuristc Search Techniques
 
AI_Planning.pdf
AI_Planning.pdfAI_Planning.pdf
AI_Planning.pdf
 
Problems, Problem spaces and Search
Problems, Problem spaces and SearchProblems, Problem spaces and Search
Problems, Problem spaces and Search
 
AI Lecture 3 (solving problems by searching)
AI Lecture 3 (solving problems by searching)AI Lecture 3 (solving problems by searching)
AI Lecture 3 (solving problems by searching)
 
Recognition-of-tokens
Recognition-of-tokensRecognition-of-tokens
Recognition-of-tokens
 
Artificial Intelligence (AI) | Prepositional logic (PL)and first order predic...
Artificial Intelligence (AI) | Prepositional logic (PL)and first order predic...Artificial Intelligence (AI) | Prepositional logic (PL)and first order predic...
Artificial Intelligence (AI) | Prepositional logic (PL)and first order predic...
 
Artificial Intelligence Notes Unit 1
Artificial Intelligence Notes Unit 1 Artificial Intelligence Notes Unit 1
Artificial Intelligence Notes Unit 1
 
Agents in Artificial intelligence
Agents in Artificial intelligence Agents in Artificial intelligence
Agents in Artificial intelligence
 
I. AO* SEARCH ALGORITHM
I. AO* SEARCH ALGORITHMI. AO* SEARCH ALGORITHM
I. AO* SEARCH ALGORITHM
 
AI: Planning and AI
AI: Planning and AIAI: Planning and AI
AI: Planning and AI
 
Problem solving agents
Problem solving agentsProblem solving agents
Problem solving agents
 
Unification and Lifting
Unification and LiftingUnification and Lifting
Unification and Lifting
 
First order logic
First order logicFirst order logic
First order logic
 
Lecture 2 agent and environment
Lecture 2   agent and environmentLecture 2   agent and environment
Lecture 2 agent and environment
 
I.BEST FIRST SEARCH IN AI
I.BEST FIRST SEARCH IN AII.BEST FIRST SEARCH IN AI
I.BEST FIRST SEARCH IN AI
 
First order logic
First order logicFirst order logic
First order logic
 
First Order Logic resolution
First Order Logic resolutionFirst Order Logic resolution
First Order Logic resolution
 

Andere mochten auch

Informed and Uninformed search Strategies
Informed and Uninformed search StrategiesInformed and Uninformed search Strategies
Informed and Uninformed search StrategiesAmey Kerkar
 
Nonparametric statistics
Nonparametric statisticsNonparametric statistics
Nonparametric statisticsTarun Gehlot
 
non parametric statistics
non parametric statisticsnon parametric statistics
non parametric statisticsAnchal Garg
 
2-Agents- Artificial Intelligence
2-Agents- Artificial Intelligence2-Agents- Artificial Intelligence
2-Agents- Artificial IntelligenceMhd Sb
 
Artificial Intelligence
Artificial IntelligenceArtificial Intelligence
Artificial Intelligenceu053675
 
Artificial Intelligence Presentation
Artificial Intelligence PresentationArtificial Intelligence Presentation
Artificial Intelligence Presentationlpaviglianiti
 

Andere mochten auch (17)

Planning Algorithms
Planning AlgorithmsPlanning Algorithms
Planning Algorithms
 
Planing presentation
Planing presentationPlaning presentation
Planing presentation
 
Alpha beta prouning
Alpha beta prouningAlpha beta prouning
Alpha beta prouning
 
Minimax
MinimaxMinimax
Minimax
 
Informed and Uninformed search Strategies
Informed and Uninformed search StrategiesInformed and Uninformed search Strategies
Informed and Uninformed search Strategies
 
Nonparametric statistics
Nonparametric statisticsNonparametric statistics
Nonparametric statistics
 
non parametric statistics
non parametric statisticsnon parametric statistics
non parametric statistics
 
Intelligent agent
Intelligent agentIntelligent agent
Intelligent agent
 
AI: AI & Problem Solving
AI: AI & Problem SolvingAI: AI & Problem Solving
AI: AI & Problem Solving
 
2-Agents- Artificial Intelligence
2-Agents- Artificial Intelligence2-Agents- Artificial Intelligence
2-Agents- Artificial Intelligence
 
AI: Learning in AI
AI: Learning in AI AI: Learning in AI
AI: Learning in AI
 
Non-Parametric Tests
Non-Parametric TestsNon-Parametric Tests
Non-Parametric Tests
 
Ai Slides
Ai SlidesAi Slides
Ai Slides
 
Natural Language Processing
Natural Language ProcessingNatural Language Processing
Natural Language Processing
 
Non parametric tests
Non parametric testsNon parametric tests
Non parametric tests
 
Artificial Intelligence
Artificial IntelligenceArtificial Intelligence
Artificial Intelligence
 
Artificial Intelligence Presentation
Artificial Intelligence PresentationArtificial Intelligence Presentation
Artificial Intelligence Presentation
 

Ähnlich wie Planning

Goal stack planning.ppt
Goal stack planning.pptGoal stack planning.ppt
Goal stack planning.pptSadagopanS
 
21CSC206T_UNIT 5.pptx artificial intelligence
21CSC206T_UNIT 5.pptx artificial intelligence21CSC206T_UNIT 5.pptx artificial intelligence
21CSC206T_UNIT 5.pptx artificial intelligenceANTOARA2211003040050
 
Cs221 logic-planning
Cs221 logic-planningCs221 logic-planning
Cs221 logic-planningdarwinrlo
 
Cs221 lecture7-fall11
Cs221 lecture7-fall11Cs221 lecture7-fall11
Cs221 lecture7-fall11darwinrlo
 
Intro to AI STRIPS Planning & Applications in Video-games Lecture2-Part2
Intro to AI STRIPS Planning & Applications in Video-games Lecture2-Part2Intro to AI STRIPS Planning & Applications in Video-games Lecture2-Part2
Intro to AI STRIPS Planning & Applications in Video-games Lecture2-Part2Stavros Vassos
 
CS1017-unitIV.pdf
CS1017-unitIV.pdfCS1017-unitIV.pdf
CS1017-unitIV.pdfSaswatSeth
 
cs344-lect17-robotic-planning-25feb08.ppt
cs344-lect17-robotic-planning-25feb08.pptcs344-lect17-robotic-planning-25feb08.ppt
cs344-lect17-robotic-planning-25feb08.pptRaviShankarPratapDeo1
 
Dsoop (co 221) 1
Dsoop (co 221) 1Dsoop (co 221) 1
Dsoop (co 221) 1Puja Koch
 
Intro to AI STRIPS Planning & Applications in Video-games Lecture3-Part1
Intro to AI STRIPS Planning & Applications in Video-games Lecture3-Part1Intro to AI STRIPS Planning & Applications in Video-games Lecture3-Part1
Intro to AI STRIPS Planning & Applications in Video-games Lecture3-Part1Stavros Vassos
 

Ähnlich wie Planning (20)

Goal stack planning.ppt
Goal stack planning.pptGoal stack planning.ppt
Goal stack planning.ppt
 
21CSC206T_UNIT 5.pptx artificial intelligence
21CSC206T_UNIT 5.pptx artificial intelligence21CSC206T_UNIT 5.pptx artificial intelligence
21CSC206T_UNIT 5.pptx artificial intelligence
 
Cs221 logic-planning
Cs221 logic-planningCs221 logic-planning
Cs221 logic-planning
 
Cs221 lecture7-fall11
Cs221 lecture7-fall11Cs221 lecture7-fall11
Cs221 lecture7-fall11
 
Lesson 23
Lesson 23Lesson 23
Lesson 23
 
AI Lesson 23
AI Lesson 23AI Lesson 23
AI Lesson 23
 
Slides15
Slides15Slides15
Slides15
 
Intro to AI STRIPS Planning & Applications in Video-games Lecture2-Part2
Intro to AI STRIPS Planning & Applications in Video-games Lecture2-Part2Intro to AI STRIPS Planning & Applications in Video-games Lecture2-Part2
Intro to AI STRIPS Planning & Applications in Video-games Lecture2-Part2
 
RPT_AI-06_A_Planning Intro.ppt
RPT_AI-06_A_Planning Intro.pptRPT_AI-06_A_Planning Intro.ppt
RPT_AI-06_A_Planning Intro.ppt
 
22 planning
22 planning22 planning
22 planning
 
CS1017-unitIV.pdf
CS1017-unitIV.pdfCS1017-unitIV.pdf
CS1017-unitIV.pdf
 
AI_unit IV Full Notes.pdf
AI_unit IV Full Notes.pdfAI_unit IV Full Notes.pdf
AI_unit IV Full Notes.pdf
 
cs344-lect17-robotic-planning-25feb08.ppt
cs344-lect17-robotic-planning-25feb08.pptcs344-lect17-robotic-planning-25feb08.ppt
cs344-lect17-robotic-planning-25feb08.ppt
 
Scheduling And Htn
Scheduling And HtnScheduling And Htn
Scheduling And Htn
 
Planning Agent
Planning AgentPlanning Agent
Planning Agent
 
Lesson 24
Lesson 24Lesson 24
Lesson 24
 
AI Lesson 24
AI Lesson 24AI Lesson 24
AI Lesson 24
 
Dsoop (co 221) 1
Dsoop (co 221) 1Dsoop (co 221) 1
Dsoop (co 221) 1
 
Intro to AI STRIPS Planning & Applications in Video-games Lecture3-Part1
Intro to AI STRIPS Planning & Applications in Video-games Lecture3-Part1Intro to AI STRIPS Planning & Applications in Video-games Lecture3-Part1
Intro to AI STRIPS Planning & Applications in Video-games Lecture3-Part1
 
lecture12.pdf
lecture12.pdflecture12.pdf
lecture12.pdf
 

Mehr von ahmad bassiouny (20)

Work Study & Productivity
Work Study & ProductivityWork Study & Productivity
Work Study & Productivity
 
Work Study
Work StudyWork Study
Work Study
 
Motion And Time Study
Motion And Time StudyMotion And Time Study
Motion And Time Study
 
Motion Study
Motion StudyMotion Study
Motion Study
 
The Christmas Story
The Christmas StoryThe Christmas Story
The Christmas Story
 
Turkey Photos
Turkey PhotosTurkey Photos
Turkey Photos
 
Mission Bo Kv3
Mission Bo Kv3Mission Bo Kv3
Mission Bo Kv3
 
Miramar
MiramarMiramar
Miramar
 
Mom
MomMom
Mom
 
Linearization
LinearizationLinearization
Linearization
 
Kblmt B000 Intro Kaizen Based Lean Manufacturing
Kblmt B000 Intro Kaizen Based Lean ManufacturingKblmt B000 Intro Kaizen Based Lean Manufacturing
Kblmt B000 Intro Kaizen Based Lean Manufacturing
 
How To Survive
How To SurviveHow To Survive
How To Survive
 
Dad
DadDad
Dad
 
Ancient Hieroglyphics
Ancient HieroglyphicsAncient Hieroglyphics
Ancient Hieroglyphics
 
Dubai In 2009
Dubai In 2009Dubai In 2009
Dubai In 2009
 
DesignPeopleSystem
DesignPeopleSystemDesignPeopleSystem
DesignPeopleSystem
 
Organizational Behavior
Organizational BehaviorOrganizational Behavior
Organizational Behavior
 
Work Study Workshop
Work Study WorkshopWork Study Workshop
Work Study Workshop
 
Workstudy
WorkstudyWorkstudy
Workstudy
 
Time And Motion Study
Time And  Motion  StudyTime And  Motion  Study
Time And Motion Study
 

Kürzlich hochgeladen

Application orientated numerical on hev.ppt
Application orientated numerical on hev.pptApplication orientated numerical on hev.ppt
Application orientated numerical on hev.pptRamjanShidvankar
 
Measures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeMeasures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeThiyagu K
 
How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17Celine George
 
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfciinovamais
 
Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Celine George
 
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptxSOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptxiammrhaywood
 
Making and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdfMaking and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdfChris Hunter
 
Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptxBasic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptxDenish Jangid
 
An Overview of Mutual Funds Bcom Project.pdf
An Overview of Mutual Funds Bcom Project.pdfAn Overview of Mutual Funds Bcom Project.pdf
An Overview of Mutual Funds Bcom Project.pdfSanaAli374401
 
Unit-V; Pricing (Pharma Marketing Management).pptx
Unit-V; Pricing (Pharma Marketing Management).pptxUnit-V; Pricing (Pharma Marketing Management).pptx
Unit-V; Pricing (Pharma Marketing Management).pptxVishalSingh1417
 
This PowerPoint helps students to consider the concept of infinity.
This PowerPoint helps students to consider the concept of infinity.This PowerPoint helps students to consider the concept of infinity.
This PowerPoint helps students to consider the concept of infinity.christianmathematics
 
microwave assisted reaction. General introduction
microwave assisted reaction. General introductionmicrowave assisted reaction. General introduction
microwave assisted reaction. General introductionMaksud Ahmed
 
Seal of Good Local Governance (SGLG) 2024Final.pptx
Seal of Good Local Governance (SGLG) 2024Final.pptxSeal of Good Local Governance (SGLG) 2024Final.pptx
Seal of Good Local Governance (SGLG) 2024Final.pptxnegromaestrong
 
fourth grading exam for kindergarten in writing
fourth grading exam for kindergarten in writingfourth grading exam for kindergarten in writing
fourth grading exam for kindergarten in writingTeacherCyreneCayanan
 
Accessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactAccessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactdawncurless
 
ICT Role in 21st Century Education & its Challenges.pptx
ICT Role in 21st Century Education & its Challenges.pptxICT Role in 21st Century Education & its Challenges.pptx
ICT Role in 21st Century Education & its Challenges.pptxAreebaZafar22
 
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...christianmathematics
 
APM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across SectorsAPM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across SectorsAssociation for Project Management
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxheathfieldcps1
 

Kürzlich hochgeladen (20)

Application orientated numerical on hev.ppt
Application orientated numerical on hev.pptApplication orientated numerical on hev.ppt
Application orientated numerical on hev.ppt
 
Measures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeMeasures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and Mode
 
How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17
 
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdf
 
Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17
 
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptxSOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
 
Making and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdfMaking and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdf
 
Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptxBasic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
 
An Overview of Mutual Funds Bcom Project.pdf
An Overview of Mutual Funds Bcom Project.pdfAn Overview of Mutual Funds Bcom Project.pdf
An Overview of Mutual Funds Bcom Project.pdf
 
Unit-V; Pricing (Pharma Marketing Management).pptx
Unit-V; Pricing (Pharma Marketing Management).pptxUnit-V; Pricing (Pharma Marketing Management).pptx
Unit-V; Pricing (Pharma Marketing Management).pptx
 
This PowerPoint helps students to consider the concept of infinity.
This PowerPoint helps students to consider the concept of infinity.This PowerPoint helps students to consider the concept of infinity.
This PowerPoint helps students to consider the concept of infinity.
 
microwave assisted reaction. General introduction
microwave assisted reaction. General introductionmicrowave assisted reaction. General introduction
microwave assisted reaction. General introduction
 
Seal of Good Local Governance (SGLG) 2024Final.pptx
Seal of Good Local Governance (SGLG) 2024Final.pptxSeal of Good Local Governance (SGLG) 2024Final.pptx
Seal of Good Local Governance (SGLG) 2024Final.pptx
 
fourth grading exam for kindergarten in writing
fourth grading exam for kindergarten in writingfourth grading exam for kindergarten in writing
fourth grading exam for kindergarten in writing
 
Accessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactAccessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impact
 
ICT Role in 21st Century Education & its Challenges.pptx
ICT Role in 21st Century Education & its Challenges.pptxICT Role in 21st Century Education & its Challenges.pptx
ICT Role in 21st Century Education & its Challenges.pptx
 
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
 
APM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across SectorsAPM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across Sectors
 
Advance Mobile Application Development class 07
Advance Mobile Application Development class 07Advance Mobile Application Development class 07
Advance Mobile Application Development class 07
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptx
 

Planning

  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15. Sussman Anomaly Initial state Goal state Achieve on(a,b) via stack(a,b) with preconds: [holding(a),clear(b)] |Achieve holding(a) via pickup(a) with preconds: [ontable(a),clear(a),handempty] ||Achieve clear(a) via unstack(_1584,a) with preconds: [on(_1584,a),clear(_1584),handempty] ||Applying unstack(c,a) ||Achieve handempty via putdown(_2691) with preconds: [holding(_2691)] ||Applying putdown(c) |Applying pickup(a) Applying stack(a,b) Achieve on(b,c) via stack(b,c) with preconds: [holding(b),clear(c)] |Achieve holding(b) via pickup(b) with preconds: [ontable(b),clear(b),handempty] ||Achieve clear(b) via unstack(_5625,b) with preconds: [on(_5625,b),clear(_5625),handempty] ||Applying unstack(a,b) ||Achieve handempty via putdown(_6648) with preconds: [holding(_6648)] ||Applying putdown(a) |Applying pickup(b) Applying stack(b,c) Achieve on(a,b) via stack(a,b) with preconds: [holding(a),clear(b)] |Achieve holding(a) via pickup(a) with preconds: [ontable(a),clear(a),handempty] |Applying pickup(a) Applying stack(a,b) From [clear(b),clear(c),ontable(a),ontable(b),on(c,a),handempty] To [on(a,b),on(b,c),ontable(c)] Do: unstack(c,a) putdown(c) pickup(a) stack(a,b) unstack(a,b) putdown(a) pickup(b) stack(b,c) pickup(a) stack(a,b) A B C A B C
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23. Solution Start Left Sock Right Sock Right Shoe Left Shoe Finish
  • 24.
  • 25.  
  • 26.
  • 27.  
  • 28.  
  • 29.  
  • 30.  
  • 31. Resolving threats Threat Demotion Promotion
  • 32.  
  • 33.  
  • 34. http://www.cs.ubc.ca/labs/lci/CIspace/ Applets to illustrate graph search, CSP, decision trees, belief networks, neural networks, deductive reasoning, planning and robot control. You can run them via the web or download them to your own computer. The planning applet uses the STRIPS representation to demonstrate the STRIPS, regression, and partial order planners.
  • 35.  
  • 36.