SlideShare a Scribd company logo
1 of 11
Knowledge-based agent
18
Propositional Logic
Subscribe
Knowledge-Based (Logical) Agents
Knowledge-based agents can accept new
tasks in the form of explicitly described
goals; they can achieve competence quickly
by being told or learning new knowledge
about the environment; and they can adapt
to changes in the environment by updating
the relevant knowledge.
Components
Knowledge Base
• A knowledge base is a set of sentence. Each sentence is
expressed in a language called a knowledge representation
language and represents some assertion about the world.
Inference Engine
• Inference engine allows us to add a new sentence to the knowledge base. A sentence is a
proposition about the world. Inference system applies logical rules to the KB to deduce new
information.
Subscribe
Operations performed by K.B.A.
 A Knowledge Base keeps track of things.
 We can TELL it facts or ASK for inference.
TELL: This operation tells the knowledge base what it perceives from the
environment.
ASK: This operation asks the knowledge base what action it should
perform.
Example:
TELL: Father of John is Robbin.
TELL: Marry is John’s sister.
TELL: John’s father is the same as John’s sister’s father.
ASK: Who is Marry’s father?
Subscribe
Each time when the function is called, it does three things.
It TELLs the KB what it perceives.
It ASKs KB what action it should take
Agent program TELLS the KB that which action was chosen.
Subscribe
Logic
 Knowledge bases consist of sentences.
 These sentences are expressed according to the
syntax of the representation language , which
specifies all the sentences that are well formed.
 x+y=2 (well formed)
 x2y=+ (not well formed)
 A logic must also defines the semantics or
meaning of sentences.
 The semantics defines the TRUTH of each
sentence with respect to each possible
world.(model)
 x+ y=4 is true in a world where x=2 and y=2
But false in a world where x is 1 and y is 1
Subscribe
Propositional Logic
Subscribe
 The Syntax of propositional logic defines the allowable
sentences.
The atomic sentences consist of a single proposition
symbol Each such symbol stands for a proposition that
can be true or false.
Example: Sunday is Holiday. (true proposition)
2+1=4 (False proposition)
some boys like to play cricket.(Not a propositional logic)
 Complex sentences are constructed from simpler
sentences, using parentheses and logical connective.
Connectives
Symbol Name Meaning
¬ (Not)
Negation
If P is true , ¬P will be false and vice
versa.
^ (and)
Conjunction
(P^Q) is true if both P and Q are true
otherwise false.
v (or)
Disjunction
(P v Q) is true if either P or Q is true (or
both) otherwise false.
→ implies If P happens then Q happens.
↔ Double
implication
P happens if and only if Q happens.
Subscribe
Examples:
1.You can use the college library only if you are a
student or are a faculty.
P(Q v R)
2. I will go for shopping if and only if I have a money.
P↔Q
3. "If it rains, I will not go to the market.
P⌐Q
4. It is not cloudy, and it is not raining.
⌐P ^ ⌐Q
Some dog is a pet.
Everybody loves someone.
Subscribe
Thanks For
Watching
Reference:
Artificial Intelligence
A Modern Approach Third Edition
Peter Norvig and Stuart J. Russell
Subscribe
Like
Share
Next Topic: The Wumpus World
OMega TechEd
About the Channel
This channel helps you to prepare for BSc IT and BSc computer science subjects.
In this channel we will learn Business Intelligence ,Artificial Intelligence, Digital Electronics,
Internet OF Things Python programming , Data-Structure etc.
Which is useful for upcoming university exams.
Gmail: omega.teched@gmail.com
Social Media Handles:
omega.teched
megha_with
Subscribe

More Related Content

What's hot

Conceptual dependency
Conceptual dependencyConceptual dependency
Conceptual dependencyJismy .K.Jose
 
State Space Representation and Search
State Space Representation and SearchState Space Representation and Search
State Space Representation and SearchHitesh Mohapatra
 
Reflex and model based agents
Reflex and model based agentsReflex and model based agents
Reflex and model based agentsMegha Sharma
 
State space search
State space searchState space search
State space searchchauhankapil
 
Local beam search example
Local beam search exampleLocal beam search example
Local beam search exampleMegha Sharma
 
Knowledge representation In Artificial Intelligence
Knowledge representation In Artificial IntelligenceKnowledge representation In Artificial Intelligence
Knowledge representation In Artificial IntelligenceRamla Sheikh
 
Adversarial search
Adversarial searchAdversarial search
Adversarial searchNilu Desai
 
Forms of learning in ai
Forms of learning in aiForms of learning in ai
Forms of learning in aiRobert Antony
 
Knowledge Representation & Reasoning
Knowledge Representation & ReasoningKnowledge Representation & Reasoning
Knowledge Representation & ReasoningSajid Marwat
 
Autoencoders
AutoencodersAutoencoders
AutoencodersCloudxLab
 
Bayesian networks in AI
Bayesian networks in AIBayesian networks in AI
Bayesian networks in AIByoung-Hee Kim
 
An introduction to reinforcement learning
An introduction to  reinforcement learningAn introduction to  reinforcement learning
An introduction to reinforcement learningJie-Han Chen
 
ProLog (Artificial Intelligence) Introduction
ProLog (Artificial Intelligence) IntroductionProLog (Artificial Intelligence) Introduction
ProLog (Artificial Intelligence) Introductionwahab khan
 
knowledge representation using rules
knowledge representation using rulesknowledge representation using rules
knowledge representation using rulesHarini Balamurugan
 

What's hot (20)

Conceptual dependency
Conceptual dependencyConceptual dependency
Conceptual dependency
 
State Space Representation and Search
State Space Representation and SearchState Space Representation and Search
State Space Representation and Search
 
Reflex and model based agents
Reflex and model based agentsReflex and model based agents
Reflex and model based agents
 
State space search
State space searchState space search
State space 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)
 
Local beam search example
Local beam search exampleLocal beam search example
Local beam search example
 
Knowledge representation In Artificial Intelligence
Knowledge representation In Artificial IntelligenceKnowledge representation In Artificial Intelligence
Knowledge representation In Artificial Intelligence
 
First order logic
First order logicFirst order logic
First order logic
 
Adversarial search
Adversarial searchAdversarial search
Adversarial search
 
Classical Planning
Classical PlanningClassical Planning
Classical Planning
 
Forms of learning in ai
Forms of learning in aiForms of learning in ai
Forms of learning in ai
 
Knowledge Representation & Reasoning
Knowledge Representation & ReasoningKnowledge Representation & Reasoning
Knowledge Representation & Reasoning
 
Informed search
Informed searchInformed search
Informed search
 
First order logic
First order logicFirst order logic
First order logic
 
Planning
Planning Planning
Planning
 
Autoencoders
AutoencodersAutoencoders
Autoencoders
 
Bayesian networks in AI
Bayesian networks in AIBayesian networks in AI
Bayesian networks in AI
 
An introduction to reinforcement learning
An introduction to  reinforcement learningAn introduction to  reinforcement learning
An introduction to reinforcement learning
 
ProLog (Artificial Intelligence) Introduction
ProLog (Artificial Intelligence) IntroductionProLog (Artificial Intelligence) Introduction
ProLog (Artificial Intelligence) Introduction
 
knowledge representation using rules
knowledge representation using rulesknowledge representation using rules
knowledge representation using rules
 

Similar to Knowledge based agents

Logic programming (1)
Logic programming (1)Logic programming (1)
Logic programming (1)Nitesh Singh
 
Discrete Mathematics
Discrete MathematicsDiscrete Mathematics
Discrete MathematicsJihudumie.Com
 
S2 1
S2 1S2 1
S2 1IIUM
 
Verbal Valency in Hebrew Verbs
Verbal Valency in Hebrew VerbsVerbal Valency in Hebrew Verbs
Verbal Valency in Hebrew VerbsDirk Roorda
 
Knowledege Representation.pptx
Knowledege Representation.pptxKnowledege Representation.pptx
Knowledege Representation.pptxArslanAliArslanAli
 
AI NOTES ppt 4.pdf
AI NOTES ppt 4.pdfAI NOTES ppt 4.pdf
AI NOTES ppt 4.pdfARMANVERMA7
 
Introduction to logic and prolog - Part 1
Introduction to logic and prolog - Part 1Introduction to logic and prolog - Part 1
Introduction to logic and prolog - Part 1Sabu Francis
 
MATHMOW_LESSON-7.pptx
MATHMOW_LESSON-7.pptxMATHMOW_LESSON-7.pptx
MATHMOW_LESSON-7.pptxPink192658
 
Logic in Computer Science Unit 2 (1).pptx
Logic in Computer Science Unit 2 (1).pptxLogic in Computer Science Unit 2 (1).pptx
Logic in Computer Science Unit 2 (1).pptxPriyalMayurManvar
 
Jarrar: First Order Logic- Inference Methods
Jarrar: First Order Logic- Inference MethodsJarrar: First Order Logic- Inference Methods
Jarrar: First Order Logic- Inference MethodsMustafa Jarrar
 
General Mathematics Lessons-Unit-4-to-6.pptx
General Mathematics Lessons-Unit-4-to-6.pptxGeneral Mathematics Lessons-Unit-4-to-6.pptx
General Mathematics Lessons-Unit-4-to-6.pptxmaynardpascual
 
MACHINE LEARNING-LEARNING RULE
MACHINE LEARNING-LEARNING RULEMACHINE LEARNING-LEARNING RULE
MACHINE LEARNING-LEARNING RULEDrBindhuM
 
Theory of first order logic
Theory of first order logicTheory of first order logic
Theory of first order logicDevaddd
 
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
 
Ai lecture 07(unit03)
Ai lecture  07(unit03)Ai lecture  07(unit03)
Ai lecture 07(unit03)vikas dhakane
 

Similar to Knowledge based agents (20)

Logic programming (1)
Logic programming (1)Logic programming (1)
Logic programming (1)
 
Discrete Mathematics
Discrete MathematicsDiscrete Mathematics
Discrete Mathematics
 
Logic.pdf
Logic.pdfLogic.pdf
Logic.pdf
 
S2 1
S2 1S2 1
S2 1
 
Verbal Valency in Hebrew Verbs
Verbal Valency in Hebrew VerbsVerbal Valency in Hebrew Verbs
Verbal Valency in Hebrew Verbs
 
Knowledege Representation.pptx
Knowledege Representation.pptxKnowledege Representation.pptx
Knowledege Representation.pptx
 
AI NOTES ppt 4.pdf
AI NOTES ppt 4.pdfAI NOTES ppt 4.pdf
AI NOTES ppt 4.pdf
 
Introduction to logic and prolog - Part 1
Introduction to logic and prolog - Part 1Introduction to logic and prolog - Part 1
Introduction to logic and prolog - Part 1
 
ICS1019.pdf
ICS1019.pdfICS1019.pdf
ICS1019.pdf
 
Notes for logic
Notes for logicNotes for logic
Notes for logic
 
Ics1019 ics5003
Ics1019 ics5003Ics1019 ics5003
Ics1019 ics5003
 
continuity of module 2.pptx
continuity of module 2.pptxcontinuity of module 2.pptx
continuity of module 2.pptx
 
MATHMOW_LESSON-7.pptx
MATHMOW_LESSON-7.pptxMATHMOW_LESSON-7.pptx
MATHMOW_LESSON-7.pptx
 
Logic in Computer Science Unit 2 (1).pptx
Logic in Computer Science Unit 2 (1).pptxLogic in Computer Science Unit 2 (1).pptx
Logic in Computer Science Unit 2 (1).pptx
 
Jarrar: First Order Logic- Inference Methods
Jarrar: First Order Logic- Inference MethodsJarrar: First Order Logic- Inference Methods
Jarrar: First Order Logic- Inference Methods
 
General Mathematics Lessons-Unit-4-to-6.pptx
General Mathematics Lessons-Unit-4-to-6.pptxGeneral Mathematics Lessons-Unit-4-to-6.pptx
General Mathematics Lessons-Unit-4-to-6.pptx
 
MACHINE LEARNING-LEARNING RULE
MACHINE LEARNING-LEARNING RULEMACHINE LEARNING-LEARNING RULE
MACHINE LEARNING-LEARNING RULE
 
Theory of first order logic
Theory of first order logicTheory of first order logic
Theory of first order logic
 
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...
 
Ai lecture 07(unit03)
Ai lecture  07(unit03)Ai lecture  07(unit03)
Ai lecture 07(unit03)
 

More from Megha Sharma

Association Rule mining
Association Rule miningAssociation Rule mining
Association Rule miningMegha Sharma
 
Bellman's equation Reinforcement learning - II
Bellman's equation Reinforcement learning - IIBellman's equation Reinforcement learning - II
Bellman's equation Reinforcement learning - IIMegha Sharma
 
Reinforcement learning in Machine learning
 Reinforcement learning in Machine learning Reinforcement learning in Machine learning
Reinforcement learning in Machine learningMegha Sharma
 
Entropy and information gain in decision tree.
Entropy and information gain in decision tree.Entropy and information gain in decision tree.
Entropy and information gain in decision tree.Megha Sharma
 
Types of Machine Learning. & Decision Tree.
Types of Machine Learning. & Decision Tree.Types of Machine Learning. & Decision Tree.
Types of Machine Learning. & Decision Tree.Megha Sharma
 
If statements in C
If statements in CIf statements in C
If statements in CMegha Sharma
 
Conditional and special operators
Conditional and special operatorsConditional and special operators
Conditional and special operatorsMegha Sharma
 
Assignment operators
Assignment operatorsAssignment operators
Assignment operatorsMegha Sharma
 
Relational and logical operators
Relational and logical operatorsRelational and logical operators
Relational and logical operatorsMegha Sharma
 
Arithmetic and increment decrement Operator
Arithmetic and increment decrement OperatorArithmetic and increment decrement Operator
Arithmetic and increment decrement OperatorMegha Sharma
 
Structure of C program
Structure of C programStructure of C program
Structure of C programMegha Sharma
 
Algorithm & Flowchart
Algorithm & FlowchartAlgorithm & Flowchart
Algorithm & FlowchartMegha Sharma
 
C Programming introduction
C Programming introductionC Programming introduction
C Programming introductionMegha Sharma
 
Enhanced ER Models
Enhanced ER ModelsEnhanced ER Models
Enhanced ER ModelsMegha Sharma
 
Entity Relationship design issues
Entity Relationship design issuesEntity Relationship design issues
Entity Relationship design issuesMegha Sharma
 
Participation Constraints in ER diagram
Participation Constraints in ER diagramParticipation Constraints in ER diagram
Participation Constraints in ER diagramMegha Sharma
 

More from Megha Sharma (20)

Ensemble learning
Ensemble learningEnsemble learning
Ensemble learning
 
Association Rule mining
Association Rule miningAssociation Rule mining
Association Rule mining
 
Bellman's equation Reinforcement learning - II
Bellman's equation Reinforcement learning - IIBellman's equation Reinforcement learning - II
Bellman's equation Reinforcement learning - II
 
Reinforcement learning in Machine learning
 Reinforcement learning in Machine learning Reinforcement learning in Machine learning
Reinforcement learning in Machine learning
 
E-M Algorithm
E-M AlgorithmE-M Algorithm
E-M Algorithm
 
Entropy and information gain in decision tree.
Entropy and information gain in decision tree.Entropy and information gain in decision tree.
Entropy and information gain in decision tree.
 
Types of Machine Learning. & Decision Tree.
Types of Machine Learning. & Decision Tree.Types of Machine Learning. & Decision Tree.
Types of Machine Learning. & Decision Tree.
 
If statements in C
If statements in CIf statements in C
If statements in C
 
Conditional and special operators
Conditional and special operatorsConditional and special operators
Conditional and special operators
 
Assignment operators
Assignment operatorsAssignment operators
Assignment operators
 
Bitwise operators
Bitwise operatorsBitwise operators
Bitwise operators
 
Relational and logical operators
Relational and logical operatorsRelational and logical operators
Relational and logical operators
 
Arithmetic and increment decrement Operator
Arithmetic and increment decrement OperatorArithmetic and increment decrement Operator
Arithmetic and increment decrement Operator
 
Structure of C program
Structure of C programStructure of C program
Structure of C program
 
C tokens
C tokensC tokens
C tokens
 
Algorithm & Flowchart
Algorithm & FlowchartAlgorithm & Flowchart
Algorithm & Flowchart
 
C Programming introduction
C Programming introductionC Programming introduction
C Programming introduction
 
Enhanced ER Models
Enhanced ER ModelsEnhanced ER Models
Enhanced ER Models
 
Entity Relationship design issues
Entity Relationship design issuesEntity Relationship design issues
Entity Relationship design issues
 
Participation Constraints in ER diagram
Participation Constraints in ER diagramParticipation Constraints in ER diagram
Participation Constraints in ER diagram
 

Recently uploaded

Enzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdf
Enzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdfEnzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdf
Enzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdfSumit Tiwari
 
MARGINALIZATION (Different learners in Marginalized Group
MARGINALIZATION (Different learners in Marginalized GroupMARGINALIZATION (Different learners in Marginalized Group
MARGINALIZATION (Different learners in Marginalized GroupJonathanParaisoCruz
 
Presiding Officer Training module 2024 lok sabha elections
Presiding Officer Training module 2024 lok sabha electionsPresiding Officer Training module 2024 lok sabha elections
Presiding Officer Training module 2024 lok sabha electionsanshu789521
 
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxPOINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxSayali Powar
 
Crayon Activity Handout For the Crayon A
Crayon Activity Handout For the Crayon ACrayon Activity Handout For the Crayon A
Crayon Activity Handout For the Crayon AUnboundStockton
 
Framing an Appropriate Research Question 6b9b26d93da94caf993c038d9efcdedb.pdf
Framing an Appropriate Research Question 6b9b26d93da94caf993c038d9efcdedb.pdfFraming an Appropriate Research Question 6b9b26d93da94caf993c038d9efcdedb.pdf
Framing an Appropriate Research Question 6b9b26d93da94caf993c038d9efcdedb.pdfUjwalaBharambe
 
Earth Day Presentation wow hello nice great
Earth Day Presentation wow hello nice greatEarth Day Presentation wow hello nice great
Earth Day Presentation wow hello nice greatYousafMalik24
 
Full Stack Web Development Course for Beginners
Full Stack Web Development Course  for BeginnersFull Stack Web Development Course  for Beginners
Full Stack Web Development Course for BeginnersSabitha Banu
 
How to Make a Pirate ship Primary Education.pptx
How to Make a Pirate ship Primary Education.pptxHow to Make a Pirate ship Primary Education.pptx
How to Make a Pirate ship Primary Education.pptxmanuelaromero2013
 
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdfssuser54595a
 
Alper Gobel In Media Res Media Component
Alper Gobel In Media Res Media ComponentAlper Gobel In Media Res Media Component
Alper Gobel In Media Res Media ComponentInMediaRes1
 
Computed Fields and api Depends in the Odoo 17
Computed Fields and api Depends in the Odoo 17Computed Fields and api Depends in the Odoo 17
Computed Fields and api Depends in the Odoo 17Celine George
 
Difference Between Search & Browse Methods in Odoo 17
Difference Between Search & Browse Methods in Odoo 17Difference Between Search & Browse Methods in Odoo 17
Difference Between Search & Browse Methods in Odoo 17Celine George
 
Historical philosophical, theoretical, and legal foundations of special and i...
Historical philosophical, theoretical, and legal foundations of special and i...Historical philosophical, theoretical, and legal foundations of special and i...
Historical philosophical, theoretical, and legal foundations of special and i...jaredbarbolino94
 
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...Marc Dusseiller Dusjagr
 
Introduction to ArtificiaI Intelligence in Higher Education
Introduction to ArtificiaI Intelligence in Higher EducationIntroduction to ArtificiaI Intelligence in Higher Education
Introduction to ArtificiaI Intelligence in Higher Educationpboyjonauth
 
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️9953056974 Low Rate Call Girls In Saket, Delhi NCR
 
EPANDING THE CONTENT OF AN OUTLINE using notes.pptx
EPANDING THE CONTENT OF AN OUTLINE using notes.pptxEPANDING THE CONTENT OF AN OUTLINE using notes.pptx
EPANDING THE CONTENT OF AN OUTLINE using notes.pptxRaymartEstabillo3
 

Recently uploaded (20)

Enzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdf
Enzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdfEnzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdf
Enzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdf
 
MARGINALIZATION (Different learners in Marginalized Group
MARGINALIZATION (Different learners in Marginalized GroupMARGINALIZATION (Different learners in Marginalized Group
MARGINALIZATION (Different learners in Marginalized Group
 
Model Call Girl in Bikash Puri Delhi reach out to us at 🔝9953056974🔝
Model Call Girl in Bikash Puri  Delhi reach out to us at 🔝9953056974🔝Model Call Girl in Bikash Puri  Delhi reach out to us at 🔝9953056974🔝
Model Call Girl in Bikash Puri Delhi reach out to us at 🔝9953056974🔝
 
Presiding Officer Training module 2024 lok sabha elections
Presiding Officer Training module 2024 lok sabha electionsPresiding Officer Training module 2024 lok sabha elections
Presiding Officer Training module 2024 lok sabha elections
 
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxPOINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
 
Crayon Activity Handout For the Crayon A
Crayon Activity Handout For the Crayon ACrayon Activity Handout For the Crayon A
Crayon Activity Handout For the Crayon A
 
Framing an Appropriate Research Question 6b9b26d93da94caf993c038d9efcdedb.pdf
Framing an Appropriate Research Question 6b9b26d93da94caf993c038d9efcdedb.pdfFraming an Appropriate Research Question 6b9b26d93da94caf993c038d9efcdedb.pdf
Framing an Appropriate Research Question 6b9b26d93da94caf993c038d9efcdedb.pdf
 
Earth Day Presentation wow hello nice great
Earth Day Presentation wow hello nice greatEarth Day Presentation wow hello nice great
Earth Day Presentation wow hello nice great
 
Full Stack Web Development Course for Beginners
Full Stack Web Development Course  for BeginnersFull Stack Web Development Course  for Beginners
Full Stack Web Development Course for Beginners
 
How to Make a Pirate ship Primary Education.pptx
How to Make a Pirate ship Primary Education.pptxHow to Make a Pirate ship Primary Education.pptx
How to Make a Pirate ship Primary Education.pptx
 
ESSENTIAL of (CS/IT/IS) class 06 (database)
ESSENTIAL of (CS/IT/IS) class 06 (database)ESSENTIAL of (CS/IT/IS) class 06 (database)
ESSENTIAL of (CS/IT/IS) class 06 (database)
 
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf
 
Alper Gobel In Media Res Media Component
Alper Gobel In Media Res Media ComponentAlper Gobel In Media Res Media Component
Alper Gobel In Media Res Media Component
 
Computed Fields and api Depends in the Odoo 17
Computed Fields and api Depends in the Odoo 17Computed Fields and api Depends in the Odoo 17
Computed Fields and api Depends in the Odoo 17
 
Difference Between Search & Browse Methods in Odoo 17
Difference Between Search & Browse Methods in Odoo 17Difference Between Search & Browse Methods in Odoo 17
Difference Between Search & Browse Methods in Odoo 17
 
Historical philosophical, theoretical, and legal foundations of special and i...
Historical philosophical, theoretical, and legal foundations of special and i...Historical philosophical, theoretical, and legal foundations of special and i...
Historical philosophical, theoretical, and legal foundations of special and i...
 
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
 
Introduction to ArtificiaI Intelligence in Higher Education
Introduction to ArtificiaI Intelligence in Higher EducationIntroduction to ArtificiaI Intelligence in Higher Education
Introduction to ArtificiaI Intelligence in Higher Education
 
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️
 
EPANDING THE CONTENT OF AN OUTLINE using notes.pptx
EPANDING THE CONTENT OF AN OUTLINE using notes.pptxEPANDING THE CONTENT OF AN OUTLINE using notes.pptx
EPANDING THE CONTENT OF AN OUTLINE using notes.pptx
 

Knowledge based agents

  • 2. Subscribe Knowledge-Based (Logical) Agents Knowledge-based agents can accept new tasks in the form of explicitly described goals; they can achieve competence quickly by being told or learning new knowledge about the environment; and they can adapt to changes in the environment by updating the relevant knowledge.
  • 3. Components Knowledge Base • A knowledge base is a set of sentence. Each sentence is expressed in a language called a knowledge representation language and represents some assertion about the world. Inference Engine • Inference engine allows us to add a new sentence to the knowledge base. A sentence is a proposition about the world. Inference system applies logical rules to the KB to deduce new information. Subscribe
  • 4. Operations performed by K.B.A.  A Knowledge Base keeps track of things.  We can TELL it facts or ASK for inference. TELL: This operation tells the knowledge base what it perceives from the environment. ASK: This operation asks the knowledge base what action it should perform. Example: TELL: Father of John is Robbin. TELL: Marry is John’s sister. TELL: John’s father is the same as John’s sister’s father. ASK: Who is Marry’s father? Subscribe
  • 5. Each time when the function is called, it does three things. It TELLs the KB what it perceives. It ASKs KB what action it should take Agent program TELLS the KB that which action was chosen. Subscribe
  • 6. Logic  Knowledge bases consist of sentences.  These sentences are expressed according to the syntax of the representation language , which specifies all the sentences that are well formed.  x+y=2 (well formed)  x2y=+ (not well formed)  A logic must also defines the semantics or meaning of sentences.  The semantics defines the TRUTH of each sentence with respect to each possible world.(model)  x+ y=4 is true in a world where x=2 and y=2 But false in a world where x is 1 and y is 1 Subscribe
  • 7. Propositional Logic Subscribe  The Syntax of propositional logic defines the allowable sentences. The atomic sentences consist of a single proposition symbol Each such symbol stands for a proposition that can be true or false. Example: Sunday is Holiday. (true proposition) 2+1=4 (False proposition) some boys like to play cricket.(Not a propositional logic)  Complex sentences are constructed from simpler sentences, using parentheses and logical connective.
  • 8. Connectives Symbol Name Meaning ¬ (Not) Negation If P is true , ¬P will be false and vice versa. ^ (and) Conjunction (P^Q) is true if both P and Q are true otherwise false. v (or) Disjunction (P v Q) is true if either P or Q is true (or both) otherwise false. → implies If P happens then Q happens. ↔ Double implication P happens if and only if Q happens. Subscribe
  • 9. Examples: 1.You can use the college library only if you are a student or are a faculty. P(Q v R) 2. I will go for shopping if and only if I have a money. P↔Q 3. "If it rains, I will not go to the market. P⌐Q 4. It is not cloudy, and it is not raining. ⌐P ^ ⌐Q Some dog is a pet. Everybody loves someone. Subscribe
  • 10. Thanks For Watching Reference: Artificial Intelligence A Modern Approach Third Edition Peter Norvig and Stuart J. Russell Subscribe Like Share Next Topic: The Wumpus World
  • 11. OMega TechEd About the Channel This channel helps you to prepare for BSc IT and BSc computer science subjects. In this channel we will learn Business Intelligence ,Artificial Intelligence, Digital Electronics, Internet OF Things Python programming , Data-Structure etc. Which is useful for upcoming university exams. Gmail: omega.teched@gmail.com Social Media Handles: omega.teched megha_with Subscribe