尊敬的 微信汇率:1円 ≈ 0.046166 元 支付宝汇率:1円 ≈ 0.046257元 [退出登录]
SlideShare a Scribd company logo
What is Knowledge Representation and Reasoning
?
• In the world of Artificial Intelligence, Knowledge representation is that area in which information
about the world is given in such a form that the computer system can understand and can leverage
it in performing complex tasks like diagnosing a medical condition or having a dialog in natural
language.
• Knowledge representation simplifies the complex system for better design and development by
studying human psychology and the way humans solve their problems.
• Semantic nets, systems architecture, frames, rules and ontologies can be considered as examples
of knowledge representation.
What is Knowledge Representation and Reasoning
?
• Knowledge representation goes parallel with automated reasoning. This is due to the aim of
knowledge representation, i.e. to be able to reason about that knowledge, assert new knowledge
etc.
• All the knowledge representation languages that are known feature reasoning, as it is its integral
part.
• Expressivity and practicality are those chunks of knowledge representation between trade-off has to
be set when design is considered.
• First Order Logic (FOL) is the ultimatum of knowledge representation formalism in terms of
expressive power and compactness.
• However, anything, no matter how good, has some setbacks and so does FOL- ease of use and
practicality of implementation.
Cont….
1. Primitives- Semantic networks belongs the circle of knowledge representation primitives. For
general fast search, data structures and algorithms were used. In the incunabula of knowledge
representation, Lisp programming language was modeled after the lambda calculus and was very
frequently as a form of functional knowledge representation. Frame and Rules were the next gen
primitive.
2. Meta-Representation - Also known as the issue of reflection in the world of computer science,
meta-representation is known as the capability of formalism to have an information access about
its own state. Smalltalk and CLOS are popular examples of meta-object protocol that not only
provides access to class objects during run time but also redefine the knowledge base structure
during that run time.
3. Incompleteness- There is a demand of traditional logic requirements for additional axioms and
constraints which are mandatory to deal with the real world which the mathematical world
opposes. The area of its usefulness lies in associating the extent of confidence in a statement.
Knowledge representation by Ron Brachman
4 . Definitions and Universals vs. facts and defaults - All the general statements about the universe lie
under Universal like “Humans are not immortal.” When we talk about specific examples of Universals,
we talk about Facts, say, “Albert Einstein was a human and so he was not immortal.” Generally
speaking, definitions and universal seem to quantify the universe whereas facts and defaults are all
about existential quantification.
5. Non- monotonic reasoning – Non-monotonic reasoning can be considered as a type of ideal,
imaginary reasoning.
6. Expressive Adequacy - FOL is something that is quite mostly used even by Brachman and almost all
researchers in order to quantify expressive adequacy. When theoretical limitations are considered, it is
clear enough that FOL, under full implementation is not practical.
7. Reasoning Efficiency - Efficiency basically means ability of a system and thus reasoning efficiency
relates to the run time efficiency of the system. This can be taken into consideration as the rear side of
expressive adequacy. Expressive adequacy is directly proportional to the power of a presentation. But
on the contrary, the effect of automatic reasoning is inversed.
Cont...
Our Specialization
Artificial Neural Network
Natural Language Processing Artificial Neural Network
Character Recognition Artificial Neural Network
Artificial Neural Network
ADDRES : 13/14/15 JAY NARAYAN COMPLEX PANIGATE VADODARA – 390017
PHONE NO - +919638544455
anantsoftcomp
anantsoftcomputing
Contact Us
anantsoftcomputing.com
jeegar@anantsoftcomputing.com

More Related Content

What's hot

Semantic net in AI
Semantic net in AISemantic net in AI
Semantic net in AI
ShahDhruv21
 
Rule based system
Rule based systemRule based system
Rule based system
Dr. C.V. Suresh Babu
 
Artificial Intelligence Notes Unit 1
Artificial Intelligence Notes Unit 1 Artificial Intelligence Notes Unit 1
Artificial Intelligence Notes Unit 1
DigiGurukul
 
Forms of learning in ai
Forms of learning in aiForms of learning in ai
Forms of learning in ai
Robert Antony
 
Artificial Neural Network Lect4 : Single Layer Perceptron Classifiers
Artificial Neural Network Lect4 : Single Layer Perceptron ClassifiersArtificial Neural Network Lect4 : Single Layer Perceptron Classifiers
Artificial Neural Network Lect4 : Single Layer Perceptron Classifiers
Mohammed Bennamoun
 
Frames
FramesFrames
Frames
amitp26
 
Heuristic Search Techniques {Artificial Intelligence}
Heuristic Search Techniques {Artificial Intelligence}Heuristic Search Techniques {Artificial Intelligence}
Heuristic Search Techniques {Artificial Intelligence}
FellowBuddy.com
 
Production System in AI
Production System in AIProduction System in AI
Production System in AI
Bharat Bhushan
 
Artificial intelligence and knowledge representation
Artificial intelligence and knowledge representationArtificial intelligence and knowledge representation
Artificial intelligence and knowledge representation
Sajan Sahu
 
Local search algorithm
Local search algorithmLocal search algorithm
Local search algorithm
Megha Sharma
 
Knowledge representation in AI
Knowledge representation in AIKnowledge representation in AI
Knowledge representation in AI
Vishal Singh
 
Production system in ai
Production system in aiProduction system in ai
Production system in ai
sabin kafle
 
First order logic in knowledge representation
First order logic in knowledge representationFirst order logic in knowledge representation
First order logic in knowledge representation
Sabaragamuwa University
 
Propositional logic
Propositional logicPropositional logic
Propositional logic
Rushdi Shams
 
Planning
PlanningPlanning
Planning
ahmad bassiouny
 
Feed forward ,back propagation,gradient descent
Feed forward ,back propagation,gradient descentFeed forward ,back propagation,gradient descent
Feed forward ,back propagation,gradient descent
Muhammad Rasel
 
Methods of Optimization in Machine Learning
Methods of Optimization in Machine LearningMethods of Optimization in Machine Learning
Methods of Optimization in Machine Learning
Knoldus Inc.
 
A* Search Algorithm
A* Search AlgorithmA* Search Algorithm
A* Search Algorithm
vikas dhakane
 
First order predicate logic (fopl)
First order predicate logic (fopl)First order predicate logic (fopl)
First order predicate logic (fopl)
chauhankapil
 
Control Strategies in AI
Control Strategies in AIControl Strategies in AI
Control Strategies in AI
Amey Kerkar
 

What's hot (20)

Semantic net in AI
Semantic net in AISemantic net in AI
Semantic net in AI
 
Rule based system
Rule based systemRule based system
Rule based system
 
Artificial Intelligence Notes Unit 1
Artificial Intelligence Notes Unit 1 Artificial Intelligence Notes Unit 1
Artificial Intelligence Notes Unit 1
 
Forms of learning in ai
Forms of learning in aiForms of learning in ai
Forms of learning in ai
 
Artificial Neural Network Lect4 : Single Layer Perceptron Classifiers
Artificial Neural Network Lect4 : Single Layer Perceptron ClassifiersArtificial Neural Network Lect4 : Single Layer Perceptron Classifiers
Artificial Neural Network Lect4 : Single Layer Perceptron Classifiers
 
Frames
FramesFrames
Frames
 
Heuristic Search Techniques {Artificial Intelligence}
Heuristic Search Techniques {Artificial Intelligence}Heuristic Search Techniques {Artificial Intelligence}
Heuristic Search Techniques {Artificial Intelligence}
 
Production System in AI
Production System in AIProduction System in AI
Production System in AI
 
Artificial intelligence and knowledge representation
Artificial intelligence and knowledge representationArtificial intelligence and knowledge representation
Artificial intelligence and knowledge representation
 
Local search algorithm
Local search algorithmLocal search algorithm
Local search algorithm
 
Knowledge representation in AI
Knowledge representation in AIKnowledge representation in AI
Knowledge representation in AI
 
Production system in ai
Production system in aiProduction system in ai
Production system in ai
 
First order logic in knowledge representation
First order logic in knowledge representationFirst order logic in knowledge representation
First order logic in knowledge representation
 
Propositional logic
Propositional logicPropositional logic
Propositional logic
 
Planning
PlanningPlanning
Planning
 
Feed forward ,back propagation,gradient descent
Feed forward ,back propagation,gradient descentFeed forward ,back propagation,gradient descent
Feed forward ,back propagation,gradient descent
 
Methods of Optimization in Machine Learning
Methods of Optimization in Machine LearningMethods of Optimization in Machine Learning
Methods of Optimization in Machine Learning
 
A* Search Algorithm
A* Search AlgorithmA* Search Algorithm
A* Search Algorithm
 
First order predicate logic (fopl)
First order predicate logic (fopl)First order predicate logic (fopl)
First order predicate logic (fopl)
 
Control Strategies in AI
Control Strategies in AIControl Strategies in AI
Control Strategies in AI
 

Similar to What is knowledge representation and reasoning ?

Artificial Intelligence_ Knowledge Representation
Artificial Intelligence_ Knowledge RepresentationArtificial Intelligence_ Knowledge Representation
Artificial Intelligence_ Knowledge Representation
ThenmozhiK5
 
Artificial-intelligence and its applications in medicine and dentistry.pdf
Artificial-intelligence and its applications in medicine and dentistry.pdfArtificial-intelligence and its applications in medicine and dentistry.pdf
Artificial-intelligence and its applications in medicine and dentistry.pdf
Romissaa ali Esmail/ faculty of dentistry/Al-Azhar university
 
A myth or a vision for interoperability: can systems communicate like humans do?
A myth or a vision for interoperability: can systems communicate like humans do?A myth or a vision for interoperability: can systems communicate like humans do?
A myth or a vision for interoperability: can systems communicate like humans do?
Milan Zdravković
 
What has ai in common with philosophy
What has ai in common with philosophyWhat has ai in common with philosophy
What has ai in common with philosophy
Lex Pit
 
Graphs, frames and related structures
Graphs, frames and related structuresGraphs, frames and related structures
Graphs, frames and related structures
SURBHI SAROHA
 
Artificial intelligence.pptx
Artificial intelligence.pptxArtificial intelligence.pptx
271_AI Lect Notes.pdf
271_AI Lect Notes.pdf271_AI Lect Notes.pdf
271_AI Lect Notes.pdf
kaxeca4096
 
1029 1026-1-pb
1029 1026-1-pb1029 1026-1-pb
1029 1026-1-pb
mustafa sarac
 
Artificial intelligent Lec 1-ai-introduction-
Artificial intelligent Lec 1-ai-introduction-Artificial intelligent Lec 1-ai-introduction-
Artificial intelligent Lec 1-ai-introduction-
Taymoor Nazmy
 
AI Introduction
AI Introduction AI Introduction
AI Introduction
Nashrah Habib
 
Introduction to Artificial Intelligence
Introduction to Artificial IntelligenceIntroduction to Artificial Intelligence
Introduction to Artificial Intelligence
Luca Bianchi
 
Towards which Intelligence? Cognition as Design Key for building Artificial I...
Towards which Intelligence? Cognition as Design Key for building Artificial I...Towards which Intelligence? Cognition as Design Key for building Artificial I...
Towards which Intelligence? Cognition as Design Key for building Artificial I...
Antonio Lieto
 
ARTIFICIAL INTELLIGENCE---UNIT 4.pptx
ARTIFICIAL INTELLIGENCE---UNIT 4.pptxARTIFICIAL INTELLIGENCE---UNIT 4.pptx
ARTIFICIAL INTELLIGENCE---UNIT 4.pptx
RuchitaMaaran
 
Artificial Intelligence Techniques In Power Systems Paper Presentation
Artificial Intelligence Techniques In Power Systems Paper PresentationArtificial Intelligence Techniques In Power Systems Paper Presentation
Artificial Intelligence Techniques In Power Systems Paper Presentation
guestac67362
 
01_Artificial_Intelligence-Introduction.ppt
01_Artificial_Intelligence-Introduction.ppt01_Artificial_Intelligence-Introduction.ppt
01_Artificial_Intelligence-Introduction.ppt
MemMem25
 
Introduction to Artificial Intelligence.doc
Introduction to Artificial Intelligence.docIntroduction to Artificial Intelligence.doc
Introduction to Artificial Intelligence.doc
butest
 
SC01_IntroductionSC-Unit-I.ppt
SC01_IntroductionSC-Unit-I.pptSC01_IntroductionSC-Unit-I.ppt
SC01_IntroductionSC-Unit-I.ppt
Ramya Nellutla
 
1 Introduction to AI.pptx
1 Introduction to AI.pptx1 Introduction to AI.pptx
1 Introduction to AI.pptx
BikashAcharya13
 
Knowledge base system
Knowledge base systemKnowledge base system
Knowledge base system
RanjithaM32
 
Soft computing01
Soft computing01Soft computing01
Soft computing01
university of sargodha
 

Similar to What is knowledge representation and reasoning ? (20)

Artificial Intelligence_ Knowledge Representation
Artificial Intelligence_ Knowledge RepresentationArtificial Intelligence_ Knowledge Representation
Artificial Intelligence_ Knowledge Representation
 
Artificial-intelligence and its applications in medicine and dentistry.pdf
Artificial-intelligence and its applications in medicine and dentistry.pdfArtificial-intelligence and its applications in medicine and dentistry.pdf
Artificial-intelligence and its applications in medicine and dentistry.pdf
 
A myth or a vision for interoperability: can systems communicate like humans do?
A myth or a vision for interoperability: can systems communicate like humans do?A myth or a vision for interoperability: can systems communicate like humans do?
A myth or a vision for interoperability: can systems communicate like humans do?
 
What has ai in common with philosophy
What has ai in common with philosophyWhat has ai in common with philosophy
What has ai in common with philosophy
 
Graphs, frames and related structures
Graphs, frames and related structuresGraphs, frames and related structures
Graphs, frames and related structures
 
Artificial intelligence.pptx
Artificial intelligence.pptxArtificial intelligence.pptx
Artificial intelligence.pptx
 
271_AI Lect Notes.pdf
271_AI Lect Notes.pdf271_AI Lect Notes.pdf
271_AI Lect Notes.pdf
 
1029 1026-1-pb
1029 1026-1-pb1029 1026-1-pb
1029 1026-1-pb
 
Artificial intelligent Lec 1-ai-introduction-
Artificial intelligent Lec 1-ai-introduction-Artificial intelligent Lec 1-ai-introduction-
Artificial intelligent Lec 1-ai-introduction-
 
AI Introduction
AI Introduction AI Introduction
AI Introduction
 
Introduction to Artificial Intelligence
Introduction to Artificial IntelligenceIntroduction to Artificial Intelligence
Introduction to Artificial Intelligence
 
Towards which Intelligence? Cognition as Design Key for building Artificial I...
Towards which Intelligence? Cognition as Design Key for building Artificial I...Towards which Intelligence? Cognition as Design Key for building Artificial I...
Towards which Intelligence? Cognition as Design Key for building Artificial I...
 
ARTIFICIAL INTELLIGENCE---UNIT 4.pptx
ARTIFICIAL INTELLIGENCE---UNIT 4.pptxARTIFICIAL INTELLIGENCE---UNIT 4.pptx
ARTIFICIAL INTELLIGENCE---UNIT 4.pptx
 
Artificial Intelligence Techniques In Power Systems Paper Presentation
Artificial Intelligence Techniques In Power Systems Paper PresentationArtificial Intelligence Techniques In Power Systems Paper Presentation
Artificial Intelligence Techniques In Power Systems Paper Presentation
 
01_Artificial_Intelligence-Introduction.ppt
01_Artificial_Intelligence-Introduction.ppt01_Artificial_Intelligence-Introduction.ppt
01_Artificial_Intelligence-Introduction.ppt
 
Introduction to Artificial Intelligence.doc
Introduction to Artificial Intelligence.docIntroduction to Artificial Intelligence.doc
Introduction to Artificial Intelligence.doc
 
SC01_IntroductionSC-Unit-I.ppt
SC01_IntroductionSC-Unit-I.pptSC01_IntroductionSC-Unit-I.ppt
SC01_IntroductionSC-Unit-I.ppt
 
1 Introduction to AI.pptx
1 Introduction to AI.pptx1 Introduction to AI.pptx
1 Introduction to AI.pptx
 
Knowledge base system
Knowledge base systemKnowledge base system
Knowledge base system
 
Soft computing01
Soft computing01Soft computing01
Soft computing01
 

Recently uploaded

Automation Student Developers Session 3: Introduction to UI Automation
Automation Student Developers Session 3: Introduction to UI AutomationAutomation Student Developers Session 3: Introduction to UI Automation
Automation Student Developers Session 3: Introduction to UI Automation
UiPathCommunity
 
ScyllaDB Leaps Forward with Dor Laor, CEO of ScyllaDB
ScyllaDB Leaps Forward with Dor Laor, CEO of ScyllaDBScyllaDB Leaps Forward with Dor Laor, CEO of ScyllaDB
ScyllaDB Leaps Forward with Dor Laor, CEO of ScyllaDB
ScyllaDB
 
Must Know Postgres Extension for DBA and Developer during Migration
Must Know Postgres Extension for DBA and Developer during MigrationMust Know Postgres Extension for DBA and Developer during Migration
Must Know Postgres Extension for DBA and Developer during Migration
Mydbops
 
Demystifying Knowledge Management through Storytelling
Demystifying Knowledge Management through StorytellingDemystifying Knowledge Management through Storytelling
Demystifying Knowledge Management through Storytelling
Enterprise Knowledge
 
Building a Semantic Layer of your Data Platform
Building a Semantic Layer of your Data PlatformBuilding a Semantic Layer of your Data Platform
Building a Semantic Layer of your Data Platform
Enterprise Knowledge
 
Discover the Unseen: Tailored Recommendation of Unwatched Content
Discover the Unseen: Tailored Recommendation of Unwatched ContentDiscover the Unseen: Tailored Recommendation of Unwatched Content
Discover the Unseen: Tailored Recommendation of Unwatched Content
ScyllaDB
 
From NCSA to the National Research Platform
From NCSA to the National Research PlatformFrom NCSA to the National Research Platform
From NCSA to the National Research Platform
Larry Smarr
 
Cyber Recovery Wargame
Cyber Recovery WargameCyber Recovery Wargame
Cyber Recovery Wargame
Databarracks
 
ScyllaDB Real-Time Event Processing with CDC
ScyllaDB Real-Time Event Processing with CDCScyllaDB Real-Time Event Processing with CDC
ScyllaDB Real-Time Event Processing with CDC
ScyllaDB
 
Facilitation Skills - When to Use and Why.pptx
Facilitation Skills - When to Use and Why.pptxFacilitation Skills - When to Use and Why.pptx
Facilitation Skills - When to Use and Why.pptx
Knoldus Inc.
 
Chapter 5 - Managing Test Activities V4.0
Chapter 5 - Managing Test Activities V4.0Chapter 5 - Managing Test Activities V4.0
Chapter 5 - Managing Test Activities V4.0
Neeraj Kumar Singh
 
Multivendor cloud production with VSF TR-11 - there and back again
Multivendor cloud production with VSF TR-11 - there and back againMultivendor cloud production with VSF TR-11 - there and back again
Multivendor cloud production with VSF TR-11 - there and back again
Kieran Kunhya
 
TrustArc Webinar - Your Guide for Smooth Cross-Border Data Transfers and Glob...
TrustArc Webinar - Your Guide for Smooth Cross-Border Data Transfers and Glob...TrustArc Webinar - Your Guide for Smooth Cross-Border Data Transfers and Glob...
TrustArc Webinar - Your Guide for Smooth Cross-Border Data Transfers and Glob...
TrustArc
 
Call Girls Chennai ☎️ +91-7426014248 😍 Chennai Call Girl Beauty Girls Chennai...
Call Girls Chennai ☎️ +91-7426014248 😍 Chennai Call Girl Beauty Girls Chennai...Call Girls Chennai ☎️ +91-7426014248 😍 Chennai Call Girl Beauty Girls Chennai...
Call Girls Chennai ☎️ +91-7426014248 😍 Chennai Call Girl Beauty Girls Chennai...
anilsa9823
 
Introduction to ThousandEyes AMER Webinar
Introduction  to ThousandEyes AMER WebinarIntroduction  to ThousandEyes AMER Webinar
Introduction to ThousandEyes AMER Webinar
ThousandEyes
 
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...
AlexanderRichford
 
Guidelines for Effective Data Visualization
Guidelines for Effective Data VisualizationGuidelines for Effective Data Visualization
Guidelines for Effective Data Visualization
UmmeSalmaM1
 
APJC Introduction to ThousandEyes Webinar
APJC Introduction to ThousandEyes WebinarAPJC Introduction to ThousandEyes Webinar
APJC Introduction to ThousandEyes Webinar
ThousandEyes
 
MongoDB to ScyllaDB: Technical Comparison and the Path to Success
MongoDB to ScyllaDB: Technical Comparison and the Path to SuccessMongoDB to ScyllaDB: Technical Comparison and the Path to Success
MongoDB to ScyllaDB: Technical Comparison and the Path to Success
ScyllaDB
 
Mutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented ChatbotsMutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented Chatbots
Pablo Gómez Abajo
 

Recently uploaded (20)

Automation Student Developers Session 3: Introduction to UI Automation
Automation Student Developers Session 3: Introduction to UI AutomationAutomation Student Developers Session 3: Introduction to UI Automation
Automation Student Developers Session 3: Introduction to UI Automation
 
ScyllaDB Leaps Forward with Dor Laor, CEO of ScyllaDB
ScyllaDB Leaps Forward with Dor Laor, CEO of ScyllaDBScyllaDB Leaps Forward with Dor Laor, CEO of ScyllaDB
ScyllaDB Leaps Forward with Dor Laor, CEO of ScyllaDB
 
Must Know Postgres Extension for DBA and Developer during Migration
Must Know Postgres Extension for DBA and Developer during MigrationMust Know Postgres Extension for DBA and Developer during Migration
Must Know Postgres Extension for DBA and Developer during Migration
 
Demystifying Knowledge Management through Storytelling
Demystifying Knowledge Management through StorytellingDemystifying Knowledge Management through Storytelling
Demystifying Knowledge Management through Storytelling
 
Building a Semantic Layer of your Data Platform
Building a Semantic Layer of your Data PlatformBuilding a Semantic Layer of your Data Platform
Building a Semantic Layer of your Data Platform
 
Discover the Unseen: Tailored Recommendation of Unwatched Content
Discover the Unseen: Tailored Recommendation of Unwatched ContentDiscover the Unseen: Tailored Recommendation of Unwatched Content
Discover the Unseen: Tailored Recommendation of Unwatched Content
 
From NCSA to the National Research Platform
From NCSA to the National Research PlatformFrom NCSA to the National Research Platform
From NCSA to the National Research Platform
 
Cyber Recovery Wargame
Cyber Recovery WargameCyber Recovery Wargame
Cyber Recovery Wargame
 
ScyllaDB Real-Time Event Processing with CDC
ScyllaDB Real-Time Event Processing with CDCScyllaDB Real-Time Event Processing with CDC
ScyllaDB Real-Time Event Processing with CDC
 
Facilitation Skills - When to Use and Why.pptx
Facilitation Skills - When to Use and Why.pptxFacilitation Skills - When to Use and Why.pptx
Facilitation Skills - When to Use and Why.pptx
 
Chapter 5 - Managing Test Activities V4.0
Chapter 5 - Managing Test Activities V4.0Chapter 5 - Managing Test Activities V4.0
Chapter 5 - Managing Test Activities V4.0
 
Multivendor cloud production with VSF TR-11 - there and back again
Multivendor cloud production with VSF TR-11 - there and back againMultivendor cloud production with VSF TR-11 - there and back again
Multivendor cloud production with VSF TR-11 - there and back again
 
TrustArc Webinar - Your Guide for Smooth Cross-Border Data Transfers and Glob...
TrustArc Webinar - Your Guide for Smooth Cross-Border Data Transfers and Glob...TrustArc Webinar - Your Guide for Smooth Cross-Border Data Transfers and Glob...
TrustArc Webinar - Your Guide for Smooth Cross-Border Data Transfers and Glob...
 
Call Girls Chennai ☎️ +91-7426014248 😍 Chennai Call Girl Beauty Girls Chennai...
Call Girls Chennai ☎️ +91-7426014248 😍 Chennai Call Girl Beauty Girls Chennai...Call Girls Chennai ☎️ +91-7426014248 😍 Chennai Call Girl Beauty Girls Chennai...
Call Girls Chennai ☎️ +91-7426014248 😍 Chennai Call Girl Beauty Girls Chennai...
 
Introduction to ThousandEyes AMER Webinar
Introduction  to ThousandEyes AMER WebinarIntroduction  to ThousandEyes AMER Webinar
Introduction to ThousandEyes AMER Webinar
 
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...
 
Guidelines for Effective Data Visualization
Guidelines for Effective Data VisualizationGuidelines for Effective Data Visualization
Guidelines for Effective Data Visualization
 
APJC Introduction to ThousandEyes Webinar
APJC Introduction to ThousandEyes WebinarAPJC Introduction to ThousandEyes Webinar
APJC Introduction to ThousandEyes Webinar
 
MongoDB to ScyllaDB: Technical Comparison and the Path to Success
MongoDB to ScyllaDB: Technical Comparison and the Path to SuccessMongoDB to ScyllaDB: Technical Comparison and the Path to Success
MongoDB to ScyllaDB: Technical Comparison and the Path to Success
 
Mutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented ChatbotsMutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented Chatbots
 

What is knowledge representation and reasoning ?

  • 1. What is Knowledge Representation and Reasoning ?
  • 2. • In the world of Artificial Intelligence, Knowledge representation is that area in which information about the world is given in such a form that the computer system can understand and can leverage it in performing complex tasks like diagnosing a medical condition or having a dialog in natural language. • Knowledge representation simplifies the complex system for better design and development by studying human psychology and the way humans solve their problems. • Semantic nets, systems architecture, frames, rules and ontologies can be considered as examples of knowledge representation. What is Knowledge Representation and Reasoning ?
  • 3. • Knowledge representation goes parallel with automated reasoning. This is due to the aim of knowledge representation, i.e. to be able to reason about that knowledge, assert new knowledge etc. • All the knowledge representation languages that are known feature reasoning, as it is its integral part. • Expressivity and practicality are those chunks of knowledge representation between trade-off has to be set when design is considered. • First Order Logic (FOL) is the ultimatum of knowledge representation formalism in terms of expressive power and compactness. • However, anything, no matter how good, has some setbacks and so does FOL- ease of use and practicality of implementation. Cont….
  • 4. 1. Primitives- Semantic networks belongs the circle of knowledge representation primitives. For general fast search, data structures and algorithms were used. In the incunabula of knowledge representation, Lisp programming language was modeled after the lambda calculus and was very frequently as a form of functional knowledge representation. Frame and Rules were the next gen primitive. 2. Meta-Representation - Also known as the issue of reflection in the world of computer science, meta-representation is known as the capability of formalism to have an information access about its own state. Smalltalk and CLOS are popular examples of meta-object protocol that not only provides access to class objects during run time but also redefine the knowledge base structure during that run time. 3. Incompleteness- There is a demand of traditional logic requirements for additional axioms and constraints which are mandatory to deal with the real world which the mathematical world opposes. The area of its usefulness lies in associating the extent of confidence in a statement. Knowledge representation by Ron Brachman
  • 5. 4 . Definitions and Universals vs. facts and defaults - All the general statements about the universe lie under Universal like “Humans are not immortal.” When we talk about specific examples of Universals, we talk about Facts, say, “Albert Einstein was a human and so he was not immortal.” Generally speaking, definitions and universal seem to quantify the universe whereas facts and defaults are all about existential quantification. 5. Non- monotonic reasoning – Non-monotonic reasoning can be considered as a type of ideal, imaginary reasoning. 6. Expressive Adequacy - FOL is something that is quite mostly used even by Brachman and almost all researchers in order to quantify expressive adequacy. When theoretical limitations are considered, it is clear enough that FOL, under full implementation is not practical. 7. Reasoning Efficiency - Efficiency basically means ability of a system and thus reasoning efficiency relates to the run time efficiency of the system. This can be taken into consideration as the rear side of expressive adequacy. Expressive adequacy is directly proportional to the power of a presentation. But on the contrary, the effect of automatic reasoning is inversed. Cont...
  • 6. Our Specialization Artificial Neural Network Natural Language Processing Artificial Neural Network Character Recognition Artificial Neural Network Artificial Neural Network
  • 7. ADDRES : 13/14/15 JAY NARAYAN COMPLEX PANIGATE VADODARA – 390017 PHONE NO - +919638544455 anantsoftcomp anantsoftcomputing Contact Us anantsoftcomputing.com jeegar@anantsoftcomputing.com
  翻译: