SlideShare a Scribd company logo
Dendral Expert system
8/13/2013
1
Presented By:
Group No:-2
8/13/2013
2
OUTLINE
1. Meaning of Dendral
2. Inventors
3. History
4. Heuristic Dendral
5. Meta Dendral
6. Working
7. Plan-test-generate paradigm
8. Knowledge engineering
9. Disadvantages
10. Conclusion
8/13/2013
3
Dendral was an influential pioneer project inartificial intelligence
(AI) of the 1960s, and the computer software expert system that it
produced.
 The name Dendral is a portmanteaux of the term
“Dendritic Algorithm“
 Its primary aim was to help organic chemists in identifying
unknown organic molecules, by analyzing their mass spectra
using knowledge of chemistry.
Meaning of Dendral
8/13/2013
4
Inventors:
It was done at Stanford University by Edward
Feigenbaum, Bruce Buchanan, Joshua
Lederberg (a Nobel prize winner in genetics)
and Carl Djerassi. It began in 1965 and spans
approximately half the history of AI research.
8/13/2013
5
8/13/2013
6
It was written in Lisp (programming language), which was considered
language of AI.
(john McCarthy, 1958, MIT)
For this in laboratory, three generate and test 'method was used:-
 (possible hypothesis about molecular structure are generated and
tested by matching to actual data).
There was an early realization that experts use certain heuristics to
rule out certain options with possible structures.
It seemed like a good idea to encode that knowledge in a software
system
8/13/2013
7
The software program Dendral is considered the first
expert system because it automated the decision-making
process and problem-solving behavior of organic
chemists.
DENDRAL marked a major paradigm shift´ in AI:
a shift from general AI: a shift from genera
-- Purpose, knowledge purpose, knowledge
-- Sparse weak methods to domain sparse weak
methods
-- Specific, knowledge
-- Intensive techniques intensive techniques
History
8/13/2013
8
The aim of the project was to develop a computer
program to attain the level of computer program to
attain the level of performance of an experienced
human chemist.
The DENDRAL project originated from the
fundamental idea of Expert system
8/13/2013
9
Dendral
It consists of two sub-programs:
 Heuristic-Dendral
 Meta-Dendral
8/13/2013
10
 Heuristic Dendral
Heuristic Dendral is a program that uses mass
spectra with knowledge base of chemistry, to produce
a set of possible chemical structures that may be
responsible for producing the data.
. As the weight increases the molecules become
more complex, the number of possible compounds
increases drastically.
(Example: H2O)
8/13/2013
11
Meta-Dendral
Input - 1) The set of possible chemical structures
2) Corresponding mass spectra
Output- Explain correlation between proposed
structures & mass spectrum.
Thus,
Heuristic Dendral is a performance system
Meta Dendral is a learning system.
The program is based on two important features:
1) the plan-generate-test paradigm
2) knowledge engineering.
8/13/2013
12
WORKING
Heuristic Dendral
Meta Dendral
8/13/2013 13
PLAN – GENERATE - TEST PARADIGM
 It is a problem solving method used by both M.D. &
H.D.
 Aim-When there are large number of possibilities at
that it find a way to put constraints that rules out
large sets of candidate solution.
 “Hypothesis Formation Programe”
“Task specific knowledge”
8/13/2013
14
EXAMPLE-CONGEN
Available Information-
 C1: Empirical Formula- C12H14O.
 C2: Compounds Contains Keto group.
 C3: Three protons to the carbonyl group.
 C4: There are two vinyl group.
 C5: There is no conjugation.
 C6: There are no diallylic protons.
 C7: There are no additional multiple bonds.
 C8: There are no methyl group.
8/13/2013
15
PROCEDURE
 #DEFINE MOLFORM C12H14O.
 #DEFINE SUBSTRUCTURE Z
 #DEFINE SUBSTRUCTURE CH3.
 #DEFINE SUBSTRUCTURE V.
 #GENERATE
 #DRAW ATNAMED1
 #AMBEDED
 #DRAW ATNAMED1
 .
 .
 .
 #EXIT 8/13/2013
16
FINAL OUTPUT
8/13/2013
17
KNOWLEDGE ENGINEERING
 Aim- To attain a productive interaction between the
available knowledge base & problem solution
techniques.
 The first essential component of K.E. is “knowledge
base”.
 It is used to determine the set of possible chemical
structures that corresponds to the input data
 & form new general rules that helps it reduce the
number of candidate solutions.
8/13/2013
18
Derived systems:
Many systems were derived from Dendral, including:-
1. MYCIN
2. MOLGEN
3. MACSYMA
4. PROSPECTOR
5. XCON, and STEAMER
8/13/2013
19
It is a system that does not guarantee
about the solution, but reduces the number of
possible solution by discarding unlikely &
irrelevant solution.
Conclusion
8/13/2013
20
References:
Wikipedia
E-book
8/13/2013
21
Any
Queries
8/13/2013
22
8/13/2013
23

More Related Content

What's hot

20 Latest Computer Science Seminar Topics on Emerging Technologies
20 Latest Computer Science Seminar Topics on Emerging Technologies20 Latest Computer Science Seminar Topics on Emerging Technologies
20 Latest Computer Science Seminar Topics on Emerging Technologies
Seminar Links
 
Artificial intelligence slides beginners
Artificial intelligence slides beginners Artificial intelligence slides beginners
Artificial intelligence slides beginners
Antonio Fernandes
 
ppt of gesture recognition
ppt of gesture recognitionppt of gesture recognition
ppt of gesture recognition
Aayush Agrawal
 
Introduction to Natural Language Processing
Introduction to Natural Language ProcessingIntroduction to Natural Language Processing
Introduction to Natural Language Processing
Pranav Gupta
 
Skin Lesion Detection from Dermoscopic Images using Convolutional Neural Netw...
Skin Lesion Detection from Dermoscopic Images using Convolutional Neural Netw...Skin Lesion Detection from Dermoscopic Images using Convolutional Neural Netw...
Skin Lesion Detection from Dermoscopic Images using Convolutional Neural Netw...
Universitat Politècnica de Catalunya
 
Screenless Display PPT Presentation
Screenless Display PPT PresentationScreenless Display PPT Presentation
Screenless Display PPT Presentation
Sai Mohith
 
The Top Trends in Artificial Intelligence
The Top Trends in Artificial IntelligenceThe Top Trends in Artificial Intelligence
The Top Trends in Artificial Intelligence
Erling Hesselberg
 
Artificial Intelligence and Expert System
Artificial Intelligence  and Expert SystemArtificial Intelligence  and Expert System
Artificial Intelligence and Expert System
Dr.R. Gunavathi Ramasamy
 
Fraud detection with Machine Learning
Fraud detection with Machine LearningFraud detection with Machine Learning
Fraud detection with Machine Learning
Scaleway
 
Machine Learning Ml Overview Algorithms Use Cases And Applications
Machine Learning Ml Overview Algorithms Use Cases And ApplicationsMachine Learning Ml Overview Algorithms Use Cases And Applications
Machine Learning Ml Overview Algorithms Use Cases And Applications
SlideTeam
 
Sign language recognizer
Sign language recognizerSign language recognizer
Sign language recognizer
Bikash Chandra Karmokar
 
Expert system
Expert systemExpert system
Expert system
Hossam El-Deen Osama
 
Machine learning seminar presentation
Machine learning seminar presentationMachine learning seminar presentation
Machine learning seminar presentation
sweety seth
 
Face recognisation system
Face recognisation systemFace recognisation system
Face recognisation system
Saumya Ranjan Behura
 
Machine Learning and Real-World Applications
Machine Learning and Real-World ApplicationsMachine Learning and Real-World Applications
Machine Learning and Real-World Applications
MachinePulse
 
Hand Gesture Recognition
Hand Gesture RecognitionHand Gesture Recognition
Hand Gesture Recognition
Shounak Katyayan
 
human computer interface
human computer interfacehuman computer interface
human computer interface
Santosh Kumar
 
Ai lecture 14(unit03)
Ai lecture  14(unit03)Ai lecture  14(unit03)
Ai lecture 14(unit03)
vikas dhakane
 
Transformers AI PPT.pptx
Transformers AI PPT.pptxTransformers AI PPT.pptx
Transformers AI PPT.pptx
RahulKumar854607
 
Introduction to Recurrent Neural Network
Introduction to Recurrent Neural NetworkIntroduction to Recurrent Neural Network
Introduction to Recurrent Neural Network
Knoldus Inc.
 

What's hot (20)

20 Latest Computer Science Seminar Topics on Emerging Technologies
20 Latest Computer Science Seminar Topics on Emerging Technologies20 Latest Computer Science Seminar Topics on Emerging Technologies
20 Latest Computer Science Seminar Topics on Emerging Technologies
 
Artificial intelligence slides beginners
Artificial intelligence slides beginners Artificial intelligence slides beginners
Artificial intelligence slides beginners
 
ppt of gesture recognition
ppt of gesture recognitionppt of gesture recognition
ppt of gesture recognition
 
Introduction to Natural Language Processing
Introduction to Natural Language ProcessingIntroduction to Natural Language Processing
Introduction to Natural Language Processing
 
Skin Lesion Detection from Dermoscopic Images using Convolutional Neural Netw...
Skin Lesion Detection from Dermoscopic Images using Convolutional Neural Netw...Skin Lesion Detection from Dermoscopic Images using Convolutional Neural Netw...
Skin Lesion Detection from Dermoscopic Images using Convolutional Neural Netw...
 
Screenless Display PPT Presentation
Screenless Display PPT PresentationScreenless Display PPT Presentation
Screenless Display PPT Presentation
 
The Top Trends in Artificial Intelligence
The Top Trends in Artificial IntelligenceThe Top Trends in Artificial Intelligence
The Top Trends in Artificial Intelligence
 
Artificial Intelligence and Expert System
Artificial Intelligence  and Expert SystemArtificial Intelligence  and Expert System
Artificial Intelligence and Expert System
 
Fraud detection with Machine Learning
Fraud detection with Machine LearningFraud detection with Machine Learning
Fraud detection with Machine Learning
 
Machine Learning Ml Overview Algorithms Use Cases And Applications
Machine Learning Ml Overview Algorithms Use Cases And ApplicationsMachine Learning Ml Overview Algorithms Use Cases And Applications
Machine Learning Ml Overview Algorithms Use Cases And Applications
 
Sign language recognizer
Sign language recognizerSign language recognizer
Sign language recognizer
 
Expert system
Expert systemExpert system
Expert system
 
Machine learning seminar presentation
Machine learning seminar presentationMachine learning seminar presentation
Machine learning seminar presentation
 
Face recognisation system
Face recognisation systemFace recognisation system
Face recognisation system
 
Machine Learning and Real-World Applications
Machine Learning and Real-World ApplicationsMachine Learning and Real-World Applications
Machine Learning and Real-World Applications
 
Hand Gesture Recognition
Hand Gesture RecognitionHand Gesture Recognition
Hand Gesture Recognition
 
human computer interface
human computer interfacehuman computer interface
human computer interface
 
Ai lecture 14(unit03)
Ai lecture  14(unit03)Ai lecture  14(unit03)
Ai lecture 14(unit03)
 
Transformers AI PPT.pptx
Transformers AI PPT.pptxTransformers AI PPT.pptx
Transformers AI PPT.pptx
 
Introduction to Recurrent Neural Network
Introduction to Recurrent Neural NetworkIntroduction to Recurrent Neural Network
Introduction to Recurrent Neural Network
 

Viewers also liked

Mycin
MycinMycin
Mycin
vini89
 
Mycin 016
Mycin  016Mycin  016
Mycin 016
Nidhi Singh
 
Expert system mycin
Expert system   mycinExpert system   mycin
Expert system mycin
university of education,Lahore
 
Introduction To Mycin Expert System
Introduction To Mycin Expert SystemIntroduction To Mycin Expert System
Introduction To Mycin Expert System
Nipun Jaswal
 
Expert systems from rk
Expert systems from rkExpert systems from rk
Expert systems from rk
ramaslide
 
Expert Systems
Expert SystemsExpert Systems
Expert Systems
sadeenedian08
 
Expert Systems
Expert SystemsExpert Systems
Expert Systems
osmancikk
 
6.expert systems
6.expert systems6.expert systems
6.expert systems
Vinayak Sharma
 
Topic 8 expert system
Topic 8 expert systemTopic 8 expert system
Topic 8 expert system
Noreliana Md Sharif
 
Systeme expert mycin
Systeme expert  mycinSysteme expert  mycin
Systeme expert mycinaouatef2010
 
Expert system (mis)
Expert system (mis)Expert system (mis)
Lecture5 Expert Systems And Artificial Intelligence
Lecture5 Expert Systems And Artificial IntelligenceLecture5 Expert Systems And Artificial Intelligence
Lecture5 Expert Systems And Artificial Intelligence
Kodok Ngorex
 
Expert Systems
Expert SystemsExpert Systems
Expert Systems
Youfan Fukutenshi
 
экспертные системы
экспертные системыэкспертные системы
экспертные системы
izengrim
 
Chapter 6 expert system
Chapter 6 expert systemChapter 6 expert system
Chapter 6 expert system
wahab khan
 
Ai inductive bias and knowledge
Ai inductive bias and knowledgeAi inductive bias and knowledge
Ai inductive bias and knowledge
Dexter Montesclaros
 
Expert Anesthesia Monitoring System Final Presentation
Expert Anesthesia Monitoring System Final PresentationExpert Anesthesia Monitoring System Final Presentation
Expert Anesthesia Monitoring System Final Presentation
Joseph (Jay) McIsaac, MD, MS
 
Networks and Natural Language Processing
Networks and Natural Language ProcessingNetworks and Natural Language Processing
Networks and Natural Language Processing
Ahmed Magdy Ezzeldin, MSc.
 
Categorization of Semantic Roles for Dictionary Definitions
Categorization of Semantic Roles for Dictionary DefinitionsCategorization of Semantic Roles for Dictionary Definitions
Categorization of Semantic Roles for Dictionary Definitions
Andre Freitas
 
Expert systems
Expert systemsExpert systems
Expert systems
Unique_Alias
 

Viewers also liked (20)

Mycin
MycinMycin
Mycin
 
Mycin 016
Mycin  016Mycin  016
Mycin 016
 
Expert system mycin
Expert system   mycinExpert system   mycin
Expert system mycin
 
Introduction To Mycin Expert System
Introduction To Mycin Expert SystemIntroduction To Mycin Expert System
Introduction To Mycin Expert System
 
Expert systems from rk
Expert systems from rkExpert systems from rk
Expert systems from rk
 
Expert Systems
Expert SystemsExpert Systems
Expert Systems
 
Expert Systems
Expert SystemsExpert Systems
Expert Systems
 
6.expert systems
6.expert systems6.expert systems
6.expert systems
 
Topic 8 expert system
Topic 8 expert systemTopic 8 expert system
Topic 8 expert system
 
Systeme expert mycin
Systeme expert  mycinSysteme expert  mycin
Systeme expert mycin
 
Expert system (mis)
Expert system (mis)Expert system (mis)
Expert system (mis)
 
Lecture5 Expert Systems And Artificial Intelligence
Lecture5 Expert Systems And Artificial IntelligenceLecture5 Expert Systems And Artificial Intelligence
Lecture5 Expert Systems And Artificial Intelligence
 
Expert Systems
Expert SystemsExpert Systems
Expert Systems
 
экспертные системы
экспертные системыэкспертные системы
экспертные системы
 
Chapter 6 expert system
Chapter 6 expert systemChapter 6 expert system
Chapter 6 expert system
 
Ai inductive bias and knowledge
Ai inductive bias and knowledgeAi inductive bias and knowledge
Ai inductive bias and knowledge
 
Expert Anesthesia Monitoring System Final Presentation
Expert Anesthesia Monitoring System Final PresentationExpert Anesthesia Monitoring System Final Presentation
Expert Anesthesia Monitoring System Final Presentation
 
Networks and Natural Language Processing
Networks and Natural Language ProcessingNetworks and Natural Language Processing
Networks and Natural Language Processing
 
Categorization of Semantic Roles for Dictionary Definitions
Categorization of Semantic Roles for Dictionary DefinitionsCategorization of Semantic Roles for Dictionary Definitions
Categorization of Semantic Roles for Dictionary Definitions
 
Expert systems
Expert systemsExpert systems
Expert systems
 

Similar to Presentation1

(2018.9) 分子のグラフ表現と機械学習
(2018.9) 分子のグラフ表現と機械学習(2018.9) 分子のグラフ表現と機械学習
(2018.9) 分子のグラフ表現と機械学習
Ichigaku Takigawa
 
NanoAgents: Molecular Docking Using Multi-Agent Technology
NanoAgents: Molecular Docking Using Multi-Agent TechnologyNanoAgents: Molecular Docking Using Multi-Agent Technology
NanoAgents: Molecular Docking Using Multi-Agent Technology
CSCJournals
 
Citizen science project list (Europe & worldwide) v1
Citizen science project list (Europe & worldwide) v1Citizen science project list (Europe & worldwide) v1
Citizen science project list (Europe & worldwide) v1
Egle Marija Ramanauskaite
 
2014 11-13-sbsm032-reproducible research
2014 11-13-sbsm032-reproducible research2014 11-13-sbsm032-reproducible research
2014 11-13-sbsm032-reproducible research
Yannick Wurm
 
io-Chem-BD, una solució per gestionar el Big Data en Química Computacional
io-Chem-BD, una solució per gestionar el Big Data en Química Computacionalio-Chem-BD, una solució per gestionar el Big Data en Química Computacional
io-Chem-BD, una solució per gestionar el Big Data en Química Computacional
CSUC - Consorci de Serveis Universitaris de Catalunya
 
Possibilities for integrating model-related data in computational biology (DI...
Possibilities for integrating model-related data in computational biology (DI...Possibilities for integrating model-related data in computational biology (DI...
Possibilities for integrating model-related data in computational biology (DI...
University Medicine Greifswald
 
Chemical Semantics at Sopron CC Conference
Chemical Semantics at Sopron CC Conference Chemical Semantics at Sopron CC Conference
Chemical Semantics at Sopron CC Conference
sopekmir
 
Chemical Semantics Sopron Talk
Chemical Semantics Sopron TalkChemical Semantics Sopron Talk
Chemical Semantics Sopron Talk
sopekmir
 
Data compression with Python: application of different algorithms with the us...
Data compression with Python: application of different algorithms with the us...Data compression with Python: application of different algorithms with the us...
Data compression with Python: application of different algorithms with the us...
Alex Camargo
 
Reproducible research in molecular biophysics and structural biology
Reproducible research in molecular biophysics and structural biologyReproducible research in molecular biophysics and structural biology
Reproducible research in molecular biophysics and structural biology
khinsen
 
Recent Advancements in DNA Computing
Recent Advancements in DNA ComputingRecent Advancements in DNA Computing
Recent Advancements in DNA Computing
MangaiK4
 
1_Ocean Modellling Introduction.pdf
1_Ocean Modellling Introduction.pdf1_Ocean Modellling Introduction.pdf
1_Ocean Modellling Introduction.pdf
MDHASIBULHASANHRIDOY
 
Ag04602228232
Ag04602228232Ag04602228232
Ag04602228232
IJERA Editor
 
Knowledge Graphs for Scholarly Communication
Knowledge Graphs for Scholarly CommunicationKnowledge Graphs for Scholarly Communication
Knowledge Graphs for Scholarly Communication
Leipziger Semantic Web Tag
 
CSCW in Times of Social Media
CSCW in Times of Social MediaCSCW in Times of Social Media
CSCW in Times of Social Media
Hendrik Drachsler
 
Blind trials of computer-assisted structure elucidation software
Blind trials of computer-assisted structure elucidation softwareBlind trials of computer-assisted structure elucidation software
Blind trials of computer-assisted structure elucidation software
US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
 
The interplay between data-driven and theory-driven methods for chemical scie...
The interplay between data-driven and theory-driven methods for chemical scie...The interplay between data-driven and theory-driven methods for chemical scie...
The interplay between data-driven and theory-driven methods for chemical scie...
Ichigaku Takigawa
 
Chemoinformatic File Format.pptx
Chemoinformatic File Format.pptxChemoinformatic File Format.pptx
Chemoinformatic File Format.pptx
wadhava gurumeet
 
Software Sustainability: Better Software Better Science
Software Sustainability: Better Software Better ScienceSoftware Sustainability: Better Software Better Science
Software Sustainability: Better Software Better Science
Carole Goble
 
V5_I2_2016_Paper11.docx
V5_I2_2016_Paper11.docxV5_I2_2016_Paper11.docx
V5_I2_2016_Paper11.docx
IIRindia
 

Similar to Presentation1 (20)

(2018.9) 分子のグラフ表現と機械学習
(2018.9) 分子のグラフ表現と機械学習(2018.9) 分子のグラフ表現と機械学習
(2018.9) 分子のグラフ表現と機械学習
 
NanoAgents: Molecular Docking Using Multi-Agent Technology
NanoAgents: Molecular Docking Using Multi-Agent TechnologyNanoAgents: Molecular Docking Using Multi-Agent Technology
NanoAgents: Molecular Docking Using Multi-Agent Technology
 
Citizen science project list (Europe & worldwide) v1
Citizen science project list (Europe & worldwide) v1Citizen science project list (Europe & worldwide) v1
Citizen science project list (Europe & worldwide) v1
 
2014 11-13-sbsm032-reproducible research
2014 11-13-sbsm032-reproducible research2014 11-13-sbsm032-reproducible research
2014 11-13-sbsm032-reproducible research
 
io-Chem-BD, una solució per gestionar el Big Data en Química Computacional
io-Chem-BD, una solució per gestionar el Big Data en Química Computacionalio-Chem-BD, una solució per gestionar el Big Data en Química Computacional
io-Chem-BD, una solució per gestionar el Big Data en Química Computacional
 
Possibilities for integrating model-related data in computational biology (DI...
Possibilities for integrating model-related data in computational biology (DI...Possibilities for integrating model-related data in computational biology (DI...
Possibilities for integrating model-related data in computational biology (DI...
 
Chemical Semantics at Sopron CC Conference
Chemical Semantics at Sopron CC Conference Chemical Semantics at Sopron CC Conference
Chemical Semantics at Sopron CC Conference
 
Chemical Semantics Sopron Talk
Chemical Semantics Sopron TalkChemical Semantics Sopron Talk
Chemical Semantics Sopron Talk
 
Data compression with Python: application of different algorithms with the us...
Data compression with Python: application of different algorithms with the us...Data compression with Python: application of different algorithms with the us...
Data compression with Python: application of different algorithms with the us...
 
Reproducible research in molecular biophysics and structural biology
Reproducible research in molecular biophysics and structural biologyReproducible research in molecular biophysics and structural biology
Reproducible research in molecular biophysics and structural biology
 
Recent Advancements in DNA Computing
Recent Advancements in DNA ComputingRecent Advancements in DNA Computing
Recent Advancements in DNA Computing
 
1_Ocean Modellling Introduction.pdf
1_Ocean Modellling Introduction.pdf1_Ocean Modellling Introduction.pdf
1_Ocean Modellling Introduction.pdf
 
Ag04602228232
Ag04602228232Ag04602228232
Ag04602228232
 
Knowledge Graphs for Scholarly Communication
Knowledge Graphs for Scholarly CommunicationKnowledge Graphs for Scholarly Communication
Knowledge Graphs for Scholarly Communication
 
CSCW in Times of Social Media
CSCW in Times of Social MediaCSCW in Times of Social Media
CSCW in Times of Social Media
 
Blind trials of computer-assisted structure elucidation software
Blind trials of computer-assisted structure elucidation softwareBlind trials of computer-assisted structure elucidation software
Blind trials of computer-assisted structure elucidation software
 
The interplay between data-driven and theory-driven methods for chemical scie...
The interplay between data-driven and theory-driven methods for chemical scie...The interplay between data-driven and theory-driven methods for chemical scie...
The interplay between data-driven and theory-driven methods for chemical scie...
 
Chemoinformatic File Format.pptx
Chemoinformatic File Format.pptxChemoinformatic File Format.pptx
Chemoinformatic File Format.pptx
 
Software Sustainability: Better Software Better Science
Software Sustainability: Better Software Better ScienceSoftware Sustainability: Better Software Better Science
Software Sustainability: Better Software Better Science
 
V5_I2_2016_Paper11.docx
V5_I2_2016_Paper11.docxV5_I2_2016_Paper11.docx
V5_I2_2016_Paper11.docx
 

Recently uploaded

Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and MilvusBuilding Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
Zilliz
 
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Neo4j
 
Mariano G Tinti - Decoding SpaceX
Mariano G Tinti - Decoding SpaceXMariano G Tinti - Decoding SpaceX
Mariano G Tinti - Decoding SpaceX
Mariano Tinti
 
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
SOFTTECHHUB
 
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERPBest 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
Pixlogix Infotech
 
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
DianaGray10
 
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
名前 です男
 
20240607 QFM018 Elixir Reading List May 2024
20240607 QFM018 Elixir Reading List May 202420240607 QFM018 Elixir Reading List May 2024
20240607 QFM018 Elixir Reading List May 2024
Matthew Sinclair
 
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Neo4j
 
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of GermanyPresentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
innovationoecd
 
Infrastructure Challenges in Scaling RAG with Custom AI models
Infrastructure Challenges in Scaling RAG with Custom AI modelsInfrastructure Challenges in Scaling RAG with Custom AI models
Infrastructure Challenges in Scaling RAG with Custom AI models
Zilliz
 
Full-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalizationFull-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalization
Zilliz
 
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
SOFTTECHHUB
 
“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”
Claudio Di Ciccio
 
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the  Possible with Graph - Q2 2024GraphSummit Singapore | The Art of the  Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Neo4j
 
Removing Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software FuzzingRemoving Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software Fuzzing
Aftab Hussain
 
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Speck&Tech
 
GenAI Pilot Implementation in the organizations
GenAI Pilot Implementation in the organizationsGenAI Pilot Implementation in the organizations
GenAI Pilot Implementation in the organizations
kumardaparthi1024
 
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
DianaGray10
 
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Neo4j
 

Recently uploaded (20)

Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and MilvusBuilding Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
 
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
 
Mariano G Tinti - Decoding SpaceX
Mariano G Tinti - Decoding SpaceXMariano G Tinti - Decoding SpaceX
Mariano G Tinti - Decoding SpaceX
 
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
 
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERPBest 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
 
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
 
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
 
20240607 QFM018 Elixir Reading List May 2024
20240607 QFM018 Elixir Reading List May 202420240607 QFM018 Elixir Reading List May 2024
20240607 QFM018 Elixir Reading List May 2024
 
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
 
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of GermanyPresentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
 
Infrastructure Challenges in Scaling RAG with Custom AI models
Infrastructure Challenges in Scaling RAG with Custom AI modelsInfrastructure Challenges in Scaling RAG with Custom AI models
Infrastructure Challenges in Scaling RAG with Custom AI models
 
Full-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalizationFull-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalization
 
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
 
“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”
 
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the  Possible with Graph - Q2 2024GraphSummit Singapore | The Art of the  Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
 
Removing Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software FuzzingRemoving Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software Fuzzing
 
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
 
GenAI Pilot Implementation in the organizations
GenAI Pilot Implementation in the organizationsGenAI Pilot Implementation in the organizations
GenAI Pilot Implementation in the organizations
 
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
 
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
 

Presentation1

  • 3. OUTLINE 1. Meaning of Dendral 2. Inventors 3. History 4. Heuristic Dendral 5. Meta Dendral 6. Working 7. Plan-test-generate paradigm 8. Knowledge engineering 9. Disadvantages 10. Conclusion 8/13/2013 3
  • 4. Dendral was an influential pioneer project inartificial intelligence (AI) of the 1960s, and the computer software expert system that it produced.  The name Dendral is a portmanteaux of the term “Dendritic Algorithm“  Its primary aim was to help organic chemists in identifying unknown organic molecules, by analyzing their mass spectra using knowledge of chemistry. Meaning of Dendral 8/13/2013 4
  • 5. Inventors: It was done at Stanford University by Edward Feigenbaum, Bruce Buchanan, Joshua Lederberg (a Nobel prize winner in genetics) and Carl Djerassi. It began in 1965 and spans approximately half the history of AI research. 8/13/2013 5
  • 7. It was written in Lisp (programming language), which was considered language of AI. (john McCarthy, 1958, MIT) For this in laboratory, three generate and test 'method was used:-  (possible hypothesis about molecular structure are generated and tested by matching to actual data). There was an early realization that experts use certain heuristics to rule out certain options with possible structures. It seemed like a good idea to encode that knowledge in a software system 8/13/2013 7
  • 8. The software program Dendral is considered the first expert system because it automated the decision-making process and problem-solving behavior of organic chemists. DENDRAL marked a major paradigm shift´ in AI: a shift from general AI: a shift from genera -- Purpose, knowledge purpose, knowledge -- Sparse weak methods to domain sparse weak methods -- Specific, knowledge -- Intensive techniques intensive techniques History 8/13/2013 8
  • 9. The aim of the project was to develop a computer program to attain the level of computer program to attain the level of performance of an experienced human chemist. The DENDRAL project originated from the fundamental idea of Expert system 8/13/2013 9
  • 10. Dendral It consists of two sub-programs:  Heuristic-Dendral  Meta-Dendral 8/13/2013 10
  • 11.  Heuristic Dendral Heuristic Dendral is a program that uses mass spectra with knowledge base of chemistry, to produce a set of possible chemical structures that may be responsible for producing the data. . As the weight increases the molecules become more complex, the number of possible compounds increases drastically. (Example: H2O) 8/13/2013 11
  • 12. Meta-Dendral Input - 1) The set of possible chemical structures 2) Corresponding mass spectra Output- Explain correlation between proposed structures & mass spectrum. Thus, Heuristic Dendral is a performance system Meta Dendral is a learning system. The program is based on two important features: 1) the plan-generate-test paradigm 2) knowledge engineering. 8/13/2013 12
  • 14. PLAN – GENERATE - TEST PARADIGM  It is a problem solving method used by both M.D. & H.D.  Aim-When there are large number of possibilities at that it find a way to put constraints that rules out large sets of candidate solution.  “Hypothesis Formation Programe” “Task specific knowledge” 8/13/2013 14
  • 15. EXAMPLE-CONGEN Available Information-  C1: Empirical Formula- C12H14O.  C2: Compounds Contains Keto group.  C3: Three protons to the carbonyl group.  C4: There are two vinyl group.  C5: There is no conjugation.  C6: There are no diallylic protons.  C7: There are no additional multiple bonds.  C8: There are no methyl group. 8/13/2013 15
  • 16. PROCEDURE  #DEFINE MOLFORM C12H14O.  #DEFINE SUBSTRUCTURE Z  #DEFINE SUBSTRUCTURE CH3.  #DEFINE SUBSTRUCTURE V.  #GENERATE  #DRAW ATNAMED1  #AMBEDED  #DRAW ATNAMED1  .  .  .  #EXIT 8/13/2013 16
  • 18. KNOWLEDGE ENGINEERING  Aim- To attain a productive interaction between the available knowledge base & problem solution techniques.  The first essential component of K.E. is “knowledge base”.  It is used to determine the set of possible chemical structures that corresponds to the input data  & form new general rules that helps it reduce the number of candidate solutions. 8/13/2013 18
  • 19. Derived systems: Many systems were derived from Dendral, including:- 1. MYCIN 2. MOLGEN 3. MACSYMA 4. PROSPECTOR 5. XCON, and STEAMER 8/13/2013 19
  • 20. It is a system that does not guarantee about the solution, but reduces the number of possible solution by discarding unlikely & irrelevant solution. Conclusion 8/13/2013 20