A talk I gave at the Yonsei University, Seoul in July 21st, 2015.
The aim was to show my background contribution to the CORCON (Correctness by Construction) research project.
I have to thank Prof. Byunghan Kim and Dr Gyesik Lee for their kind hospitality.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
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AN IMPLEMENTATION, EMPIRICAL EVALUATION AND PROPOSED IMPROVEMENT FOR BIDIRECT...ijaia
Abstract argumentation frameworks are formal systems that facilitate obtaining conclusions from nonmonotonic knowledge systems. Within such a system, an argumentation semantics is defined as a set of arguments with some desired qualities, for example, that the elements are not in conflict with each other.
Splitting an argumentation framework can efficiently speed up the computation of argumentation semantics. With respect to stable semantics, two methods have been proposed to split an argumentation framework either in a unidirectional or bidirectional fashion. The advantage of bidirectional splitting is
that it is not structure-dependent and, unlike unidirectional splitting, it can be used for frameworks consisting of a single strongly connected component. Bidirectional splitting makes use of a minimum cut. In this paper, we implement and test the performance of the bidirectional splitting method, along with two
types of graph cut algorithms. Experimental data suggest that using a minimum cut will not improve the performance of computing stable semantics in most cases. Hence, instead of a minimum cut, we propose to use a balanced cut, where the framework is split into two sub-frameworks of equal size. Experimental results conducted on bidirectional splitting using the balanced cut show a significant improvement in the performance of computing semantics.
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete RecordAsst.prof M.Gokilavani
LIST OF EXPERIMENTS:
1. Implement simple vector addition in Tensor Flow.
2. Implement a regression model in Keras.
3. Implement a perception in TensorFlow/Keras Environment.
4. Implement a Feed Forward Network in TensorFlow/Keras.
5. Implement an image classifier using CNN in TensorFlow/Keras.
6. Improve the deep Learning model by fine tuning hyper parameters.
7. Implement a Transfer Learning concept in image classification.
8. Using a pre trained model on Keras for transfer learning.
9. Perform Sentimental Analysis using RNN.
10. Implement an LSTM based Auto encoding inTensorflow/Keras.
11. Image generation using GAN.
ADDITIONAL EXPERIMENTS
12. Train a deep Learning model to classify a given image using pre trained model.
13. Recommendation system from sales data using Deep Learning.
14. Implement Object detection using CNN.
15. Implement any simple Reinforcement Algorithm for an NLP problem.
A talk I gave at the Yonsei University, Seoul in July 21st, 2015.
The aim was to show my background contribution to the CORCON (Correctness by Construction) research project.
I have to thank Prof. Byunghan Kim and Dr Gyesik Lee for their kind hospitality.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
AN IMPLEMENTATION, EMPIRICAL EVALUATION AND PROPOSED IMPROVEMENT FOR BIDIRECT...ijaia
Abstract argumentation frameworks are formal systems that facilitate obtaining conclusions from nonmonotonic knowledge systems. Within such a system, an argumentation semantics is defined as a set of arguments with some desired qualities, for example, that the elements are not in conflict with each other.
Splitting an argumentation framework can efficiently speed up the computation of argumentation semantics. With respect to stable semantics, two methods have been proposed to split an argumentation framework either in a unidirectional or bidirectional fashion. The advantage of bidirectional splitting is
that it is not structure-dependent and, unlike unidirectional splitting, it can be used for frameworks consisting of a single strongly connected component. Bidirectional splitting makes use of a minimum cut. In this paper, we implement and test the performance of the bidirectional splitting method, along with two
types of graph cut algorithms. Experimental data suggest that using a minimum cut will not improve the performance of computing stable semantics in most cases. Hence, instead of a minimum cut, we propose to use a balanced cut, where the framework is split into two sub-frameworks of equal size. Experimental results conducted on bidirectional splitting using the balanced cut show a significant improvement in the performance of computing semantics.
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
Similar to AI_session 22 inference and unification.pptx (20)
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete RecordAsst.prof M.Gokilavani
LIST OF EXPERIMENTS:
1. Implement simple vector addition in Tensor Flow.
2. Implement a regression model in Keras.
3. Implement a perception in TensorFlow/Keras Environment.
4. Implement a Feed Forward Network in TensorFlow/Keras.
5. Implement an image classifier using CNN in TensorFlow/Keras.
6. Improve the deep Learning model by fine tuning hyper parameters.
7. Implement a Transfer Learning concept in image classification.
8. Using a pre trained model on Keras for transfer learning.
9. Perform Sentimental Analysis using RNN.
10. Implement an LSTM based Auto encoding inTensorflow/Keras.
11. Image generation using GAN.
ADDITIONAL EXPERIMENTS
12. Train a deep Learning model to classify a given image using pre trained model.
13. Recommendation system from sales data using Deep Learning.
14. Implement Object detection using CNN.
15. Implement any simple Reinforcement Algorithm for an NLP problem.
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdfAsst.prof M.Gokilavani
UNIT I INTRODUCTION
Neural Networks-Application Scope of Neural Networks-Artificial Neural Network: An IntroductionEvolution of Neural Networks-Basic Models of Artificial Neural Network- Important Terminologies of
ANNs-Supervised Learning Network.
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Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
1. ARTIFICAL INTELLIGENCE
(R18 III(II Sem))
Department of computer science and
engineering (AI/ML)
Session 22
by
Asst.Prof.M.Gokilavani
VITS
4/25/2023 Dpaertment of CSE ( AL & ML) 1
2. TEXTBOOK:
• Artificial Intelligence A modern Approach, Third
Edition, Stuart Russell and Peter Norvig, Pearson
Education.
REFERENCES:
• Artificial Intelligence, 3rd Edn, E. Rich and K.Knight
(TMH).
• Artificial Intelligence, 3rd Edn, Patrick Henny
Winston, Pearson Education.
• Artificial Intelligence, Shivani Goel, Pearson
Education.
• Artificial Intelligence and Expert Systems- Patterson,
Pearson Education.
4/25/2023 Dpaertment of CSE ( AL & ML) 2
3. Topics covered in session 22
4/25/2023 Dpaertment of CSE ( AL & ML) 3
Logic and Knowledge Representation
First-Order Logic: Representation, Syntax and
Semantics of First-Order Logic, Using First-Order Logic,
Knowledge Engineering in First-Order Logic.
Inference in First-Order Logic: Propositional vs. First-
Order Inference, Unification and Lifting, Forward
Chaining, Backward Chaining, Resolution.
Knowledge Representation: Ontological Engineering,
Categories and Objects, Events. Mental Events and
Mental Objects, Reasoning Systems for Categories,
Reasoning with Default Information.
4. Propositional vs. First-Order Inference
Propositional logic
• Propositional logic(PL) is a statement made by
propositions.
• It is a simple form of logic.
• Propositions are declarative statements which are
either true or false.
• PL is a technique often used in logical and
mathematical form.
• When we study PL, we usually start with formal
natural language arguments, but they can also be
expressed mathematically.
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Dpaertment of CSE ( AL & ML)
5. • Propositional logic is based on formal logic, deductive reason
and Boolean logic.
• We use symbolic variables to represent the logic. Often true
and false can be symbolized by 1 and 0.
• Propositional logic also consists of an object, relations or
function, and logical connectives.
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6. First order logic
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Dpaertment of CSE ( AL & ML)
• First-order logic(FOL), Predicate logic or First-order
predicate logic is an extension to propositional logic.
• First-order logic expresses information about objects and
expresses relationships between those objects in a more
functional way in comparison to propositional logic.
• First-order logic is more concise than propositional logic.
• In First-order logic, the statements are divided into two
parts: the subject and the predicate.
• The predicate is not a proposition.
• It is neither true nor false.
• Predicates use variables and objects like people, colors,
numbers, letters or ideas.
• They can also represent relationships and functions
7. • Both syntax and semantics are important to first-
order logic.
• Symbols are the basic syntax of FOL and can be
written in shorthand.
• Syntax is the structure of the logical statements.
Semantics gives meaning to the statements.
Examples:
• Every man has a heart = ∀x man(x) → have (x,
heart).
• Some men are mean = ∃x: man(x) ∧ mean(x).
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Dpaertment of CSE ( AL & ML)
8. What is Unification?
• Unification is a process of making two different
logical atomic expressions identical by finding a
substitution.
• Unification depends on the substitution process.
• It takes two literals as input and makes them
identical using substitution.
• Let Ψ1 and Ψ2 be two atomic sentences and 𝜎 be a
unifier such that, Ψ1𝜎 = Ψ2𝜎, then it can be
expressed as UNIFY(Ψ1, Ψ2).
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Dpaertment of CSE ( AL & ML)
9. Example: Find the MGU for Unify{King(x), King(John)}
Let Ψ1 = King(x), Ψ2 = King(John),
Substitution: θ = {John/x} is a unifier for these atoms and
applying this substitution, and both expressions will be
identical.
• The UNIFY algorithm is used for unification, which takes
two atomic sentences and returns a unifier for those
sentences (If any exist).
• Unification is a key component of all first-order inference
algorithms.
• It returns fail if the expressions do not match with each
other.
• The substitution variables are called Most General Unifier
or MGU.
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10. Example: Let's say there are two different
expressions, P(x, y), and P(a, f(z)).
In this example, we need to make both above
statements identical to each other.
For this, we will perform the substitution.
P(x, y)......... (i)
P(a, f(z))......... (ii)
• Substitute x with a, and y with f(z) in the first
expression, and it will be represented as a/x and
f(z)/y.
• With both the substitutions, the first expression
will be identical to the second expression and the
substitution set will be: [a/x, f(z)/y].
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Dpaertment of CSE ( AL & ML)
11. Conditions for Unification
Following are some basic conditions for
unification:
• Predicate symbol must be same, atoms or
expression with different predicate symbol can
never be unified.
• Number of Arguments in both expressions
must be identical.
• Unification will fail if there are two similar
variables present in the same expression.
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13. Implementation of the Algorithm
• Step 1: Begin by making the substitute set empty.
Step 2: Unify atomic sentences in a recursive manner:
a. Check for expressions that are identical.
b. If one expression is a variable vΨi, and the other
is a term ti which does not contain variable vi, then:
i. Substitute ti / vi in the existing substitutions
ii. Add ti / vi to the substitution set list.
Iii. If both the expressions are functions, then
function name must be similar, and the number of
arguments must be the same in both the expression.
c. Find the most general unifier for each pair of the
following atomic statements (If exist).
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14. Example 1
Find the MGU of {p(f(a), g(Y)) and p(X, X)}.
Sol: S0 => Here,
• Ψ1 = p(f(a), g(Y)), and
• Ψ2 = p(X, X)
SUBST θ = {f(a) / X}
• S1 => Ψ1 = p(f(a), g(Y)), and Ψ2 = p(f(a), f(a))
SUBST θ = {f(a) / g(y)},
Unification failed.
Unification is not possible for these expressions.
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16. Example 3
Find the MGU of {p (X, X), and p (Z, f(Z))}
Sol: Here, Ψ1 = {p (X, X)
Ψ2 = p (Z, f(Z))
S0 => {p (X, X), p (Z, f(Z))}
SUBST θ= {X/Z}
S1 => {p (Z, Z), p (Z, f(Z))}
SUBST θ= {f(Z) / Z},
Unification Failed.
Therefore, unification is not possible for
these expressions.
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Dpaertment of CSE ( AL & ML)