UNIT – 3
Knowledge Representation:
Type of Knowledge;
Knowledge Representation Issue;
Logic:
First order Predicate Logic;
Representation of facts in FOL;
Interface of FOL;
Resolution Principle,
Clausal Form and Unification;
Interface Mechanisms:
Forword and Backward Chaining;
Slot and Filler Structures:
Semantic Networks;
Frame Systems and value inheritance;
Conceptual Dependency;
Scripts;
Q1. What is knowledge? Mca 2022
Q2. Define logical reasoning #
Q1.
Q2.
Q1.
Q1. What are the two ways to represent knowledge in ai system? Mca 2021
Q2. What is the purpose of unification?
Q1. What is resolution principle in ai?
Q2. What is frame system in ai ? why frame are used in ai?
Q1. Compare forward and backward chaining.
Q1. What is knowledge agent? Mca 2021 fifth sem.
Q2. What is semantic network.
Q1. Write a note on “ knowledge representation”.
Q2. Explain first order predicate logic.
Q1. What is conceptual dependencies? What are the various primitives concept in
conceptual dependencies.
Q1. What are frames? How do the differ from semantic nets. Msc 2022
Q2. Compare forward and backward chain.
Q1. What is the significance of knowledge representation? Write the characteristics of
knowledge representation.
Q2. Express the following sentence as conceptual dependency structure:
1. Bill is programmer
2. Sam gave marry a box of candy
Q1. What are scripts? Construct a script for a “fast food restaurant”.
Q2. Explain first order predicate logic.
Q1. What is knowledge? Mca fifth 2022
Q2. Define forward chaining.
Q1. What are frames? Explain the descriptive frame.
Q2. Explain conceptual dependency.
Q1.explain first order predicate logic.
Q1. What is knowledge representation? MSC 2021
Q2. What is frame system?
Q1. Explain first order predicate logic? Msc 2021
Q2. What is conceptual dependency?
Q1. What is semantic network? Explain frame system and value inheritance with
examples.
Or
What is frame representation? Explain syntax and numeric function in LISP.
Q1. Q1. Give the expansion of LISP and PROLOG. Msc atkt 2019 #
Q2. Define sematic networks.
Q1. Give the definition of Horn clause. #
Q2. Explain some of the knowledge representation techniques.
Q1. Explain in detail about frame representation.
Or
Explain syntax and Numerical function in LISP.#
MCA
2021
Knowledge Representation:
Knowledge Representation in AI describes the representation of knowledge.
One of the primary purposes of Knowledge Representation includes modeling intelligent
behavior for an agent.
It is responsible for representing information about the real world so that a computer can
understand and can utilize this knowledge to solve the complex real world problems
Knowledge representation in AI is not just about storing data in a database, it allows a
machine to learn from that knowledge and behave intelligently like a human being.
The different kinds of knowledge that need to be represented in AI include:
Objects
Events
Performance
Facts
Meta-Knowledge
Knowledge-base
Type of Knowledge :-
Following are the various types of knowledge:
1. Declarative Knowledge:
Declarative knowledge is to know about something.
It includes concepts, facts, and objects.
2. Procedural Knowledge :
It is also known as imperative knowledge.
Procedural knowledge is a type of knowledge which is responsible for knowing how to
do something.
3. Meta-knowledge:
Knowledge about the other types of knowledge is called Meta-knowledge.
4. Heuristic knowledge:
Heuristic knowledge is rules of thumb based on previous experiences, awareness of
approaches, and which are good to work but not guaranteed.
5. Structural knowledge:
Structural knowledge is basic knowledge to problem-solving.
It describes the relationship that exists between concepts or objects.
Knowledge Representation Issue:-
Logic:
First order Predicate Logic :-
First-order logic is another way of knowledge representation in artificial intelligence.
It is an extension to propositional logic.
FOL is sufficiently expressive to represent the natural language statements in a concise
way.
First-order logic is also known as Predicate logic or First-order predicate logic.
First-order logic is a powerful language that develops information about the objects in a more
easy way and can also express the relationship between those objects.
Unlike propositional logic, First Order Logic in Artificial Intelligence doesn't only
include facts but also different other entities as listed below.
First Order Logic assumes the world contains Object, Relations, and functions.
1.Object:
Objects can denote any real-world entity or any variable.
E.g., A, B, colours, theories, circles etc.
2.Relations:
Relations represent the links between different objects. Relations can be
unary(relations defined for a single term) and n-ary(relations defined for n
terms).
3.Functions:
Functions map their input object to the output object using their underlying
relation. Eg: father_of(), mother_of() etc.
Parts of First Order Logic
First-order logic in Artificial Intelligence comprises two main components, which are as
follows.
1. Syntax:
Syntax represents the rules to write expressions in First Order Logic in Artificial
Intelligence.
2. Semantics:
Semantics refers to the techniques that we use to evaluate an expression of First Order
Logic in AI.
Representation of facts in FOL;
In AI, the representation of facts in FOL involves expressing knowledge about the
world using logical statements.
These statements are structured using variables, constants, predicates, and
quantifiers.
Below is an overview of how facts are represented in FOL:
1. Atom and Predicates:
Atom: Basic units of FOL statements, combining constants and predicates.
These are basic assertions about the world.
Predicates: Predicates are symbols that denote relationships or
properties.
Example:
The statement “Cat (Tom) represents the fact that Tom is a cat.
Here, “Cat” is a predicate, and “Tom” is a constant.
Interface of FOL;
Resolution Principle,
Resolution is a method of theorem proof that involves constructing refutation
proofs, or proofs by contradictions.
It was created in 1965 by a mathematician named John Alan Robinson.
Resolution is used, if there are various statements are given, and we need to
prove a conclusion of those statements.
Unification is a key concept in proofs by resolutions.
Resolution is a single inference rule which can efficiently operate on
the conjunctive normal form or clausal form.
Clause:
A clause is a disjunction on literals (an atomic sentence).
Conjunction Normal Form (CNF):
Conjunctive normal form (CNF) is a sentence that is represented as a
conjunction of clauses.
Clausal Form and Unification;
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.
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.
Interface Mechanisms:
Forward and Backward Chaining;
Forward Chaining:
Forward Chaining is a data-driven reasoning approach used by inference
engines.
It starts with the given facts and applies rules to derive new conclusions or facts
from them.
The engine keeps applying the rules until it reaches a conclusion.
Forward chaining can be used in planning, monitoring, controling, and
interpreting applications.
Properties of forward chaining
The process uses a down-up approach (bottom to top).
It starts from an initial state and uses facts to make a conclusion.
This approach is data-driven.
It’s employed in expert systems and production rule system.
Backward Chaining:
Backward Chaining is also called backward reasoning.
In this technique, the inference engine starts with the goal and works backwords
to find evidence that supports the goal.
In simple words, it works from the goal state and reaches the initial state.
Properties of backward chaining
The process uses an up-down approach (top to bottom).
It’s a goal-driven method of reasoning.
The endpoint (goal) is broken into sub-goals to prove the truth of
facts.
A backward chaining algorithm is employed in inference engines,
game theories, and complex database systems.
Difference between Forwarding Chaining and Backward Chaining:
Forward Chaining Backward Chaining
1.
Forward chaining is known as data- Backward chaining is known as goal-
driven technique because we driven technique because we start from
2.
reaches to the goal using the the goal and reaches the initial state in
available data. order to extract the facts.
3. It is a bottom-up approach. It is a top-down approach.
It applies the Breadth-First
4. It applies the Depth-First Strategy.
Strategy.
Its goal is to get the possible facts or the
5. Its goal is to get the conclusion.
required data.
6. Slow as it has to use all the rules. Fast as it has to use only a few rules.
It operates in forward direction i.e
It operates in backward direction i.e it
7. it works from initial state to final
works from goal to reach initial state.
decision.
Forward chaining is used for the It is used in automated inference engines,
8. planning, monitoring, control, and theorem proofs, proof assistants and
interpretation application. other artificial intelligence applications.
Slot and Filler Structures:
Semantic Networks;
In artificial intelligence, a semantic network is a knowledge
representation technique for organizing and storing knowledge.
Semantic networks are a type of graphical model that shows the
relationships between concepts, ideas, and objects in a way that
is easy for humans to understand.
The nodes in a semantic network are concepts, and the edges
between nodes represent the relationships between those
concepts.
Semantic networks are used to represent both simple and
complex knowledge structures.
it can help to improve the performance of AI systems by
providing a way to represent knowledge in a more efficient way.
Frame Systems and value inheritance;
Conceptual Dependency;
CD theory was developed by Schank in 1973 to 1975 to represent the meaning
of NL (Natural Language) sentences.
It helps in drawing interfaces.
It is independent of the language.
CD representation of a sentence is not built using words in the sentence rather
built using conceptual primitives which give the intended meaning of words.
CD provide structures and specific set of primitives from which representation
can be built.
Scripts;
