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AI technique > week 5 > Flashcards

week 5 Flashcards

chp 5

1
Q

Knowledge Representation (KR) definition, significance

A

The method of translating complex,
real-world information into a format that AI systems can utilize to
mimic “intelligent” behaviour.
Significance of KR
* Facilitates intelligent decision-making by providing the necessary information foundation.
* Acts as a precursor to reasoning—the ability of AI to make deductions and decisions.

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2
Q

Knowledge Reasoning

A

The process by which an AI system draws
new conclusions from stored knowledge. Involves logical deduction,
inferencing rules, and decision-making.

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3
Q

What to Represent:
-object, events, performance, meta-knowledge, facts, knowledge-base

A
  • Object:All the facts about objects in our world domain. E.g., Guitars contains
    strings, trumpets are brass instruments.
  • Events: Events are the actions which occur in our world.
  • Performance: It describe behavior which involves knowledge about how to do
    things.
  • Meta-knowledge: It is knowledge about what we know.
  • Facts: Facts are the truths about the real world and what we represent.
  • Knowledge-Base: The central component of the knowledge-based agents is the
    knowledge base. It is represented as KB. The Knowledgebase is a group of the
    Sentences (Here, sentences are used as a technical term and not identical with
    the English language).
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4
Q

Type of knowledge: type 1 :declarative knowledge

A
  • Declarative knowledge is to know about something.
  • It includes concepts, facts, and objects.
  • It is also called descriptive knowledge and expressed in declarative
    sentences.
  • It is simpler than procedural language.
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5
Q

type of knowledge: Type 2: Procedural Knowledge

A

It is also known as imperative knowledge.
* Procedural knowledge is a type of knowledge that is responsible for
knowing how to do something.
* It can be directly applied to any task.
* It includes rules, strategies, procedures, agendas, etc.
* Procedural knowledge depends on the task on which it can be
applied.

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6
Q

Type 3: Meta-knowledge

A
  • Knowledge about the other types of knowledge is called Meta-
    knowledge.
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7
Q

type of knowledge : Type 4: Heuristic Knowledge

A
  • Heuristic knowledge is representing knowledge of some experts in a
    filed or subject.
  • Heuristic knowledge is rules of thumb based on previous experiences,
    awareness of approaches, and which are good to work but not
    guaranteed.
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8
Q

type of knowledge :Type 5: Structural Knowledge

A
  • Structural knowledge is basic knowledge to problem-solving.
  • It describes relationships between various concepts such as kind of,
    part of, and grouping of something.
  • It describes the relationship that exists between concepts or objects.
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9
Q

AI Knowledge Cycle

  • An Artificial intelligence
    system has the following
    components for displaying
    intelligent behavior:
A
  • Perception
  • Learning
  • Knowledge
    Representation and
    Reasoning
  • Planning
  • Execution
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10
Q

Approaches to Knowledge Representation :(1. Simple relational knowledge:, 2. Inheritable knowledge:, )

A
  1. Simple relational knowledge:
    * It is the simplest way of storing facts which uses the relational method, and
    each fact about a set of the object is set out systematically in columns.
    * This approach of knowledge representation is famous in database systems
    where the relationship between different entities is represented.
    * This approach has little opportunity for inference.
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11
Q

Approaches to Knowledge Representation

  1. Inheritable knowledge:
A
  1. Inheritable knowledge:
    * In the inheritable knowledge approach, all data must be
    stored into a hierarchy of classes.
    * All classes should be arranged in a generalized form or a
    hierarchal manner.
    * In this approach, we apply inheritance property.
    * Elements inherit values from other members of a class.
    * This approach contains inheritable knowledge which shows
    a relation between instance and class, and it is called
    instance relation.
    * Every individual frame can represent the collection of
    attributes and its value.
    * In this approach, objects and values are represented in
    Boxed nodes.
    * We use Arrows which point from objects to their values.
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12
Q

Approaches to Knowledge Representation

  1. Inferential knowledge:
A
  1. Inferential knowledge:
    * Inferential knowledge approach represents knowledge in the form of formal
    logics.
    * This approach can be used to derive more facts.
    * It guaranteed correctness.
    * Example: Let’s suppose there are two statements:
    * Marcus is a man
    * All men are mortal
    * Then it can represent as;

man(Marcus)
∀x = man (x) ———-> mortal (x)

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13
Q

Approaches to Knowledge Representation

  • Procedural knowledge:
A

Procedural knowledge approach uses small programs and codes which
describes how to do specific things, and how to proceed.
* In this approach, one important rule is used which is If-Then rule.
* In this knowledge, we can use various coding languages such as LISP language
and Prolog language.
* We can easily represent heuristic or domain-specific knowledge using this
approach.
* But it is not necessary that we can represent all cases in this approach.

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14
Q

Techniques of Knowledge Representation( Logical Representation)

A
  1. Logical Representation
  • Syntax
  • Syntaxes are the rules that decide how we can construct legal sentences in
    the logic.
  • It determines which symbol we can use in knowledge representation.
  • How to write those symbols.
  • Semantics
  • Semantics are the rules by which we can interpret the sentence in the logic.
  • Semantic also involves assigning a meaning to each sentence.
  • Logical representation can be categorized into mainly two logics:
  • Propositional Logics
  • Predicate logics
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15
Q

Techniques of Knowledge Representation (Semantic Networks)

A
  1. Semantic Networks
  • This representation consists of mainly two types of relations:
  • IS-A relation (Inheritance)
  • Kind-of-relation
  • Example: The following are some statements that we need to
    represent in the form of nodes and arcs.
  • Statements:
    Jerry is a cat.
    Jerry is a mammal
    Jerry is owned by Priya.
    Jerry is brown colored.
    All Mammals are animal.
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16
Q

Techniques of Knowledge Representation (Frame Representation)

A
  1. Frame Representation
  • Consists of a collection of attributes and its
    values to describe an entity in the world.
  • Facets: The various aspects of a slot is
    known as Facets. Facets are features of
    frames which enable us to put constraints
    on the frames.
  • Example: IF-NEEDED facts are called when
    data of any particular slot is needed.
  • A frame is also known as slot-filter
    knowledge representation.
17
Q

Techniques of Knowledge Representation (Production Rules)

A
  • Production rules system consist of (condition, action) pairs which mean, “If
    condition then action”.
  • It has mainly three parts:
  • The set of production rules - checks for the condition and if the condition exists then
    production rule fires and corresponding action is carried out.
  • Working Memory - contains the description of the current state of problems-solving
    and rule can write knowledge to the working memory.
  • The recognize-act-cycle - The condition part of the rule determines which rule may

be applied to a problem. And the action part carries out the associated problem-
solving steps.

18
Q

First Order Logic (FOL)

A
  • First-order logic is also known as Predicate logic or First-order
    predicate logic.
  • First-order logic does not only assume that the world contains facts
    but also assumes the following things in the world:
  • Objects: A, B, people, numbers, colors, wars, theories, squares, pits, ……
  • Relations: It can be unary relation such as: red, round, is adjacent, or n-any
    relation such as: the sister of, brother of, has color, comes between
  • Function: Father of, best friend, third inning of, end of, ……
  • As a natural language, first-order logic also has two main parts:
  • Syntax
  • Semantics
19
Q

Syntax of FOL

A

*The syntax of FOL determines which collection of symbols is a logical
expression in first-order logic.
* Following are the basic elements of FOL syntax:

20
Q

atomic sentence, complex sentence, first-order logic statements

A

Atomic sentence
* Atomic sentences are the most basic sentences of first-order logic. These
sentences are formed from a predicate symbol followed by a parenthesis with
a sequence of terms.
* We can represent atomic sentences as Predicate (term1, term2, ……, term n).

Example: Ravi and Ajay are brothers: => Brothers(Ravi, Ajay).
Chinky is a cat: => cat (Chinky).

  • Complex sentence
  • Complex sentences are made by combining atomic sentences using
    connectives.
  • First-order logic statements can be divided into two parts:
  • Subject: Subject is the main part of the statement.
  • Predicate: A predicate can be defined as a relation, which binds two atoms
    together in a statement.
  • Consider the statement:
    “x is an integer.”
    ,

it consists of two parts, the first part x is the subject of the statement
and second part “is an integer,” is known as a predicate.

21
Q

Quantifiers in FOL

A
  • A quantifier is a language element which generates quantification,
    and quantification specifies the quantity of specimen in the universe
    of discourse.
  • These are the symbols that permit to determine or identify the range
    and scope of the variable in the logical expression. There are two
    types of quantifier:
  • Universal Quantifier, (for all, everyone, everything)
  • Existential quantifier, (for some, at least one).
22
Q

Inference in FOL

A
  • FOL inference rules for quantifier:
  • Universal Generalization
  • Universal Instantiation
  • Existential Instantiation
  • Existential introduction
23
Q

Reasoning in AI

A
  • Deductive reasoning
  • Inductive reasoning
  • Abductive reasoning
  • Common Sense Reasoning
  • Monotonic Reasoning
  • Non-monotonic Reasoning
24
Q

Deductive Reasoning

A
  • Deducing new information from logically related known information.
  • The argument’s conclusion must be true when the premises are true.
  • Referred to as top-down reasoning, and contradictory to inductive
    reasoning.

Example:
Premise-1: All the human eats veggies
Premise-2: Suresh is human.
—————————————————
Conclusion: Suresh eats veggies.

25
Q

Inductive Reasoning

A
  • A form of reasoning to arrive at a conclusion using limited sets of
    facts by the process of generalization.
  • Also known as cause-effect reasoning or bottom-up reasoning.

Example:
Premise: All of the pigeons we have seen in the zoo are white.
———————————————————————————-
Conclusion: Therefore, we can expect all the pigeons to be white.

26
Q

Abductive Reasoning

A
  • A form of logical reasoning which starts with single or multiple
    observations and then seeks to find the most likely explanation or
    conclusion for the observation.

Example:
Implication: Cricket ground is wet if it is raining
Axiom: Cricket ground is wet.

Conclusion: It is raining.

27
Q

Common Sense Reasoning

A
  • An informal form of reasoning, which can be gained through
    experiences.
  • It relies on good judgment rather than exact logic and operates
    on heuristic knowledge and heuristic rules.

Example:
1. One person can be at one place at a time.
2. If I put my hand in a fire, then it will burn.

28
Q

Monotonic Reasoning

A
  • Once the conclusion is taken, then it will remain the same even if we
    add some other information to existing information in our knowledge
    base.
  • Not useful for the real-time systems. Why?
  • Monotonic reasoning is used in conventional reasoning systems.

Example:
Earth revolves around the Sun.

29
Q

Non-Monotonic Reasoning

A
  • Some conclusions may be invalidated if we add some more information to
    our knowledge base.

Example: Let’s suppose the knowledge base contains the following
knowledge:
Birds can fly
Penguins cannot fly
Pitty is a bird
* So from the above sentences, we can conclude that Pitty can fly.
* However, if we add one another sentence into knowledge base “Pitty is a
penguin”, which concludes “Pitty cannot fly”, so it invalidates the above
conclusion.

30
Q

Comparison of deductive reasoning and inductive reasoning (definition, approach, starts from, validity, usage, process, argument, structure)

A

draw a table explain it

31
Q

predicate logic (briefly explain and give example)

A

use feyman technique to explain

32
Q

propositional logic

A

use feyman technique to explain

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