week 5 Flashcards
chp 5
Knowledge Representation (KR) definition, significance
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.
Knowledge Reasoning
The process by which an AI system draws
new conclusions from stored knowledge. Involves logical deduction,
inferencing rules, and decision-making.
What to Represent:
-object, events, performance, meta-knowledge, facts, knowledge-base
- 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).
Type of knowledge: type 1 :declarative knowledge
- 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.
type of knowledge: Type 2: Procedural Knowledge
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.
Type 3: Meta-knowledge
- Knowledge about the other types of knowledge is called Meta-
knowledge.
type of knowledge : Type 4: Heuristic Knowledge
- 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.
type of knowledge :Type 5: Structural Knowledge
- 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.
AI Knowledge Cycle
- An Artificial intelligence
system has the following
components for displaying
intelligent behavior:
- Perception
- Learning
- Knowledge
Representation and
Reasoning - Planning
- Execution
Approaches to Knowledge Representation :(1. Simple relational knowledge:, 2. Inheritable knowledge:, )
- 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.
Approaches to Knowledge Representation
- Inheritable knowledge:
- 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.
Approaches to Knowledge Representation
- Inferential knowledge:
- 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)
Approaches to Knowledge Representation
- Procedural knowledge:
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.
Techniques of Knowledge Representation( Logical Representation)
- 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
Techniques of Knowledge Representation (Semantic Networks)
- 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.
Techniques of Knowledge Representation (Frame Representation)
- 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.
Techniques of Knowledge Representation (Production Rules)
- 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.
First Order Logic (FOL)
- 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
Syntax of FOL
*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:
atomic sentence, complex sentence, first-order logic statements
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.
Quantifiers in FOL
- 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).
Inference in FOL
- FOL inference rules for quantifier:
- Universal Generalization
- Universal Instantiation
- Existential Instantiation
- Existential introduction
Reasoning in AI
- Deductive reasoning
- Inductive reasoning
- Abductive reasoning
- Common Sense Reasoning
- Monotonic Reasoning
- Non-monotonic Reasoning
Deductive Reasoning
- 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.
Inductive Reasoning
- 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.
Abductive Reasoning
- 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.
Common Sense Reasoning
- 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.
Monotonic Reasoning
- 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.
Non-Monotonic Reasoning
- 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.
Comparison of deductive reasoning and inductive reasoning (definition, approach, starts from, validity, usage, process, argument, structure)
draw a table explain it
predicate logic (briefly explain and give example)
use feyman technique to explain
propositional logic
use feyman technique to explain
