The document discusses knowledge representation and reasoning in AI, emphasizing the need for a synthetic model to replicate human knowledge capabilities. It outlines various types of knowledge and formal representation techniques, such as facts, rules, semantic networks, frames, and logic. Additionally, it addresses limitations of traditional propositional logic and introduces predicate calculus for more complex reasoning and proof capabilities.

1. Knowledge Representation
and
Reasoning

2. The AI Cycle

3. The dilemma
• How to approach the problem?
• should we try to emulate the human brain completely and exactly as it is?
• Or should we come up with something new?
• Do we know how the KR and reasoning components are implemented in
humans ? No
• we need a synthetic (artificial) way to model the knowledge
representation and reasoning capability of humans in computers.

4. Knowledge and its Types
• Knowledge is the Understanding of a subject area
• knowledge domain : a well-focused subject area
• Eg. medical domain, engineering domain, business domain, etc

5. Categories of knowledge

6. Intuitive Ways of Knowledge Representation
• Pictures
• they provide a high level view of
a concept to be obtained
• Graphs and networks
• allow relationships between
objects/entities to be incorporated
• Numbers
• integral part of knowledge
representation used by humans.

7. Formal KR techniques
• Choosing the proper knowledge representation helps in reasoning. =>
‘Knowledge is Power’.
• Formal KR techniques includes
• Facts
• Rules
• Semantic networks
• Frames
• Logic

8. Facts
• basic block of knowledge (the atomic units of knowledge).
• represent declarative knowledge (they declare knowledge about objects).
• A proposition is the statement of a fact.
• Each proposition has an associated truth value. It may be either true or false.
• In AI, to represent a fact, we use a proposition and its associated truth value.
eg.
–Proposition A: It is raining
–Proposition B: I have an umbrella
–Proposition C: I will go to school

9. Types of facts
• Single-valued or multiple –valued
• Uncertain facts
• Fuzzy facts
• Object-Attribute-Value triplets

10. Rules
• Rule: A knowledge structure that relates some known information to
other information that can be concluded or inferred to be true.
• Components of a rule
• A rule consists of two components
• Antecedent or premise or the IF part
• Consequent or conclusion or the THEN part
• Example

11. Compound Rules
• Multiple premises or antecedents may be joined using AND
(conjunctions) and OR (disjunctions), e.g.

12. Types of Rules
• Relationship
• Recommendation
• Directive
• Variable Rules
• Uncertain Rules
• Meta Rules
• Rule Sets

13. Semantic networks
• Semantic networks are graphs, with
• nodes representing objects and
• arcs representing relationships between
objects.
• The two most common types of
relationships are
• IS-A (Inheritance relation)
• HAS (Ownership relation)

14. Vehicle Semantic Network

15. Problems with Semantic Networks
• Semantic networks are computationally expensive at run-time
• They try to model human associative memory (store information
using associations),
• Semantic networks are logically inadequate as they do not have
any equivalent quantifiers, e.g., for all, for some, none.

16. Frames
• Frames are data structures for representing stereotypical
knowledge of some concept or object

17. Inheritance
The specific frame inherits properties from the more general
frame,
Example of a Frame-Based Decision-Support System: HELP

18. FACETS
• A slot in a frame can hold more that just a value, it consists of
metadata and procedures.
• The various aspects of a slot are called facets.
• They are a feature of frames that allows us to put constraints on
frames.
• e.g. IF-NEEDED Facets are called when the data of a particular slot is
needed.
• Similarly, IFCHANGED Facets are when the value of a slot changes.

19. Logic
• propositional logic and predicate calculus are forms of formal
logic for dealing with propositions.
• Two basic logic representation techniques:
• Propositional Logic
• Predicate Calculus

20. Propositional logic
• A proposition is the statement of a fact.
• We usually assign a symbolic variable to represent a proposition, e.g.
• A proposition is a sentence whose truth values may be determined.
• So, each proposition has a truth value, e.g.
–The proposition ‘A rectangle has four sides’ is true
–The proposition ‘The world is a cube’ is false.

21. Compound Statements
Logical Connectives

22. Limitations of Propositional Logic
• Propositions can only represent knowledge as complete sentences, e.g.
a = the ball’s color is blue.
• Cannot analyze the internal structure of the sentence.
• No quantifiers are available, e.g. for-all, there-exists
• Propositional logic provides no framework for proving statements
such as:
All humans are mortal
All women are humans
Therefore, all women are mortals
This is a limitation in its representational power.

23. Predicate calculus
• Predicate Calculus is an extension of propositional logic that allows
the structure of facts and sentences to be defined.
• With predicate logic, we can use expressions like
Color( ball, blue)
• predicate calculus provides a mechanism for proving
statements and can be used as a logic system for proving logical
theorems

24. Quantifiers
• The Universal quantifier
Eg.
• Existential quantifier
Eg.

25. First Order Predicate Logic