Artificial Intelligence lecture notes. AI summarized notes for knowledge reasoning and knowledge representation, its for you in order for reading and may be for self-learning, I think.

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