artificial intelligence

1. Subject : Artificial Intelligence
Topic : Knowledge Representation
Prepared By.
Prof. Syed Rehan
Anjuman College Of Engg. &
Tech.
Department Of Computer Science

2.  Introduction: What is AI? History & Applications, Artificial intelligence as
representation & Search, Production system, Basics of problem solving:
problem representation paradigms, defining problem as a state space
representation, Characteristics.
 Search Techniques: Uninformed Search techniques, Informed Heuristic
Based Search, Generate and test, Hill-climbing, Best-First Search, Problem
Reduction, and Constraint Satisfaction.
 Knowledge representation: Knowledge representation Issues: First order
logic, Predicate Logic, Structured Knowledge Representation: Backward
Chaining , Backward Chaining , Resolution ,Semantic Nets, Frames, and
Scripts, Ontology.
 Uncertainty: Handing uncertain knowledge, rational decisions, basics of
probability, axioms of probability, Baye’s Rule and conditional independence
, Bayesian networks , Exact and Approximate inference in Bayesian
Networks, Fuzzy Logic .
 Learning: What is learning?, Knowledge and learning, Learning in Problem
Solving, Learning from example, learning probabilistic models, Formal
Learning Theory
Syllabus (Artificial Intelligence – 6th Sem CSE

3.  Explain the concept behind problem representation
paradigms & its characteristics, production system and
defining problem as a state space representation.
 Analyse various AI search algorithms (uninformed,
informed, heuristic, constraint satisfaction, best-first search,
problem reduction
 Explain the fundamentals of knowledge representation
(logic-based, frame-based, semantic nets), inference and
theorem proving ,Know how to build simple knowledge-
based systems.
 Demonstrate working knowledge of reasoning in the
presence of incomplete and/or uncertain information by
applying Bayesian Networks and Fuzzy Logic.
 Apply learning in problem solving , learning probabilistic
models.
Course Outcome

4.  Knowledge Representation Issues
 Semantic Networks
 FRAMES
 Scripts
Contents:-

5.  It becomes clear that particular knowledge
representation models allow for more specific more
powerful problem solving mechanisms that operate on
them.
 Examine specific techniques that can be used for
representing & manipulating knowledge within
programs.
 Representation & Mapping
 Facts :- truths in some relevant world
 These are the things we want to represent.
 Representations of facts in some chosen formalism.
 Things we are actually manipulating. Structuring these
entities is as two levels.
 The knowledge level, at which facts concluding each
agents behavior & current goals are described.
Knowledge Representation Issues
Internal Representations
Facts
English Representations
English understanding English
generation

6.  Representational adequacy the ability to
represent all of the kinds of knowledge that are
needed in that domain.
 Inferential Adequacy: - the ability to manipulate
the representation structures in such a way as to
derive new structures corresponding to new
knowledge inferred from ol.
 Inferential Efficiency: - the ability to incorporate
into the knowledge structure additional
information that can be used to focus the
attention of the inference mechanism in the most
promising directions.
 Acquisitioned Efficiency: - the ability to acquire
new information easily. The simplest case
involves direct insertion by a person of new
Approaches to knowledge Representation.

7.  Semantic Network is a structure for representing
knowledge as a pattern of interconnected nodes
and arcs. It is also defined as a graphical
representation of knowledge.
 The objects under consideration serves as nodes
& the relationships with another node give the
arcs.
 Nodes represent
 Entities, Attributes, States or Events Arcs in the
network give the relationship between the nodes
& Labels on the arc specify what type of
relationship actually exists.
Semantic Networks

8.  Example
 Is a & instance relations.
has
is a
Scooter Two - wheeler Motor – bike
Brakes Moving – vehicles Engine
Electrical system Fuel - system
is a is a
has
has has
Has part
Uniform color
team
Mammal
Person
Pee-wee Reese
Blue Brooklyn Dodgers
Nose
is a
instance

9.  FRAMES :- means of representing common sense
knowledge. Knowledge is organized into small packets
called “Frames”. All frames of a given situation constitute
the system.
 A frame can be defined as a structure that has slots for
various objects & a collection of frames consist of
expectation for a given situation.
 Frame are used to represent two types of knowledge viz.
declarative/factual and procedural, declarative &
procedural Frames: -
 A frame that merely contains description about objects is
call a declarative type/factual situational frame.
FRAMES
Name : Computer Centre
A/c Stationary
cupboard
Computer Dumb
terminals
Printer
Name of the frame
Slots in the frame

10.  Frames which have procedural knowledge embedded in it
are called action procedure frames. The action frame has
the following slots.
 Actor slot which holds information @ who is performing
the activity.
 Source Slot hold information from where the action has to
begin.
 Destination slot holds information about the place where
action has to end.
 Task slot This generates the necessary sub frames
required to perform the operation.
Name : Cleaning the ict of carburetor
Actor
Expert
Object
Source Destination
Scooter Scooter
Task 1 Task 2
Task 3
Remove Carburetor Clean
Nozzl
e
Fix Carburetor

11.  A mechanisms for representing knowledge about common
sequences of events.
 A script is a structure that describes a stereotyped
sequence of events in a particular content consist of slots 
contains values/default values.
 Components of a script
 Entry conditions – conditions before the events described in the
script can occur.
 Result – conditions that will in general be true after the events
described in the script have occurred.
 Props - slots representing objects that are involved in the
event described in the script.
 Roles – Slots representing people who are envolved in the
events described in the script.
 Track – The specific variation on a more general pattern that is
represented by this particular script.
 Scenes – The actual sequences of events that occur.
Scripts

12.  Pseudo form of a restaurant script
Script : Going to a restaurant
Props : Food
Tables
Menu
Money
Roles : Owner
Customer
Waiter
Cashier
Scene1 : Entering the restaurant.
Enters the restaurant.
scans the tables chooses the best one.
decides to sit there.
goes there.
occupies the seat.
Entry conditions
Customer is hungry
Customer has money
Owner has food.
Scene 2: Ordering the food.
Customer asks for menu.
Waiter brings it.
Customer glances it.
Chooses what to eat.
Orders that item.
Results :
Customer is hungry.
Owner has more money.
Customer has less money.
Owner has less food.
Scene 3 :
Eating the food.
Waiter brings the food.
Customer eats it.