Download Artificial Intelligence

Artificial Intelligence
Lecture 2
Knowledge
• Knowledge is the collection of facts and principles
accumulated by human.
• Knowledge can be language, concepts,
procedures, rules, ideas, abstractions, places ,
customs, and so on.
• study of knowledge is called Epistemology.
• Belief meaningful coherent expression
• Hypothesis  belief that is not known to be true
• Knowledge can be defined as true justified belief
• Mete-knowledge knowledge about knowledge
Types of Knowledge
• Procedural knowledge
• Procedural knowledge is compiled or processed form of
information. Procedural knowledge is related to the performance of
some task. It tells what about any object For example, sequence of
steps to solve a problem is procedural knowledge.
• Declarative knowledge
• Declarative knowledge is passive knowledge in the form of
statements of facts about the world. It gives simple information
about any organization or object or event
For example, mark statement of a student is declarative knowledge.
• Heuristic knowledge
• Heuristic knowledge is used to make judgments and also to simplify
solution of problems. It is acquired through experience. An expert
uses his knowledge that he has gathered due to his experience and
learning.
Difference between Knowledge and
Data
• Data is raw facts and figures that do not have
meaning e.g 7, summer, vanilla, money
• information is data with meaning e.g 7
flavored vanilla ice creams
• Knowledge is the applying of rules to
information.
• So the knowledge to my data would be in the
summer I will buy 7 flavored vanilla ice creams
if I have enough money
Knowledge Representation
Some Issues in Knowledge
Representation
• What are the attributes of objects that should be
captured and what relationship does exist between the
attributes?
• What is the granularity of representation?
• Granularity depth of detail
• If every minute detail of event is captured  fine grain
• If fewer details are captured  coarse grain
• What is the inferential mechanism used?
• Inference drawing conclusion or finding solution of
some problem
Knowledge base System
• Software that uses artificial intelligence or expert
system techniques in problem solving processes.
• It incorporates a store (database)
of expert knowledge with couplings
and linkages designed to facilitate its retrieval
in response to specific queries, or
to transfer expertise from one domain of
knowledge to another.
Types of Knowledge-base
• Machine readable knowledgebase  Stores
data in machine(computer) readable form
• Human readable Knowledgebase  can be
used by people for training purpose(papers ,
user manuals etc)
Types of knowledge in knowledgebase
• Factual knowledge  widely shared and
agreed by expertise(from text books , journals,
papers etc)
• Heuristic Knowledge  knowledge from
experience
Knowledge Acquisition
• Gathering of knowledge from various sources
• Issues of Knowledge
Requirements of Knowledge
Acquisition
• Focus on essential part of knowledge
• Can capture tacit knowledge
• Allow knowledge to be manipulated and
validated
Modes of Knowledge creation or
conversion
• From tacit to tacit(Socialization)sharing
experience to create tacit knowledge
• From explicit to tacit(Internalization) written
form of knowledge into ones head
• From tacit to explicit (Externalization)
articulating knowledge in the head into
communicable form
• From explicit to explicit(Combination) 
transferring knowledge through documents ,
meeting and conversation
Knowledge Acquisition Techniques
• Protocol-Generation Technique  Structured and
unstructured interviews , reporting technique and
observational technique
• Protocol-Analysis Technique To identify types of
knowledge
• Hierarchy Generation Technique Used to build
hierarchical structure
• Matrix-based Technique  Construction of grids indicating
problems encountered against possible solutions
• Sorting Technique  Used to compare and order concepts
• Diagram-based Technique  Concept maps , event diagram
and process maps are used to capture (why,when,who,how
and where)features of events and tasks
Knowledge Organization and
Management
• Always be able to accept new knowledge
• Be possible to locate any stored item of
knowledge efficiently
• Organization process of knowledge must be in
clustered format indexing
• Organization facilitates consolidating recurrent
incidents and forgetting knowledge when it is
no longer needed
Knowledge Engineering
• Art of designing and building knowledge-base
system
Knowledge Engineering Techniques
• Assessment of problems
• Acquisition of knowledge and specific preference
• Implementation of structured knowledge into
knowledge-base
• Testing and validation of inserted knowledge
• Development of structured knowledge-base
system
• Integration and maintenance of system
• Revision and evaluation of system
questions
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Define knowledge
Mention difference between knowledge and data
Explain different types of knowledge
Define knowledgebase
Explain different types of knowledgebase
What do you mean by knowledge acquisition
State considerations for knowledge acquisition
How can we manage knowledge
Briefly explain knowledge engineering techniques
Mention considerations for knowledge representations