Artificial intelligence
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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.

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Artificial Intelligence

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Artificial intelligence

Artificial Intelligence

Lecture 2


Knowledge

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

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

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

Knowledge Representation


Some issues in 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

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

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 knowledge base

Types of knowledge in knowledge-base

  • Factual knowledge  widely shared and agreed by expertise(from text books , journals, papers etc)

  • Heuristic Knowledge  knowledge from experience


Knowledge acquisition

Knowledge Acquisition

  • Gathering of knowledge from various sources

  • Issues of Knowledge


Requirements of knowledge acquisition

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

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

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

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

Knowledge Engineering

  • Art of designing and building knowledge-base system


Knowledge engineering techniques

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

questions

  • 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


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