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Contents
Knowledge Acquisition
Machine Learning
Knowledge Acquisition
The transfer and transformation of potential problem solving
expertise from some knowledge source to a program.
Buchanan, 1983
Knowledge Acquisition
The process of acquiring, studying and organizing
knowledge, so that it can be used in a knowledge-based
system.
Expert may provide irrelevant, incomplete or inconsistent
information.
Data and knowledge acquisition
Collect and analyze data and knowledge
Make key concepts of the system design more explicit
Knowledge Acquisition (Cont.)
Acquired knowledge may consist facts, rules, concept,
procedures, heuristics, formulas, relationships, statistics, or
other useful informationSources
DocumentedWritten, viewed, sensory, behavior
UndocumentedMemory
Acquired fromHuman sensesMachines
Knowledge
Levels Shallow
Surface levelInput-output
Deep Problem solvingDifficult to collect, validateInteractions between system components
Knowledge
CategoriesDeclarative
Descriptive representationProcedural
How things work under different circumstancesHow to use declarative knowledge
Problem solvingMeta knowledge
Knowledge about knowledge
Knowledge EngineersProfessionals who elicit knowledge from experts
Empathetic, patient
Broad range of understanding, capabilities
Integrate knowledge from various sourcesCreates and edits code
Operates tools
Build knowledge baseValidates information
Trains users
Knowledge Acquisition Difficulties
Problems in Transferring Knowledge
Expressing KnowledgeTransfer to a MachineNumber of ParticipantsStructuring Knowledge
Experts may lack time or not cooperate
Testing and refining knowledge is complicated
Poorly defined methods for knowledge elicitation
System builders may collect knowledge from one source, but the relevant knowledge may be scattered across several sources
May collect documented knowledge rather than use experts
The knowledge collected may be incomplete
Difficult to recognize specific knowledge when mixed with irrelevant data
Other Reasons
Overcoming the Difficulties Knowledge acquisition tools with ways to decrease the representation mismatch between the human expert and the program (“learning by being told”)
Simplified rule syntax
Natural language processor to translate knowledge to a specific representation
Impacted by the role of the three major participants
Knowledge Engineer
Expert
End user
Critical
The ability and personality of the knowledge engineer
Must develop a positive relationship with the expert
The knowledge engineer must create the right
impression
Computer-aided knowledge acquisition tools
Extensive integration of the acquisition efforts
Overcoming the Difficulties
Knowledge Acquisition Methods
Manual
Semiautomatic
Automatic (Computer Aided)
Manual Methods - Structured Around Interviews
Process (Figure next slide)InterviewingTracking the Reasoning Process ObservingManual methods: slow, expensive and sometimes inaccurate
Manual Methods of Knowledge Acquisition
Elicitation
Knowledgebase
Documentedknowledge
Experts
CodingKnowledgeengineer
Semiautomatic Methods
Support Experts Directly (Figure next slide)
Help Knowledge Engineers
Expert-Driven Knowledge Acquisition
Knowledgebase
Knowledgeengineer
Expert CodingComputer-aided(interactive)interviewing
Automatic Methods
Expert’s and/or the knowledge engineer’s roles are minimized (or eliminated)
Induction Method (Figure next slide)
Induction-Driven Knowledge Acquisition
Knowledgebase
Case historiesand examples
Inductionsystem
Machine Learning
Machine learning is a specialized form of autonomous
knowledge acquisition.
Autonomous knowledge creation or refinement through
the use of computer programs.
Why is Machine Learning Important?
Some tasks cannot be defined well, except by examples (e.g.,
recognizing people).
Relationships and correlations can be hidden within large
amounts of data. Machine Learning/Data Mining may be able to
find these relationships.
Human designers often produce machines that do not work as
well as desired in the environments in which they are used.
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The amount of knowledge available about certain tasks might be
too large for explicit encoding by humans (e.g., medical
diagnostic).
Environments change over time.
New knowledge about tasks is constantly being discovered by
humans. It may be difficult to continuously re-design systems “by
hand”.
Why is Machine Learning Important?
Types of Learning Learning by memorization
Direct instruction
Analogy
Induction
Deduction
Learning by Memorization
It requires the least amount of inference and is
accomplished by simply copying the knowledge in the
same form that it will be used directly into the
knowledge base.
Learning by Direct Instruction
The knowledge must be transformed into an
operational form before being integrated into the
knowledge base
This type of learning used when a teacher presents
a number of facts directly to us in a well organized
manner.
Analogical Learning
Is the process of learning a new concept or solution
through the use of similar known concepts or solutions.
Here, previously learn examples serve as a guide.
Driving a truck using experience of driving a car.
Learning by Induction
This form of learning requires the use of inductive
inference
We use inductive learning when we formulate a
general concept after seeing a number of instances
or examples of the concept.Example:
we learn the concepts of color or sweet taste after experiencing the sensations associated with several examples of color example objects or sweet foods
Deductive Learning
It is accomplished through a sequence of deductive
inference steps using known facts.
From the known facts, new facts and relationships
are logically derived.
Example:(father X of Y), (father Y of Z);
(Grandfather X of Z)
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