Download Knowledge Acquisition, Representation, and Reasoning

Knowledge Acquisition,
Representation, and Reasoning
By
Dr.S.Sridhar,Ph.D.,
RACI(Paris),RZFM(Germany),RMR(USA),RIEEEProc
.
email : [email protected]
web-site : http://drsridhar.tripod.com
Learning Objectives
• Understand the nature of knowledge.
• Learn the knowledge engineering
processes.
• Evaluate different approaches for
knowledge acquisition.
• Examine the pros and cons of different
approaches.
• Illustrate methods for knowledge
verification and validation.
• Examine inference strategies.
• Understand certainty and uncertainty
processing.
Development of a RealTime Knowledge-Based
System at Eli Lilly Vignette
• Problems with fermentation process
• Quality parameters difficult to control
• Many different employees doing same task
• High turnover
• Expert system used to capture knowledge
• Expertise available 24 hours a day
• Knowledge engineers developed system
by:
• Knowledge elicitation
• Interviewing experts and creating knowledge
bases
• Knowledge fusion
• Fusing individual knowledge bases
• Coding knowledge base
Knowledge Engineering
• Process of acquiring knowledge
from experts and building
knowledge base
• Narrow perspective
• Knowledge acquisition,
representation, validation, inference,
maintenance
• Broad perspective
• Process of developing and
maintaining intelligent system
Knowledge Engineering
Process
• Acquisition of knowledge
• General knowledge or metaknowledge
• From experts, books, documents,
sensors, files
• Knowledge representation
• Organized knowledge
• Knowledge validation and
verification
• Inferences
• Software designed to pass statistical
sample data to generalizations
• Explanation and justification
Knowledge
• Sources
• Documented
• Written, viewed, sensory, behavior
• Undocumented
• Memory
• Acquired from
• Human senses
• Machines
•
Knowledge
• Levels
• Shallow
• Surface level
• Input-output
• Deep
• Problem solving
• Difficult to collect, validate
• Interactions betwixt system
components
Knowledge
• Categories
• Declarative
• Descriptive representation
• Procedural
• How things work under different
circumstances
• How to use declarative knowledge
− Problem solving
• Metaknowledge
• Knowledge about knowledge
Knowledge Engineers
• Professionals who elicit knowledge
from experts
• Empathetic, patient
• Broad range of understanding,
capabilities
• Integrate knowledge from various
sources
• Creates and edits code
• Operates tools
• Build knowledge base
• Validates information
• Trains users
Elicitation Methods
• Manual
• Based on interview
• Track reasoning process
• Observation
• Semiautomatic
• Build base with minimal help from
knowledge engineer
• Allows execution of routine tasks with
minimal expert input
• Automatic
• Minimal input from both expert and
knowledge engineer
Manual Methods
• Interviews
• Structured
• Goal-oriented
• Walk through
• Unstructured
• Complex domains
• Data unrelated and difficult to
integrate
• Semistructured
Manual Methods
• Process tracking
• Track reasoning processes
• Protocol analysis
• Document expert’s decisionmaking
• Think aloud process
• Observation
• Motor movements
• Eye movements
Manual Methods
•
•
•
•
•
•
•
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Case analysis
Critical incident
User discussions
Expert commentary
Graphs and conceptual models
Brainstorming
Prototyping
Multidimensional scaling for distance
matrix
• Clustering of elements
• Iterative performance review
Semiautomatic Methods
• Repertory grid analysis
• Personal construct theory
• Organized, perceptual model of expert’s
knowledge
• Expert identifies domain objects and their
attributes
• Expert determines characteristics and
opposites for each attribute
• Expert distinguishes between objects,
creating a grid
• Expert transfer system
• Computer program that elicits
information from experts
• Rapid prototyping
• Used to determine sufficiency of
Semiautomatic Methods,
continued
• Computer based tools features:
• Ability to add knowledge to base
• Ability to assess, refine knowledge
• Visual modeling for construction
of domain
• Creation of decision trees and
rules
• Ability to analyze information
flows
• Integration tools
Automatic Methods
• Data mining by computers
• Inductive learning from existing
recognized cases
• Neural computing mimicking
human brain
• Genetic algorithms using
natural selection
Multiple Experts
• Scenarios
• Experts contribute individually
• Primary expert’s information reviewed
by secondary experts
• Small group decision
• Panels for verification and validation
• Approaches
• Consensus methods
• Analytic approaches
• Automation of process through software
usage
• Decomposition
Automated Knowledge
Acquisition
• Induction
• Activities
• Training set with known outcomes
• Creates rules for examples
• Assesses new cases
• Advantages
• Limited application
• Builder can be expert
− Saves time, money
Automated Knowledge
Acquisition
• Difficulties
• Rules may be difficult to understand
• Experts needed to select attributes
• Algorithm-based search process
produces fewer questions
• Rule-based classification problems
• Allows few attributes
• Many examples needed
• Examples must be cleansed
• Limited to certainties
• Examples may be insufficient
Automated Knowledge
Acquisition
• Interactive induction
• Incrementally induced knowledge
• General models
− Object Network
• Based on interaction with expert
• interviews
• Computer supported
• Induction tables
• IF-THEN-ELSE rules
Evaluation, Validation,
Verification
• Dynamic activities
• Evaluation
• Assess system’s overall value
• Validation
• Compares system’s performance to
expert’s
• Concordance and differences
• Verification
• Building and implementing system
correctly
Production Rules
• IF-THEN
• Independent part, combined
with other pieces, to produce
better result
• Model of human behavior
• Examples
• IF condition, THEN conclusion
• Conclusion, IF condition
• If condition, THEN conclusion1
(OR) ELSE conclusion2
Artificial Intelligence
Rules
• Types
• Knowledge rules
• Declares facts and relationships
• Stored in knowledge base
• Inference
• Given facts, advises how to proceed
• Part of inference engines
• Metarules
Artificial Intelligence
Rules
• Advantages
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•
•
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Easy to understand, modify, maintain
Explanations are easy to get.
Rules are independent.
Modification and maintenance are relatively
easy.
• Uncertainty is easily combined with rules.
• Limitations
• Huge numbers may be required
• Designers may force knowledge into rule-based
entities
• Systems may have search limitations; difficulties
in evaluation
Semantic Networks
• Graphical
depictions
• Nodes and
links
• Hierarchical
relationships
between
concepts
• Reflects
inheritance
Frames
• All knowledge about object
• Hierarchical structure allows for
inheritance
• Allows for diagnosis of knowledge
independence
• Object-oriented programming
• Knowledge organized by characteristics
and attributes
• Slots
• Subslots/facets
• Parents are general attributes
• Instantiated to children
• Often combined with production
Knowledge Relationship
Representations
• Decision tables
• Spreadsheet format
• All possible attributes compared to
conclusions
• Decision trees
• Nodes and links
• Knowledge diagramming
• Computational logic
• Propositional
• True/false statement
• Predicate logic
• Variable functions applied to components of
statements
Reasoning Programs
• Inference Engine
• Algorithms
• Directs search of knowledge base
• Forward chaining
− Data driven
− Start with information, draw conclusions
• Backward chaining
− Goal driven
− Start with expectations, seek supporting
evidence
• Inference/goal tree
• Schematic view of inference process
− AND/OR/NOT nodes
− Answers why and how
• Rule interpreter
Explanation Facility
• Justifier
• Makes system more understandable
• Exposes shortcomings
• Explains situations that the user did not
anticipate
• Satisfies user’s psychological and social needs
• Clarifies underlying assumptions
• Conducts sensitivity analysis
• Types
• Why
• How
• Journalism based
• Who, what, where, when, why, how
• Why not
Generating Explanations
• Static explanation
• Preinsertion of text
• Dynamic explanation
• Reconstruction by rule evaluation
• Tracing records or line of
reasoning
• Justification based on empirical
associations
• Strategic use of
Uncertainty
• Widespread
• Important component
• Representation
• Numeric scale
• 1 to 100
• Graphical presentation
• Bars, pie charts
• Symbolic scales
• Very likely to very unlikely
Uncertainty
• Probability Ratio
• Degree of confidence in conclusion
• Chance of occurrence of event
• Bayes Theory
• Subjective probability for propositions
• Imprecise
• Combines values
• Dempster-Shafer
• Belief functions
• Creates boundaries for assignments of
probabilities
• Assumes statistical independence
Certainty
• Certainty factors
• Belief in event based on evidence
• Belief and disbelief independent
and not combinable
• Certainty factors may be
combined into one rule
• Rules may be combined
Expert System
Development
• Phases
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Project initialization
Systems analysis and design
Prototyping
System development
Implementation
Postimplementation
Project Initialization
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Identify problems
Determine functional requirements
Evaluate solutions
Verify and justify requirements
Conduct feasibility study and costbenefit analysis
• Determine management issues
• Select team
• Project approval
Systems Analysis and
Design
• Create conceptual system design
• Determine development strategy
• In house, outsource, mixed
• Determine knowledge sources
• Obtain cooperation of experts
• Select development environment
• Expert system shells
• Programming languages
• Hybrids with tools
• General or domain specific shells
• Domain specific tools
Prototyping
• Rapid production
• Demonstration prototype
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Small system or part of system
Iterative
Each iteration tested by users
Additional rules applied to later
iterations
System Development
• Development strategies
formalized
• Knowledge base developed
• Interfaces created
• System evaluated and improved
Implementation
• Adoption strategies formulated
• System installed
• All parts of system must be fully
documented and security
mechanisms employed
• Field testing if it stands alone;
otherwise, must be integrated
• User approval
Postimplementation
• Operation of system
• Maintenance plans
• Review, revision of rules
• Data integrity checks
• Linking to databases
• Upgrading and expansion
• Periodic evaluation and testing
Internet
• Facilitates knowledge
acquisition and distribution
• Problems with use of informal
knowledge
• Open knowledge source