knowledge acquisition, representation, and reasoning by dr.s.sridhar,ph.d.,...
TRANSCRIPT
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Knowledge Acquisition, Representation, and Reasoning
ByDr.S.Sridhar,Ph.D.,
RACI(Paris),RZFM(Germany),RMR(USA),RIEEEProc.email : [email protected]
web-site : http://drsridhar.tripod.com
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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.
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Development of a Real-Time 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• Testing and evaluation of system
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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
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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 capabilities
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Knowledge
• Sources • Documented
• Written, viewed, sensory, behavior
• Undocumented• Memory
• Acquired from• Human senses• Machines
•
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Knowledge
• Levels • Shallow
• Surface level• Input-output
• Deep • Problem solving• Difficult to collect, validate• Interactions betwixt system components
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Knowledge
• Categories• Declarative
• Descriptive representation
• Procedural • How things work under different
circumstances• How to use declarative knowledge
− Problem solving
• Metaknowledge• Knowledge about knowledge
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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
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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
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Manual Methods
• Interviews• Structured
• Goal-oriented• Walk through
• Unstructured• Complex domains• Data unrelated and difficult to integrate
• Semistructured
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Manual Methods
• Process tracking• Track reasoning processes
• Protocol analysis• Document expert’s decision-making • Think aloud process
• Observation• Motor movements• Eye movements
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Manual Methods
• 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
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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 available
knowledge
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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
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Automatic Methods
• Data mining by computers• Inductive learning from existing
recognized cases• Neural computing mimicking
human brain• Genetic algorithms using natural
selection
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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
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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
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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
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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
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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• Can be automated
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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
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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
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Artificial Intelligence Rules
• Advantages• 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
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Semantic Networks
• Graphical depictions
• Nodes and links • Hierarchical
relationships between concepts
• Reflects inheritance
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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 rules
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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
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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
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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
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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 metaknowledge
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Uncertainty
• Widespread• Important component• Representation
• Numeric scale• 1 to 100
• Graphical presentation• Bars, pie charts
• Symbolic scales• Very likely to very unlikely
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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
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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
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Expert System Development
• Phases• Project initialization• Systems analysis and design• Prototyping• System development• Implementation• Postimplementation
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Project Initialization
• Identify problems• Determine functional requirements• Evaluate solutions• Verify and justify requirements• Conduct feasibility study and cost-
benefit analysis• Determine management issues • Select team• Project approval
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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
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Prototyping
• Rapid production• Demonstration prototype
• Small system or part of system• Iterative• Each iteration tested by users• Additional rules applied to later
iterations
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System Development
• Development strategies formalized• Knowledge base developed• Interfaces created• System evaluated and improved
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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
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Postimplementation
• Operation of system• Maintenance plans
• Review, revision of rules• Data integrity checks• Linking to databases
• Upgrading and expansion• Periodic evaluation and testing
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Internet
• Facilitates knowledge acquisition and distribution
• Problems with use of informal knowledge
• Open knowledge source