Knowledge Acquisition, Representation, and Reasoning

ByDr.S.Sridhar,Ph.D.,

RACI(Paris),RZFM(Germany),RMR(USA),RIEEEProc.email : drssridhar@yahoo.com

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 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

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 capabilities

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 decision-making • Think aloud process

• Observation• Motor movements• Eye movements

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

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

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• Can be automated

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• 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 rules

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 metaknowledge

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• Project initialization• Systems analysis and design• Prototyping• System development• Implementation• Postimplementation

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

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

• 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