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Artificial Intelligence: Knowledge Acquisition
This document discusses different types of knowledge and methods for knowledge acquisition. It describes declarative and procedural knowledge, as well as the knowledge acquisition paradox where experts have difficulty verbalizing their knowledge. Various knowledge acquisition methods are outlined, including observation, problem discussion, and protocol analysis. Knowledge representation techniques like rules, semantic networks, frames, and predicate logic are also introduced.
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Artificial Intelligence: Knowledge Acquisition
- 1. Knowledge Acquisition •Types of Knowledge • Knowledge Acquisition Paradox • Difficulties of Knowledge Acquisition • Knowledge Acquisition Methods • Automatic Knowledge Acquisition Technology
- 2. Types of Knowledge • Declarative Knowledge - tells us the facts – Facts, knowledge about objects and relationships – Descriptive representation of knowledge – It is often shallow knowledge · Procedural Knowledge - tells us what to do – Knowledge about procedures involved in solving problems · Declarative Knowledge - tells us facts and procedural knowledge tells us what to do
- 3. Knowledge Acquisition Paradox · The more competent a Domain Expert (DE) becomes, the less able they are to describe the knowledge they use to solve problems · Don’t be your own expert · Don’t believe everything experts say
- 4. Difficulties of KnowledgeAcquisition • Difficulty in verbalizing – Reasoning process too broad – Use of combined and compiled knowledge – Unaware of the individual steps taken to reach a solution • Difficulties in transferring to a machine – Machine works at a more basic level, but the expert seldom operates at a basic level
- 5. Difficulties of KnowledgeAcquisition • Difficulties in structuring knowledge – Losing a significant amount of knowledge when structuring implicit knowledge • Domain Expert’s unwillingness – Unavailable – Uncooperative – No knowledge of computers and Expert Systems
- 6. Knowledge Acquisition Methods1 · On-site observation · Watch the expert solving real problems on the job • We are not the experts, so we research the particular area BEFORE sitting down with the Domain Expert(s) • Ex: Sometimes a Doctor brings a Student with them/Student learns from the Expert
- 7. Knowledge Acquisition Methods2 · Problem discussion - observe at first · Explore the kinds of data, knowledge, and procedures needed to solve specific problems · How does the problem differ from prototypical problems in the domain? · How is this problem different from others? · What different approach do you use? · Types of data required and kinds of solutions adequate for the problem? · What kinds of knowledge are needed to solve the problem? · What constitutes an adequate explanation or justification of a problem solution?
- 8. Knowledge Acquisition Methods3 · Problem Description · Have the expert describe a prototypical problem for each category of answer in the domain · Protocol Analysis (Problem Analysis) · Present the expert with a series of realistic problems to solve aloud, probing for the rationale behind the reasoning steps (solve the problem verbally) · Widely used in psychology · Ex: Dermatology-Psoriasis · Expert Syst. to diagnose Psoriasis · Color? · How long rash lasts? · Where is the rash?
- 9. Knowledge Acquisition Methods4 • Repertory Grid Analysis – Identify important objects – Identify important attributes • Specific objects – Example: Rash/Color/Duration/Level of itching/Local or whole body? – For each attributes, establish a bipolar scale with differentiable characteristics and their opposites – Ex: Computer Language
- 10. Repertory Grid Analysis • Assisting in selecting a computer language – Identify objectives • LISP, C (Procedural Lang), C++(OOP Lang) – Attributes • Availability, Ease of Programming, Training Time • Orientation – Traits • high, low, symbolic, numeric
- 11. Reasoning Methods •Deductive Reasoning • Inductive Reasoning • Forward Reasoning (Chaining) – Reasoning starts with raw facts • Backward Reasoning (Chaining) – Reasoning starts with hypothesis as in statistics, them moves to prove or disprove hypothesis
- 12. RGA Input forSelecting a Computer Language Attributes: Trait or Opposite Availability: Widely Available or Not Available Ease of Programming: High or Low (C++) (C) Training Time: Low or High Orientation: Symbolic or Numeric Example: The Animal Problem – Done in LISP – “Symbol Oriented Can store colors – Red/Blue/Orange/Green 1 variable can be 26 Char long/1 char long
- 13. Automatic Knowledge Acquisition Techniques • Methods – Rule Induction - DE provides some examples similar to Data Mining, then apply Statistical/Mathematical Techniques such as Multivariate Regression – Artificial Neural Net (ANN) - Qualitative Approach-Statistical & Mathematical Methods/Dev. Intelligent Machine/Data Mining
- 14. Automatic Knowledge Acquisition Techniques • Methods – Case-based Reasoning - asking DE to provide case/Law - Attorney • Work by previous cases/Dev. argument from previous cases • Use previous as base argument – Example: Help Desk » Printer not functioning » Refer to previous case from “n” weeks ago
- 15. Automatic Knowledge Acquisition Techniques • Methods – Model-based Reasoning • Applicable to design of an engineering application • Give me specifications of some hardware • Used often in NASA • Build a model using DE knowledge
- 16. Knowledge Representation •Logic is used heavily in AI – Prepositional Logic – Predicate Logic – Rules (easiest to represent) – Semantic Nets – Frame – Object
- 17. Propositional Logic •It is raining – RAINING • Proposition/Propositional Logic - Is this true or false? Is it raining now? • It is sunny • We can deduce whether a certain proposition (fact) is true or false
- 18. Proposition Logic •Propositional logic cannot drive the association • Socrates is a man (true or false) – SOCRATESMAN • Plato is a man (true or false) • We can not draw any conclusions about similarities between Socrates and Plato – By separate propositional logic cannot reach a conclusion • Variable = Substituting a value • Constant = Have to assign value
- 19. Predicate Logic •More like a variable/can hold different values • Socrates is a man (true or false) – PREDICATE(VALUE) • Socrates is a man – MAN(SOCRATES) • Plato is a man – MAN(PLATO) • Now the structure of representation reflects the structure of knowledge itself
- 20. Predicate Logic •Marcus is a man – MAN(Marcus) • Marcus is a Pompeian – POMPEIAN (Marcus) • All Pompeians were Romans – Vx POMPEIAN(x) -> ROMAN(x)
- 21. Predicate Logic •All Romans were either loyal to Caesar or hated him • Vx ROMAN(x) -> loyalto (x, Caesar) v hate (x, Caesar) • It is difficult to represent knowledge in predicate logic
- 22. Rules If (conditions) Then (actions) Else (actions)
- 23. Semantic Networks (Nets) • Semantic net is a knowledge presentation method based on a network structure • It consists of – points called nodes connected by – links called arcs • Nodes – object, concepts, events • Arcs - relationships between nodes
- 24. Semantic Nets •Common arcs used for representing hierarchies include isa and has-part • Processing Natural Language – Example: Text Mining • Uses Natural Language for summarizing article or newspaper
- 25. Example: The QueenMary is an ocean liner Every ocean liner is a ship Ship isa Ocean Liner isa Queen Mary
- 26. SHIP Isa (hierarchicalrelationship) Ocean Liner Oil Tanker Engine Hull Swimming Queen Mary Liver Pool Boiler Pool Has-part (component relationship) isa
- 27. Bill gives Judya gift Judy Give (verb) Gift Bill Recieves Object Gives Node Node
- 28. Bill told Laurathat he gave Judy a gift Judy Give (verb) Gift Bill Recieves Object Gives Tell Laura Speaker Listener Time Past
- 29. Frame 1 •Similar to Object • Hierarchical Representation – Introduce details as necessary – Polymorphism – Multi-inheritance • A data structure for representing a stereotyped situation • A network of nodes and relations organized in a hierarchy • The topmost nodes - general concepts (abstract class) • The lower nodes - more specific instances (more specific classes)
- 30. Frame 2 •The concepts at each node is described by a set of attributes and values of those attributes • Attributes are called slots • Each slot can have procedures (codes) • Typical procedures – if added procedure – if deleted procedure – if needed procedure
- 31. Frame 3 •OOP – Class – Attribute – Method • AI – Node – Slots – Procedures
- 32. Report isa isa Progress Report Technical Report isa DSS Project Process Report
- 33. A Node ina Frame System Value 1 Slot 1 Slot 2 Value 2 Value 3 Slot 3 Procedure 1 Procedure 2 Procedure 3
- 34. Comparisons of KRMethods • Rules • When get too large become unmanageable – IF… THEN… ELSE – Advantage • Simple syntax, easy to understand, simple interpreter, high modularity, flexible – Disadvantage • Hard to follow hierarchies, inefficient for large systems, not all knowledge can be expressed as rules
- 35. Comparisons of KRMethods • Semantic Nets – Advantage • Easy to follow hierarchy, easy to trace association, flexible – Disadvantage • Meaning attached to nodes might be ambiguous • Exception handling is difficult • Difficult to program
- 36. Comparisons of KRMethods • Frames – Advantage • Expressive power, easy to set up slots for new properties and relations • Easy to create specialized procedures • Easy to include default information and detect missing values – Disadvantage • Difficult to program • Difficult for inference