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Knowledge

Knowledge. Knowledge is a collection of specialized facts, procedures and judgment rules. High. Knowledge. Degree of Abstraction. Information. Data. Low. Quantity. Knowledge Sources. Documented (books, manuals, etc.) Undocumented (in people's minds) From people, from machines

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Knowledge

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  1. Knowledge • Knowledge is a collection of specialized facts, procedures and judgment rules High Knowledge Degree of Abstraction Information Data Low Quantity

  2. Knowledge Sources • Documented (books, manuals, etc.) • Undocumented (in people's minds) • From people, from machines • Knowledge Acquisition from Databases • Knowledge Acquisition Via the Internet

  3. Knowledge Levels • Shallow knowledge (surface) • Deep knowledge • Can implement a computerized representation that is deeper than shallow knowledge • Special knowledge representation methods (semantic networks and frames) to allow the implementation of deeper-level reasoning (abstraction and analogy): important expert activity • Represent objects and processes of the domain of expertise at this level • Relationships among objects are important

  4. Major Categories of Knowledge • Declarative Knowledge • Procedural Knowledge • Metaknowledge

  5. Declarative Knowledge Descriptive Representation of Knowledge • Expressed in a factual statement • Shallow • Important in the initial stage of knowledge acquisition

  6. Procedural Knowledge • Considers the manner in which things work under different sets of circumstances • Includes step-by-step sequences and how-to types of instructions • May also include explanations • Involves automatic response to stimuli • May tell how to use declarative knowledge and how to make inferences

  7. Descriptive knowledge relates to a specific object. Includes information about the meaning, roles, environment, resources, activities, associations and outcomes of the object • Procedural knowledge relates to the procedures employed in the problem-solving process

  8. Metaknowledge Knowledge about Knowledge In ES, Metaknowledge refers to knowledge about the operation of knowledge-based systems Its reasoning capabilities

  9. Knowledge Modeling • The knowledge model views knowledge acquisition as the construction of a model of problem-solving behavior-- a model in terms of knowledge instead of representations • Can reuse models across applications

  10. Knowledge Representation • Logical representation – first order predicate calculus, Prolog, declarative knowledge • Procedural representation – a set of instructions for solving a problem, such as a production system • Network representation – knowledge is in a graph structure, such as conceptual dependency and conceptual graphs we will study in this chapter • Structured representation – an extension of networks, such as scripts or frames we will study in this chapter

  11. Group Work • What type of knowledge representation would be appropriate to contain a rules to generate the following sequences

  12. Group Work • What type of knowledge representation would be appropriate to solve “analogy” problems?

  13. Knowledge Representation Once acquired, knowledge must be organized for use

  14. Introduction • A good knowledge representation naturally represents the problem domain • An unintelligible knowledge representation is wrong • Most artificial intelligence systems consist of: • Knowledge Base • Inference Mechanism (Engine)

  15. Knowledge Base • Forms the system's intelligence source • Inference mechanism uses to reason and draw conclusions • Inference mechanism: Examines the knowledge base to answer questions, solve problems or make decisions within the domain

  16. Many knowledge representation schemes • Can be programmed and stored in memory • Are designed for use in reasoning • Major knowledge representation schemas: • Production rules • Frames

  17. Representation in Logic andOther Schemas • General form of any logical process • Inputs (Premises) • Premises used by the logical process to create the output, consisting of conclusions (inferences) • Facts known true can be used to derive new facts that are true

  18. Symbolic logic: System of rules and procedures that permits the drawing of inferences from various premises • Basic Forms of Computational Logic • Propositional logic (or propositional calculus) • Predicate logic (or predicate calculus)

  19. Propositional Logic • A proposition is a statement that is either true or false • Once known, it becomes a premise that can be used to derive new propositions or inferences • Rules are used to determine the truth (T) or falsity (F) of the new proposition

  20. Symbols represent propositions, premises or conclusions Statement: A = The mail carrier comes Monday through Friday. Statement: B = Today is Sunday. Conclusion: C = The mail carrier will not come today. • Propositional logic: limited in representing real-world knowledge

  21. Predicate Calculus • Predicate logic breaks a statement down into component parts, an object, object characteristic or some object assertion • Predicate calculus uses variables and functions of variables in a symbolic logic statement • Predicate calculus is the basis for Prolog (PROgramming in LOGic) • Prolog Statement Examples • comes_on(mail_carrier, monday). • likes(jay, chocolate). (Note - the period “.” is part of the statement)

  22. Lists Written Series of Related Items • Normally used to represent hierarchical knowledge where objects are grouped, categorized or graded according to • Rank or • Relationship

  23. Decision Tables(Induction Table) Knowledge Organized in a Spreadsheet Format • Attribute List • Conclusion List • Different attribute configurations are matched against the conclusion

  24. Decision Trees • Related to tables • Similar to decision trees in decision theory • Can simplify the knowledge acquisition process • Knowledge diagramming - very natural

  25. O-A-V Triplet • Objects, Attributes and Values • O-A-V Triplet • Objects may be physical or conceptual • Attributes are the characteristics of the objects • Values are specific measures of the attributes

  26. Default Logic • Deals with uncertainties • Incomplete information

  27. Knowledge Maps • Visual representation • Cognitive maps

  28. Semantic Networks • By following links, simple questions can be answered • Studies with human recall supported this model • Semantics nets were introduced by Quilian in the late 1960s for representing knowledge as a network of associations

  29. Semantic Networks • Graphic Depiction of Knowledge • Nodes and Links Showing Hierarchical Relationships Between Objects • Nodes: Objects • Arcs: Relationships • is-a • has-a

  30. Semantic networks can show inheritance • Semantic Nets - visual representation of relationships • Can be combined with other representation methods

  31. Semantic Network Example Human Being Is a Boy Is a Is a Needs Goes to Woman Joe School Is a Food Has a child Kay

  32. Conceptual Graphs • Graph Structure • Finite, connected, bipartite • Arcs are not labeled • Conceptual relation nodes are introduced between concepts • The bipartite nature of the graph means concepts can only link to conceptual relations and vice versa • In drawings, concepts are shown in boxes and conceptual relations in ellipses • Concepts may be concrete (dog, child, etc.) or abstract (love, beauty, etc.)

  33. Arity of Relations • Examples of 1-ary, 2-ary, and 3-ary relations

  34. Graph of a Sentence • “Mary gave John the book” • As in conceptual dependency, the verb plays a central role in the structure • The verb “give” in this sentence has an agent, an object, and a recipient

  35. Group Work • What does the following conceptual graph represent

  36. Types and Individuals • In the first case, the type is dog and the individual is “emma” • A specific but unnamed dog is given a unique number (#) • An alternative representation is to use a dog specified by a # and add a conceptual relation for a name

  37. Three Names • “Her name was McGill and she called herself Lil, but everyone knew her as Nancy” (song lyric) • Who was the artist? What was the name of the song?

  38. Itchy Dog • What is the English sentence for this structure? • If the same, unspecified individual is present in two or more nodes, a variable can be introduced that may eventually be bound to the same value

  39. Type Lattice • Concepts often form a lattice of types, such as a class golden retriever a type of dog, a type of carnivore, a type of animal, and so forth •  is a supertype of all types,  is the absent type • Answering queries about a pair of concepts may involve finding the minimum common supertype

  40. Generalization and Specialization • A concept node can be replaced with a restriction

  41. Join of Concepts • If two graphs contain an identical node, they can be joined together by having only one copy of the identical node • Join is a form of restriction since the resultant graph is more specific than the original graphs

  42. Simplification • A join may result in duplicate information • The simply operation allows the removal of duplication information

  43. Inheritance • Inheritance is a form of generalization • Generalization does not guarantee that the resultant graph is true even if the original graphs are true

  44. Propositional Nodes • “Tom believes that Jane likes pizza” • The verb believes takes a propositional node as its object

  45. “There are no pink dogs” • In some cases a propositional node may stand alone, as seen here: • This is similar to modal logics that introduce a level of believability, such as necessary, probably, possible, or other levels, such as negative shown here

  46. Group Work • What does the following conceptual graph represent

  47. Conceptual Graphs and Logic • Conceptual graphs are equivalent to predicate calculus in expressive power • Here is an algorithm to change a conceptual graph into a predicate calculus expression

  48. Production Rules • Condition-Action Pairs • IF this condition (or premise or antecedent) occurs, • THEN some action (or result, or conclusion, or consequence) will (or should) occur • IF the stop light is red AND you have stopped, THEN a right turn is OK

  49. Each production rule in a knowledge base represents an autonomous chunk of expertise • When combined and fed to the inference engine, the set of rules behaves synergistically • Rules can be viewed as a simulation of the cognitive behavior of human experts • Rules represent a model of actual human behavior

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