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Knowledge Representation

Knowledge Representation. We’ve discussed generic search techniques. Usually we start out with a generic technique and enhance it to take advantage of a specific domain. The representation of knowledge about the domain is a major issue. Picking a good representation can make a big difference.

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Knowledge Representation

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  1. Knowledge Representation • We’ve discussed generic search techniques. • Usually we start out with a generic technique and enhance it to take advantage of a specific domain. • The representation of knowledge about the domain is a major issue. • Picking a good representation can make a big difference.

  2. Knowledge & Mappings • Knowledge is a collection of “facts” from some domain. • What we need is a representation of facts that can be manipulated by a program. • Some symbolic representation is necessary. • Need to be able to map facts to symbols. • Need to be able to map symbols to facts?

  3. A.I. Problems & K.R. • Game playing - need rules of the game, strategy, heuristic function(s). • Expert Systems - list of rules, methods to extract new rules. • Learning - the space of all things learnable (domain representation), concept representation. • Natural Language - symbols, groupings, semantic mappings, ...

  4. Representation Properties Representational Adequacy - Is it possible to represent everything of interest ? Inferential Adequacy - Can new information be inferred? Inferential Efficiency - How easy is it to infer new knowledge? Acquisitional Efficiency - How hard is it to gather information (knowledge)?

  5. Using Logic ro Represent Facts • Logic representation is common in AI programs: Spot is a dog dog(Spot) All dogs have tails x:dogs(x)->hastail(x) Spot has a tail hastail(Spot)

  6. Relational Databases • One way to store declarative facts is with a relational database: • Collection of attributes and values.

  7. Inheritance • It is often useful to provide a representation structure that directly supports inference mechanisms. • Property Inheritance is a common inference mechanism. • Objects belong to classes. • Classes have properties that are inherited by objects that belong to the class.

  8. Class Hierarchy • Classes are arranged in a hierarchy, so that some classes are members of more general classes. • There are a variety of representation strategies used in AI that are based on inheritance: slot-and-filler semantic network frame system

  9. Animal Mammal Bird fly? fly? NO YES fly? fly? Bat Dog NO YES Penguin color BLACK fly? color UnderDog Sam YES RED

  10. Inheritance Algorithm • We want to find the value of the attribute a of a specific object o. • First look at object o itself. • Next look for an instance attribute and look there for the value of a. • If still no attribute a, check out all isa attributes.

  11. Important Attributes • The instance and isa attributes support property inheritance. • Instance and isa may go by other names, or may be implicitly represented. • The isa (class membership) attribute is transitive.

  12. Attributes as objects • Attributes are themselves objects that have properties: • Inverse • Existence in a hierarchy • Techniques for reasoning about values • Single-valued attributes

  13. Inferential Knowledge • Inheritance is not the only inferential mechanism - logic formulas are often used: • We will study logical based inference procedures soon.

  14. Procedural Knowledge • Some knowledge in contained in the code we write (for example, a hard coded chess strategy). • How does procedural knowledge do with respect to the representation properties: • Representational Adequacy • Inferential Adequacy • Inferential Efficiency • Acquisitional Efficiency

  15. Granularity of Representation • High-level facts may require lots of storage if represented as a collection of low-level primitives. • Most knowledge is available in a high-level form (English). • It is not always clear what low-level primitives should be.

  16. Representing Sets of Object • Extensional definition: list all members of a set. • Dorks = {Bill, Bob, Dave, Jane} • Intensional: use rules to define membership in a set: • Dork = {x: geek(x) and bald(x) }

  17. Search and State Representation • Each state could be represented as a collection of facts. • Keeping many such states in memory may be impossible. • Most facts will not change when we move from one state to another.

  18. The Frame Problem • Determining how to best represent facts that change from state to state along with those facts that do not change is the Frame Problem. • Sometimes the hard part is determining which facts change and which do not.

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