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Knowledge Representation: Types, Stages, and Issues

Explore the syntax, semantics, and types of knowledge representation along with stages of knowledge use like acquisition and reasoning. Uncover advantages, attributes, and broad questions surrounding knowledge representation schemes.

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Knowledge Representation: Types, Stages, and Issues

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  1. Knowledge Representation CIS 479/579 Bruce R. Maxim UM-Dearborn

  2. Representation • Set of syntactic and semantic conventions which make it possible to describe things • Syntax • specific symbols allowed and rules allowed • Semantics • how meaning is associated with symbol arrangements allowed by syntax

  3. Representation Types • Relational databases • Constraints • Predicate logic • Concept hierarchies • Semantic networks • Frames • Conceptual Dependency • Scripts

  4. Types of Knowledge • Objects • both physical & concepts • Events • usually involve time • maybe cause & effect relationships • Performance • how to do things • META Knowledge • knowledge about how to use knowledge

  5. Stages of Knowledge Use • Acquisition • structure of facts • integration of old & new knowledge • Retrieval (recall) • roles of linking and chunking • means of improving recall efficiency

  6. Stages of Knowledge Use • Reasoning • Formal reasoning • deductive theorem proving • Procedural Reasoning • expert system • Reasoning by Analogy • very hard for machines • Generalization • reasoning from examples • Abstraction • simplification

  7. Knowledge Representation Issues • Grain size or resolution detail • Scope or domain • Modularity • Understandability • Explicit versus implicit knowledge • Procedural versus declarative knowledge

  8. Advantages • Declarative representation • Store each fact once • Easy to add new facts • Procedural representation • Easy to represent "how to do things" • Easy to represent any knowledge not fitting declarative format • Relatively easy to implement heuristic stuff on doing thing efficiently

  9. Attributes of Good KR Schemes • Representational Adequacy • works for all knowledge in problem domain • Inferential Adequacy • provides ability to manipulate structures to desire new structures • Inferential Adequacy • ability to incorporate additional information in knowledge structures to help focus attention of promising new directions

  10. Attributes of Good KR Schemes • Acquisitional Efficiency • easy to add new knowledge • Semantic Power • Supports truth theory • Provides for constraint satisfaction • Can cope with incomplete or uncertain knowledge • Contains some commonsense reasoning capability

  11. Broad KR Questions • Are there properties of objects so basic that they occur in every domain? • If so what are they? • At what level should knowledge be represented? • Is there a good set of primitives into which all knowledge can be broken down? • How can the relevant parts of a large knowledge base be accessed when needed?

  12. State Space Representation • How can individual objects and facts be represented? • How do you combine individual object descriptions to form a representation of the complete problem state? • How can the sequences of problem states that arise be represented efficiently?

  13. Two Approaches • Use complete object descriptions that include relations to other objects in the environment • Use predicate logic to express these kind of relations on(plant,table). under(table,window). in(table,room).

  14. Frame Problem • What (or how much) should be stored at each node? • How do you distinguish between facts that change from facts that do not change between frames? • Stated and another way, how do you decide how much information to record as you move from problem state to problem state?

  15. Frame Problem • The naive approach is to store complete state descriptions and make changes to them each time a node is updated • Disadvantage • takes time to do • descriptions can become large • what happens when algorithm needs to backtrack and undo changes?

  16. Frame Problem • Better solution is to not physically modify the state description but merely record a list of changes that should be made at this node • To get to the current state, you start at the initial state and then apply the changes recorded • Backtracking will be easy, but state description is complicated

  17. Frame Problem • Another alternative would be to modify the state description but mark the changes made so they can be undone when backtracking is required • If temporal relations (time) are involved things will become regardless of the approach used

  18. Searching Rule-bases • Sequential search of a large rule-base is time consuming and should be avoided if possible • Making use of rule indices and hash tables would improve the efficiency • Using variables in rules can reduce the number of rules

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