Knowledge Representation

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