1. Knowledge Representation
CIS 479/579
Bruce R. Maxim
UM-Dearborn
9/22/2018

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
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3. Representation Types Relational databases Constraints Predicate logic
Concept hierarchies
Semantic networks
Frames
Conceptual Dependency
Scripts
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4. Types of Knowledge Objects Events Performance META Knowledge
both physical & concepts
Events
usually involve time
maybe cause & effect relationships
Performance
how to do things
META Knowledge
knowledge about how to use knowledge
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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
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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
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7. Knowledge Representation Issues
Grain size or resolution detail
Scope or domain
Modularity
Understandability
Explicit versus implicit knowledge
Procedural versus declarative knowledge
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8. Advantages Declarative representation Procedural 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
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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
ability to incorporate additional information in knowledge structures to help focus attention of promising new directions
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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
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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?
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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?
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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).
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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?
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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?
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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
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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
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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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