1. Knowledge Representation
CS 171/271
(Chapter 10)
Some text and images in these slides were drawn from Russel & Norvig’s published material

2. Using Logic for Knowledge Representation
Propositional and First-Order Logic describe the technology for knowledge-based agents
What gets into these knowledge bases?
Categories, objects, substances
Agent actions, situations, events
Beliefs
Uncertain information
Dynamic information

3. Categories Representing categories Related notions
As predicates: Singer( Madonna)
As objects: Member( Madonna, Singers ) or Madonna  Singers
Related notions
Subclasses/subcategories (  )
Categories versus properties
Categories of categories

4. Relationships between Categories
Disjoint categories
Disjoint( {Animals, Vegetables} )
Exhaustive decomposition
ExhaustiveDecomposition( {Faculty,Staff,Administrators}, UniversityPersonnel )
Partition
Partition( {Males,Females}, Persons )

5. Physical Composition Part-of relationship Composite objects
With structural properties (e.g., car as something with wheels and other things attached to it)
Bunches (e.g., apples in a bag)

6. Measurements Measures as objects Measure: a number with units Example
Length(L1) = Inches(1.5)

7. Substances and Objects
World not necessarily individuated
Not always divided into distinct objects
In the English language
Count nouns versus mass nouns
Has special properties
Example: x  Butter  PartOf( y,x )  y  Butter

8. Actions
In the context of an agent, we need to represent actions and consequences
Need to aslo worry about percepts, time, changing situations, and many others
Situation calculus or event calculus

9. Situation Calculus Situations Fluents Eternal predicates
Objects/terms that stand for the states between actions carried out (initial situation and generated situations after an action)
Result( a, s ) names the resulting state when action a is executed in situation s
Fluents
Predicates/functions that vary across situations
Eternal predicates
Not dependent on situation

10. Actions in Situation Calculus
Possibility Axiom
preconditions  Poss( action, situation )
Example: “can move to a square if it is adjacent”
Effect Axiom
Poss( action, situation )  changes
Example: “moving updates agent position”

11. Frame Problem
In the real world, most things stay the same from one situation to the next
Change occurs for a tiny fraction of the fluents
Note: effect action would often only note those changes
Frame problem: problem of representing those that stay the same
Efficiency/compactness issue
Representational versus Inferential

12. Event Calculus Time as objects Fluents hold at points in time
Reasoning can be made over time intervals

13. Other Challenges Beliefs Uncertain Information Dynamic Information
Read Chapter 10