1. CS 484 – Artificial Intelligence
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CS 484 – Artificial Intelligence

2. Representations: Semantic Nets, Frames, and Trees
Lecture 3
First too representation used for knoweledge based systems (toward strong AI)
Trees broader application – games, planning, etc.

3. The Need for a Good Representation
A computer needs a representation of a problem in order to solve it.
A representation must be:
Efficient – not wasteful in time or resources.
Useful – allows the computer to solve the problem.
Meaningful – really relates to the problem.
CS 484 – Artificial Intelligence

4. CS 484 – Artificial Intelligence
Semantic Nets
A graph with nodes, connected by edges.
The nodes represent objects or properties.
The edges represent relationships between the objects.
Label to indicate nature of relationship
CS 484 – Artificial Intelligence

5. CS 484 – Artificial Intelligence
A Simple Semantic Net
Arrows indicate the direction of the relationship
Represents knowledge about objects and relationships that exist between objects
semantic nets can be reasoned about in order to produce systems that have knowledge about a particular domain
Can't represent negations
specific objects are instances of a particular class
Is Cheese instance or class?
Rule – an object without an "is-a" relationship is a class
Semantic Net not always that best representation – bad for a dictionary
real world semantic net – Word Net words groups as synsets and interlinked by means of conceptual-semantic and lexical relations
CS 484 – Artificial Intelligence

6. CS 484 – Artificial Intelligence
Create a Semantic Net
A Ford is a type of car. Bob owns two cars. Bob parks his car at home. His house is in California, which is a state. Sacramento is the state capital of California. Cars drive on the freeway, such as Route 101 and Highway 81.
CS 484 – Artificial Intelligence

7. CS 484 – Artificial Intelligence
Your Semantic Web
Bob ->(owns) 2
2 -> (instance of) cars
Ford ->(type of) car
House -> (own by) Bob
2 -> (parked at) House
house -> (located in) California
Sacromento -> (capital of) California
car -> (driven on) freeway
Route 101 -> (instance of) freeway
Highway 81 -> (instance of) freeway
CS 484 – Artificial Intelligence

8. CS 484 – Artificial Intelligence
Inheritance
Inheritance is the process by which a subclass inherits properties from a superclass.
Example:
Mammals give birth to live young.
Fido is a mammal.
Therefore Fido gives birth to live young.
In some cases, as in the example above, inherited values may need to be overridden. (Fido may be a mammal, but if he’s male then he probably won’t give birth).
CS 484 – Artificial Intelligence

9. CS 484 – Artificial Intelligence
Frames
A frame system consists of a number of frames, connected by edges, like a semantic net.
Class frames describe classes.
Instance frames describe instances.
Each frame has a number of slots.
Each slot can be assigned a slot value.
instances are objects
object can be a physical object
object can be property, place situation, or feeling
frames are an object-oriented representation used to build expert systems
CS 484 – Artificial Intelligence

10. Frames: A Simple Example
fido is a dog – generalization
dog is more general than fido
CS 484 – Artificial Intelligence

11. CS 484 – Artificial Intelligence
Other relationships
Aggregation – one object being part of another object
Fido has a tail
Association – explains how objects are related to each other
"chases relationship" – how Fido and Fang are related
tail is a part of Fido
Fido is an aggregation of dog parts
CS 484 – Artificial Intelligence

12. Create a frame-based representation
A Ford is a type of car. Bob owns two cars. Bob parks his car at home. His house is in California, which is a state. Sacramento is the state capital of California. Cars drive on the freeway, such as Route 101 and Highway 81.
CS 484 – Artificial Intelligence

13. Your Frame-Based Representation
Bob: owns->house, owns->2
house: located in-> CA
Sacromento: capital->CA
2: instance of->car, parked at->house
Ford: type of->car
car: driven on->freeway
Route 101: instance of-> freeway
Highway 81: instance of->freeway
CS 484 – Artificial Intelligence

14. CS 484 – Artificial Intelligence
Why Are Frames Useful?
Used as a data structure by Expert Systems
All information about an object stored in one place
As opposed to rule-based systems
In real world systems frames have a large number of slots
Searching for all relevant information would take a long time
Expert systems were the bread and butter of AI in the 80s. Digital Equipment Corporation created an Expert System for order for new computers – saved the company $40 million a year
CS 484 – Artificial Intelligence

15. CS 484 – Artificial Intelligence
Search Space
A set of possible choices in a given problem
One or more are the solution to the problem
Identify one or more goals
Identify one or more paths to those goals
Problem
set of states
states connected by paths that represent actions
CS 484 – Artificial Intelligence

16. CS 484 – Artificial Intelligence
Search Trees
Semantic trees – a type of semantic net.
Used to represent search spaces.
Root node has no predecessor.
Leaf nodes have no successors.
Goal nodes (of which there may be more than one) represent solutions to a problem.
CS 484 – Artificial Intelligence

17. Search Trees: An Example
A is the root node.
L is the goal node.
H, I, J, K, M, N and O are leaf nodes.
There is only one complete path:
A, C, F, L
CS 484 – Artificial Intelligence

18. Example: Missionaries and Cannibals
Three missionaries and three cannibals
Want to cross a river using one canoe.
Canoe can hold up to two people.
Can never be more cannibals than missionaries on either side of the river.
Aim: To get all safely across the river without any missionaries being eaten.
CS 484 – Artificial Intelligence

19. CS 484 – Artificial Intelligence
A Representation
The first step in solving the problem is to choose a suitable representation.
We will show number of cannibals, missionaries and canoes on each side of the river.
Start state is therefore:
C=3,M=3,B=1 0,0,0
CS 484 – Artificial Intelligence

20. A Simpler Representation
In fact, since the system is closed, we only need to represent one side of the river, as we can deduce the other side.
We will represent the finishing side of the river, and omit the starting side.
So start state is:
0,0,0
CS 484 – Artificial Intelligence

21. CS 484 – Artificial Intelligence
Operators
Now we have to choose suitable operators that can be applied:
Move one cannibal across the river.
Move two cannibals across the river.
Move one missionary across the river.
Move two missionaries across the river.
Move one missionary and one cannibal.
CS 484 – Artificial Intelligence

22. CS 484 – Artificial Intelligence
The Search Tree
Cycles have been removed.
Nodes represent states, edges represent operators.
There are two shortest paths that lead to the solution.
CS 484 – Artificial Intelligence

23. What Other Representations are Possible?
CS 484 – Artificial Intelligence

24. Combinatorial Explosion
Problems that involve assigning values to a set of variables can grow exponentially with the number of variables.
Some such problems can be extremely hard to solve (NP-Complete, NP-Hard).
Reduce state space
select good representation help
using heuristics (see chapter 4).
number of configurations of people playing 4 square (position matters) vs. 5 square vs. 100 square
CS 484 – Artificial Intelligence

25. CS 484 – Artificial Intelligence
Problem Reduction
Breaking a problem down into smaller sub-problems (or sub-goals).
Can be represented using goal trees (or and-or trees).
Nodes in the tree represent sub-problems.
The root node represents the overall problem.
Some nodes are and nodes, meaning all their children must be solved.
CS 484 – Artificial Intelligence

26. Problem Reduction: Example
move top disk to different poll or
move a,b,c to 2, d to 3 (sub goal)
CS 484 – Artificial Intelligence