1. CPSC 433 : Artificial Intelligence Tutorials T01 & T02
Andrew Kuipers
Please include [CPSC433] in the subject line of any s regarding this course.
CPSC 433 Artificial Intelligence

2. CPSC 433 Artificial Intelligence
Expert Systems
Designed to function similar to a human expert operating within a specific problem domain
Used to:
Provide an answer to a certain problem, or
Clarify uncertainties where normally a human expert would be consulted
Often created to operate in conjunction with humans working within the given problem domain, rather than as a replacement for them.
CPSC 433 Artificial Intelligence

3. Components of an Expert Systems
Knowledge Base
Stores knowledge used by the system, usually represented in a formal logical manner
Knowledge Base
CPSC 433 Artificial Intelligence

4. Components of an Expert Systems
Knowledge Base
Stores knowledge used by the system, usually represented in a formal logical manner
Inference System
Defines how existing knowledge may be used to derive new knowledge
Inference System
Knowledge Base
CPSC 433 Artificial Intelligence

5. Components of an Expert Systems
Knowledge Base
Stores knowledge used by the system, usually represented in a formal logical manner
Inference System
Defines how existing knowledge may be used to derive new knowledge
Search Control
Determines which inference to apply at a given stage of the deduction
Search
Control
Inference System
Knowledge Base
CPSC 433 Artificial Intelligence

6. Components of an Expert Systems
Knowledge Base
Stores knowledge used by the system, usually represented in a formal logical manner
Inference System
Defines how existing knowledge may be used to derive new knowledge
Search Control
Determines which inference to apply at a given stage of the deduction
Search
Control
Inference System
Knowledge Base
CPSC 433 Artificial Intelligence

7. Knowledge Representation
For now, we’ll use a simple If … then … consequence relation using English semantics
ie: If [it is raining] Then [I should wear a coat]
[it is raining] is the antecedent of the relation
[I should wear a coat] is the consequent of the relation
Facts can be understood as consequence relations with an empty antecedent
ie: “If [] Then [it is raining]” is equivalent to the fact that [it is raining]
CPSC 433 Artificial Intelligence

8. Inferring New Knowledge
New knowledge can be constructed from existing knowledge using inference rules
For instance, the inference rule modus ponens can be used to derive the consequent of a consequence relation, given that the antecedent is true
ie:
k1: If [it is raining] Then [I should wear a coat]
k2: [it is raining]
result: [I should wear a coat]
CPSC 433 Artificial Intelligence

9. Goal Directed Reasoning
Inference rules are applied to knowledge base in order to achieve a particular goal
The goal in an expert system is formed as a question, or query, to which we want the answer
ie: [I should wear a coat]?
note: this would read easier in English as “should I wear a coat”, but we want to use the same propositional symbol as is in our knowledge base
The goal of the search is to determine an answer to the query, which may be boolean as above or more complex
CPSC 433 Artificial Intelligence

10. CPSC 433 Artificial Intelligence
Forward Chaining
Forward chaining is a data driven method of deriving a particular goal from a given knowledge base and set of inference rules
Inference rules are applied by matching facts to the antecedents of consequence relations in the knowledge base
The application of inference rules results in new knowledge (from the consequents of the relations matched), which is then added to the knowledge base
CPSC 433 Artificial Intelligence

11. CPSC 433 Artificial Intelligence
Forward Chaining
Inference rules are successively applied to elements of the knowledge base until the goal is reached
A search control method is needed to select which element(s) of the knowledge base to apply the inference rule to at any point in the deduction
CPSC 433 Artificial Intelligence

12. Forward Chaining Example
Knowledge Base:
If [X croaks and eats flies] Then [X is a frog]
If [X chirps and sings] Then [X is a canary]
If [X is a frog] Then [X is colored green]
If [X is a canary] Then [X is colored yellow]
[Fritz croaks and eats flies]
Goal: Finding the color of Fritz.
[Fritz is colored Y]?
CPSC 433 Artificial Intelligence

13. Forward Chaining Example
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
Goal
[Fritz is colored Y]?
CPSC 433 Artificial Intelligence

14. Forward Chaining Example
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
Goal
[Fritz is colored Y]?
CPSC 433 Artificial Intelligence

15. Forward Chaining Example
If [X croaks and eats flies]
Then [X is a frog]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
Goal
[Fritz is colored Y]?
[Fritz croaks and eats flies]
[Fritz is a frog]
CPSC 433 Artificial Intelligence

16. Forward Chaining Example
If [X croaks and eats flies]
Then [X is a frog]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
[Fritz is a frog]
Goal
[Fritz is colored Y]?
[Fritz croaks and eats flies]
[Fritz is a frog]
CPSC 433 Artificial Intelligence

17. Forward Chaining Example
If [X croaks and eats flies]
Then [X is a frog]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
[Fritz is a frog]
Goal
[Fritz is colored Y]?
[Fritz croaks and eats flies]
[Fritz is a frog] ?
CPSC 433 Artificial Intelligence

18. Forward Chaining Example
If [X croaks and eats flies]
Then [X is a frog]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
[Fritz is a frog]
Goal
[Fritz is colored Y]?
[Fritz croaks and eats flies]
[Fritz is a frog]
CPSC 433 Artificial Intelligence

19. Forward Chaining Example
If [X croaks and eats flies]
Then [X is a frog]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
[Fritz is a frog]
Goal
[Fritz is colored Y]?
[Fritz croaks and eats flies]
If [X is a frog]
Then [X is colored green]
[Fritz is a frog]
[Fritz is colored green]
CPSC 433 Artificial Intelligence

20. Forward Chaining Example
If [X croaks and eats flies]
Then [X is a frog]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
[Fritz is a frog]
[Fritz is colored green]
Goal
[Fritz is colored Y]?
[Fritz croaks and eats flies]
If [X is a frog]
Then [X is colored green]
[Fritz is a frog]
[Fritz is colored green]
CPSC 433 Artificial Intelligence

21. Forward Chaining Example
If [X croaks and eats flies]
Then [X is a frog]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
[Fritz is a frog]
[Fritz is colored green]
Goal
[Fritz is colored Y]?
[Fritz croaks and eats flies]
If [X is a frog]
Then [X is colored green]
[Fritz is a frog]
[Fritz is colored green] ?
CPSC 433 Artificial Intelligence

22. Forward Chaining Example
If [X croaks and eats flies]
Then [X is a frog]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
[Fritz is a frog]
[Fritz is colored green]
Goal
[Fritz is colored Y]?
[Fritz croaks and eats flies]
If [X is a frog]
Then [X is colored green]
[Fritz is a frog]
[Fritz is colored green]
CPSC 433 Artificial Intelligence

23. Forward Chaining Example
If [X croaks and eats flies]
Then [X is a frog]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
[Fritz is a frog]
[Fritz is colored green]
Goal
[Fritz is colored Y]?
[Fritz croaks and eats flies]
If [X is a frog]
Then [X is colored green]
[Fritz is a frog]
[Fritz is colored green]
[Fritz is colored Y] ?
Y = green
CPSC 433 Artificial Intelligence

24. CPSC 433 Artificial Intelligence
Backward Chaining
Backward chaining is a goal driven method of deriving a particular goal from a given knowledge base and set of inference rules
Inference rules are applied by matching the goal of the search to the consequents of the relations stored in the knowledge base
When such a relation is found, the antecedent of the relation is added to the list of goals (and not into the knowledge base, as is done in forward chaining)
CPSC 433 Artificial Intelligence

25. CPSC 433 Artificial Intelligence
Backward Chaining
Search proceeds in this manner until a goal can be matched against a fact in the knowledge base
Remember: facts are simply consequence relations with empty antecedents, so this is like adding the ‘empty goal’ to the list of goals
As with forward chaining, a search control method is needed to select which goals will be matched against which consequence relations from the knowledge base
CPSC 433 Artificial Intelligence

26. Backward Chaining Example
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
Goals
[Fritz is colored Y]?
CPSC 433 Artificial Intelligence

27. Backward Chaining Example
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
Goals
[Fritz is colored Y]?
CPSC 433 Artificial Intelligence

28. Backward Chaining Example
[Fritz is colored Y]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
Goals
[Fritz is colored Y]?
If [X is a frog]
Then [X is colored green]
[X is a frog]
CPSC 433 Artificial Intelligence

29. Backward Chaining Example
[Fritz is colored Y]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
Goals
[Fritz is colored Y]?
[X is a frog]
If [X is a frog]
Then [X is colored green]
[X is a frog]
CPSC 433 Artificial Intelligence

30. Backward Chaining Example
[Fritz is colored Y]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
Goals
[Fritz is colored Y]?
[X is a frog]
If [X is a frog]
Then [X is colored green]
[X is a frog]
CPSC 433 Artificial Intelligence

31. Backward Chaining Example
[Fritz is colored Y]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
Goals
[Fritz is colored Y]?
[X is a frog]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[X is a frog]
[X is a canary]
CPSC 433 Artificial Intelligence

32. Backward Chaining Example
[Fritz is colored Y]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
Goals
[Fritz is colored Y]?
[X is a frog]
[X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[X is a frog]
[X is a canary]
CPSC 433 Artificial Intelligence

33. Backward Chaining Example
[Fritz is colored Y]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
Goals
[Fritz is colored Y]?
[X is a frog]
[X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[X is a frog]
[X is a canary]
CPSC 433 Artificial Intelligence

34. Backward Chaining Example
[Fritz is colored Y]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
Goals
[Fritz is colored Y]?
[X is a frog]
[X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[X is a frog]
[X is a canary]
If [X croaks and eats flies]
Then [X is a frog]
[X croaks and eats flies]
CPSC 433 Artificial Intelligence

35. Backward Chaining Example
[Fritz is colored Y]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
Goals
[Fritz is colored Y]?
[X is a frog]
[X is a canary]
[X croaks and eats flies]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[X is a frog]
[X is a canary]
If [X croaks and eats flies]
Then [X is a frog]
[X croaks and eats flies]
CPSC 433 Artificial Intelligence

36. Backward Chaining Example
[Fritz is colored Y]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
Goals
[Fritz is colored Y]?
[X is a frog]
[X is a canary]
[X croaks and eats flies]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[X is a frog]
[X is a canary]
If [X croaks and eats flies]
Then [X is a frog]
[X croaks and eats flies]
CPSC 433 Artificial Intelligence

37. Backward Chaining Example
[Fritz is colored Y]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
Goals
[Fritz is colored Y]?
[X is a frog]
[X is a canary]
[X croaks and eats flies]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[X is a frog]
[X is a canary]
If [X croaks and eats flies]
Then [X is a frog]
[X croaks and eats flies]
[Fritz croaks and eats flies]
X = Fritz, Y = green
CPSC 433 Artificial Intelligence

38. Backward Chaining Example
[Fritz is colored Y]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
Goals
[Fritz is colored Y]?
[X is a frog]
[X is a canary]
[X croaks and eats flies]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[X is a frog]
[X is a canary]
If [X croaks and eats flies]
Then [X is a frog]
[X croaks and eats flies]
[Fritz croaks and eats flies]
X = Fritz, Y = green
CPSC 433 Artificial Intelligence