CPSC 433 : Artificial Intelligence Tutorials T01 & T02

CPSC 433 Artificial Intelligence

CPSC 433 : Artificial Intelligence Tutorials T01 & T02

Andrew “M” [email protected]

note: please include [cpsc 433] in the subject line of any emails regarding this course

http://www.ucalgary.ca/


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



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



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]



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]



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



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



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



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]?




Knowledge BaseIf [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]?




If [X croaks and eats flies] Then [X is a frog] [Fritz croaks and eats flies]

[Fritz is a frog]











[Fritz is a frog]






[Fritz is a frog]






[Fritz is a frog]

?






[Fritz is a frog]






[Fritz is a frog]






[Fritz is a frog]






[Fritz is a frog]If [X is a frog]Then [X is colored green]

[Fritz is colored green]






[Fritz is a frog]













[Fritz is a frog]









?






[Fritz is a frog]














[Fritz is a frog]








[Fritz is colored green] [Fritz is colored Y] ?






[Fritz is a frog]


Goal[Fritz is colored Y]?Y = green



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)



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



Backward Chaining Example






Goals[Fritz is colored Y]?










[Fritz is colored Y]

If [X is a frog]Then [X is colored green]

[X is a frog]










[X is a frog]



[X is a frog]










[X is a frog]



[X is a frog]

If [X is a canary]Then [X is colored yellow]

[X is a canary]










[X is a frog]

[X is a canary]



[X is a frog]


[X is a canary]










[X is a frog]

[X is a canary]



[X is a frog]


[X is a canary]

If [X croaks and eats flies]Then [X is a frog]

[X croaks and eats flies]










[X is a frog]

[X is a canary]




[X is a frog]


[X is a canary]












[X is a frog]

[X is a canary]




[X is a frog]


[X is a canary]


[X croaks and eats flies] [Fritz croaks and eats flies]

X = Fritz, Y = green










[X is a frog]

[X is a canary]





[X is a frog] [X is a canary]


[X croaks and eats flies] [Fritz croaks and eats flies]

X = Fritz, Y = green


CPSC 433 : Artificial Intelligence Tutorials T01 & T02

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