Expert Systems CPSC 433 : Artificial Intelligence Tutorials T01 & T02 Andrew “M” Kuipers
[email protected] note: please include [cpsc 433] in the subject line of any emails regarding this course
CPSC 433 Artificial Intelligence
Components of an Expert Systems • Knowledge Base – Stores knowledge used by the system, usually represented in a formal logical manner
• 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
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]
• Inference System – Defines how existing knowledge may be used to derive new knowledge
– [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
• Search Control – Determines which inference to apply at a given stage of the deduction CPSC 433 Artificial Intelligence
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: “If [] Then [it is raining]” is equivalent to the fact that [it is raining] CPSC 433 Artificial Intelligence
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
• 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
• 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
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
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
• 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
CPSC 433 Artificial Intelligence
Forward Chaining Example
Forward Chaining Example
• Knowledge Base: – – – – –
Knowledge Base If [X croaks and eats flies] Then [X is a frog]
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]
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]?
Goal [Fritz is colored Y]?
CPSC 433 Artificial Intelligence
CPSC 433 Artificial Intelligence
Forward Chaining Example Knowledge Base If [X croaks and eats flies] Then [X is a frog]
Forward Chaining Example If [X croaks and eats flies] Then [X is a frog]
[Fritz croaks and eats flies]
If [X chirps and sings] Then [X is a canary] If [X is a frog] Then [X is colored green]
CPSC 433 Artificial Intelligence
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]
[Fritz is a frog]
If [X is a canary] Then [X is colored yellow]
If [X is a canary] Then [X is colored yellow]
[Fritz croaks and eats flies]
[Fritz croaks and eats flies]
Goal [Fritz is colored Y]?
Goal [Fritz is colored Y]?
CPSC 433 Artificial Intelligence
Forward Chaining Example If [X croaks and eats flies] Then [X is a frog]
[Fritz croaks and eats flies]
Knowledge Base If [X croaks and eats flies] Then [X is a frog]
Forward Chaining Example If [X croaks and eats flies] Then [X is a frog]
[Fritz croaks and eats flies]
If [X chirps and sings] Then [X is a canary] If [X is a frog] Then [X is colored green]
[Fritz is a frog]
If [X chirps and sings] Then [X is a canary] If [X is a frog] Then [X is colored green]
[Fritz is a frog]
If [X is a canary] Then [X is colored yellow]
If [X is a canary] Then [X is colored yellow]
[Fritz croaks and eats flies]
[Fritz croaks and eats flies]
[Fritz is a frog]
[Fritz is a frog]
Goal [Fritz is colored Y]?
?
CPSC 433 Artificial Intelligence
[Fritz croaks and eats flies]
Knowledge Base If [X croaks and eats flies] Then [X is a frog]
Forward Chaining Example If [X croaks and eats flies] Then [X is a frog]
[Fritz croaks and eats flies]
If [X chirps and sings] Then [X is a canary] If [X is a frog] Then [X is colored green]
[Fritz is a frog]
[Fritz is a frog]
If [X is a frog] Then [X is colored green]
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 croaks and eats flies]
[Fritz is colored green]
[Fritz is a frog]
Goal [Fritz is colored Y]?
Goal [Fritz is colored Y]?
CPSC 433 Artificial Intelligence
CPSC 433 Artificial Intelligence
Forward Chaining Example [Fritz croaks and eats flies]
Knowledge Base If [X croaks and eats flies] Then [X is a frog]
Forward Chaining Example If [X croaks and eats flies] Then [X is a frog]
[Fritz croaks and eats flies]
If [X chirps and sings] Then [X is a canary]
[Fritz is a frog]
If [X is a frog] Then [X is colored green]
If [X is a frog] Then [X is colored green]
[Fritz is a frog]
If [X is a canary] Then [X is colored yellow]
[Fritz is a frog]
If [X is a frog] Then [X is colored green]
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 colored green]
[Fritz is a frog]
[Fritz is colored green]
[Fritz is colored green]
Goal [Fritz is colored Y]?
CPSC 433 Artificial Intelligence
Knowledge Base If [X croaks and eats flies] Then [X is a frog] If [X chirps and sings] Then [X is a canary]
[Fritz croaks and eats flies]
[Fritz is colored green]
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 canary] Then [X is colored yellow]
If [X croaks and eats flies] Then [X is a frog]
Goal [Fritz is colored Y]?
CPSC 433 Artificial Intelligence
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]
? CPSC 433 Artificial Intelligence
Goal [Fritz is colored Y]?
Forward Chaining Example If [X croaks and eats flies] Then [X is a frog]
[Fritz croaks and eats flies]
Knowledge Base If [X croaks and eats flies] Then [X is a frog]
Forward Chaining Example If [X croaks and eats flies] Then [X is a frog]
[Fritz croaks and eats flies]
If [X chirps and sings] Then [X is a canary]
[Fritz is a frog]
If [X is a frog] Then [X is colored green]
If [X is a frog] Then [X is colored green]
If [X chirps and sings] Then [X is a canary]
[Fritz is a frog]
If [X is a canary] Then [X is colored yellow]
If [X is a frog] Then [X is colored green]
[Fritz croaks and eats flies]
[Fritz is colored green]
[Fritz is a frog]
CPSC 433 Artificial Intelligence
If [X is a canary] Then [X is colored yellow]
[Fritz is colored Y] ?
[Fritz is a frog] [Fritz is colored green] Goal [Fritz is colored Y]?
Y = green
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
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
Backward Chaining Example
CPSC 433 Artificial Intelligence
If [X is a frog] Then [X is colored green]
[Fritz croaks and eats flies]
[Fritz is colored green]
[Fritz is colored green] Goal [Fritz is colored Y]?
Knowledge Base If [X croaks and eats flies] Then [X is a frog]
Backward Chaining Example
Knowledge Base 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 chirps and sings] Then [X is a canary]
If [X is a frog] Then [X is colored green]
If [X is a frog] Then [X is colored green]
If [X is a canary] Then [X is colored yellow]
If [X is a canary] Then [X is colored yellow]
[Fritz croaks and eats flies]
[Fritz croaks and eats flies]
Goals [Fritz is colored Y]?
Goals [Fritz is colored Y]?
CPSC 433 Artificial Intelligence
Backward Chaining Example [Fritz is colored Y]
Backward Chaining Example [Fritz is colored Y]
Knowledge Base If [X croaks and eats flies] Then [X is a frog]
If [X is a frog] Then [X is colored green]
If [X chirps and sings] Then [X is a canary]
Knowledge Base If [X croaks and eats flies] Then [X is a frog]
If [X is a frog] Then [X is colored green]
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]
[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]
[Fritz croaks and eats flies]
[Fritz croaks and eats flies]
Goals [Fritz is colored Y]?
Goals [Fritz is colored Y]? [X is a frog]
CPSC 433 Artificial Intelligence
CPSC 433 Artificial Intelligence
Backward Chaining Example [Fritz is colored Y]
Backward Chaining Example [Fritz is colored Y]
Knowledge Base If [X croaks and eats flies] Then [X is a frog]
If [X is a frog] Then [X is colored green]
If [X chirps and sings] Then [X is a canary]
If [X is a frog] Then [X is colored green]
Knowledge Base If [X croaks and eats flies] Then [X is a frog]
If [X is a canary] Then [X is colored yellow]
If [X is a frog] Then [X is colored green] If [X is a canary] Then [X is colored yellow]
[X is a frog]
If [X is a frog] Then [X is colored green]
[X is a frog]
[X is a canary]
[Fritz croaks and eats flies]
Goals [Fritz is colored Y]?
Goals [Fritz is colored Y]?
[X is a frog]
[X is a frog]
CPSC 433 Artificial Intelligence
Backward Chaining Example If [X is a frog] Then [X is colored green]
Backward Chaining Example [Fritz is colored Y]
Knowledge Base If [X croaks and eats flies] Then [X is a frog]
If [X is a canary] Then [X is colored yellow]
If [X chirps and sings] Then [X is a canary]
If [X is a frog] Then [X is colored green]
Knowledge Base If [X croaks and eats flies] Then [X is a frog]
If [X is a canary] Then [X is colored yellow]
If [X is a frog] Then [X is colored green]
[X is a frog]
[X is a canary]
CPSC 433 Artificial Intelligence
If [X is a canary] Then [X is colored yellow]
[Fritz croaks and eats flies]
CPSC 433 Artificial Intelligence
[Fritz is colored Y]
If [X chirps and sings] Then [X is a canary]
If [X is a canary] Then [X is colored yellow]
If [X chirps and sings] Then [X is a canary] If [X is a frog] Then [X is colored green]
[X is a frog]
[X is a canary]
If [X is a canary] Then [X is colored yellow]
[Fritz croaks and eats flies]
[Fritz croaks and eats flies]
Goals [Fritz is colored Y]?
Goals [Fritz is colored Y]?
[X is a frog]
[X is a frog]
[X is a canary]
[X is a canary]
CPSC 433 Artificial Intelligence
Backward Chaining Example [Fritz is colored Y] If [X is a frog] Then [X is colored green]
Backward Chaining Example [Fritz is colored Y]
Knowledge Base If [X croaks and eats flies] Then [X is a frog]
If [X is a canary] Then [X is colored yellow]
If [X chirps and sings] Then [X is a canary]
If [X is a frog] Then [X is colored green]
Knowledge Base If [X croaks and eats flies] Then [X is a frog]
If [X is a canary] Then [X is colored yellow]
If [X is a frog] Then [X is colored green]
[X is a frog]
[X is a canary] If [X croaks and eats flies] Then [X is a frog]
[X croaks and eats flies]
If [X is a canary] Then [X is colored yellow]
If [X is a frog] Then [X is colored green]
[X is a frog]
[X is a canary]
[Fritz croaks and eats flies]
If [X croaks and eats flies] Then [X is a frog]
Goals [Fritz is colored Y]? [X is a frog]
If [X chirps and sings] Then [X is a canary]
[X croaks and eats flies]
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 is a canary] [X croaks and eats flies]
CPSC 433 Artificial Intelligence
CPSC 433 Artificial Intelligence
Backward Chaining Example [Fritz is colored Y] If [X is a frog] Then [X is colored green]
Backward Chaining Example [Fritz is colored Y]
Knowledge Base If [X croaks and eats flies] Then [X is a frog]
If [X is a canary] Then [X is colored yellow]
If [X chirps and sings] Then [X is a canary]
If [X is a frog] Then [X is colored green]
Knowledge Base If [X croaks and eats flies] Then [X is a frog]
If [X is a canary] Then [X is colored yellow]
If [X is a frog] Then [X is colored green]
[X is a frog]
[X is a canary] If [X croaks and eats flies] Then [X is a frog]
[X croaks and eats flies]
If [X is a canary] Then [X is colored yellow]
If [X is a frog] Then [X is colored green]
[X is a frog]
[X is a canary]
[Fritz croaks and eats flies]
If [X croaks and eats flies] Then [X is a frog]
Goals [Fritz is colored Y]? [X is a frog]
[X croaks and eats flies]
[Fritz croaks and eats flies]
[X is a canary] [X croaks and eats flies]
CPSC 433 Artificial Intelligence
[Fritz is colored Y] If [X is a frog] Then [X is colored green]
Knowledge Base If [X croaks and eats flies] Then [X is a frog]
If [X is a canary] Then [X is colored yellow]
If [X chirps and sings] Then [X is a canary] If [X is a frog] Then [X is colored green]
[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]
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 = Fritz, Y = green CPSC 433 Artificial Intelligence
[X croaks and eats flies]
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 = Fritz, Y = green CPSC 433 Artificial Intelligence
Backward Chaining Example
If [X chirps and sings] Then [X is a canary]
[X croaks and eats flies]
