Artificial Intelligence – Lecture 9
Transcript of Artificial Intelligence – Lecture 9
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Artificial Intelligence – Lecture 9
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● Prolog
● Other Logics
● Expert Systems
● Probabilities
Lecture plan
• AI in general (ch. 1)
• Search based AI (ch. 4)
• search, games, planning, optimization
• Agents (ch. 8)
• applied AI techniques in robots, software agents, ...
• Knowledge representation (ch. 2)
• semantic networks, frames, logic, resolution
• Expert systems (ch. 3)
• forward/backward chaining, uncertainty, baysian networks
• Natural language processing (ch. 5)
• Machine learning (ch. 7)
• version spaces, decision trees, classification, neural networks
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● Prolog
● Other Logics
● Expert Systems
● Probabilities
Conjunctive Normal Form (CNF)
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● Prolog
● Other Logics
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Resolution
• Requires all expression in CNF
• Only one rule required for all reasoning
A1 ∨ ... ∨A
i ∨ C ∨ A
i+1 ∨ ... ∨ A
N
B1 ∨ ... ∨ B
j ∨ ¬C ∨ B
j+1 ∨ ... ∨ B
M
⇒
A1 ∨ .... ∨ A
N ∨ B
1 ∨ ... ∨ B
M
Example: X ∨ ¬Y Y ∨ Z⇒X ∨ Z
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● Prolog
● Other Logics
● Expert Systems
● Probabilities
Resolution in propositional logic
• To prove that P holds with respect to axioms F
• 1. Convert all propositions in F into CNF
• 2. Negate P and convert into CNF
• 3. Repeat until a contradiction is found• a) Select two clauses
• b) Resolve these two clauses
• c) If resolvent is empty, a contradiction have been found
• Propositional resolution is sound
• Propositional resolution is complete
• The algorithm will always terminate, and will find a contraduction if there is one
• Sooner or later, no more new clauses can be added
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● Prolog
● Other Logics
● Expert Systems
● Probabilities
Examples
• 1.Given X (Y (¬X ⇒ ⇒ ∧ Z) and Y, prove ¬X
• 2. Given A (B⇒ ∨C) and C ¬A, prove ¬A⇒ ∨B
A1 ∨ ... ∨ A
i ∨ C ∨ A
i+1 ∨ ... ∨ A
N
B1 ∨ ... ∨ B
j ∨ ¬C ∨ B
j+1 ∨ ... ∨ B
M
⇒
A1 ∨ .... ∨ A
N ∨ B
1 ∨ ... ∨ B
M
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● Expert Systems
● Probabilities
Limitations of propositional logic
• Can only express statements about specific propositional variables
• Finite number of propositional variables
• How do we express a statements like:• “All birds can fly, Tom is a bird, therefore Tom can fly?”
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● Prolog
● Other Logics
● Expert Systems
● Probabilities
Predicate logic
• More expressive logic
• Predicates
• Functions
• Quantifiers
• No complete inference mechanism
• Terms
• Constants and variables are terms
• f(t1, ..., t
N) is a term iff f is a function and t
1 .., t
N are terms
• Atomic sentence
• p(t1, ..., t
N) is an atomic sentence iff p is a predicate and t
1 .., t
N are
terms
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● Other Logics
● Expert Systems
● Probabilities
Predicate logic – well formed formulas
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Quantifiers
• Ground term / formula
• Scope of quantifiers
• Closed term / formula
• Quantification rules• ∀ x : A is equivalent to ¬∃ x : ¬A
• ∃ x : A is equivalent to ¬∀ x : ¬A
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● Expert Systems
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Example: english to predicate logic
• All purple mushrooms are poisonous.
• No purple mushrooms are poisonous.
• All mushrooms are purple or poisonous.
• All mushrooms are either purple or poisonous, but not both.
• There are exactly two purple mushrooms.
• All purple mushrooms except one are poisonous.
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Semantics
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● Prolog
● Other Logics
● Expert Systems
● Probabilities
Semantics
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● Prolog
● Other Logics
● Expert Systems
● Probabilities
Predicate logic – inference mechanism
• Recall resolution• To prove that P holds with respect to axioms F
• 1. Convert all propositions in F into CNF
• 2. Negate P and convert into CNF
• 3. Repeat until a contradiction is found
• a) Select two clauses
• b) Resolve these two clauses
• c) If resolvent is empty, a contradiction have been found
• Only new step is how to convert predicate logic formulas into CNF
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● Prolog
● Other Logics
● Expert Systems
● Probabilities
Predicate logic – CNF
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● Prolog
● Other Logics
● Expert Systems
● Probabilities
Predicate logic – resolution
• Recall resolution rule – how can we match the two expressions for C?
• Does father(bill, bob) match ¬father(x,y)
• Depends on how we substitute values for variables
A1 ∨ ... ∨ A
i ∨ C ∨ A
i+1 ∨ ... ∨ A
N
B1 ∨ ... ∨ B
j ∨ ¬C ∨ B
j+1 ∨ ... ∨ B
M
⇒A
1 ∨ .... ∨ A
N ∨ B
1 ∨ ... ∨ B
M
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● Prolog
● Other Logics
● Expert Systems
● Probabilities
Unification
• Find most general substitution so that two formulas become syntactically equivalent
The substitution algorithm for L1, L2
1. Set SUBST to nil2. If L1 or L2 is a variable or a constant, then: 2.1 If L1 and L2 are identical, then return success. 2.2 If L1 is a variable then: 2.2.1 If L1 occurs in L2 return fail, 2.2.2 Otherwise set SUBST to {L2/L1} and return success. 2.3 Equivalent case if L2 is a variable. 2.4 Otheriwise FAIL.3. If the initial predicate symbol of L1, L2 are not identical then FAIL.4. If L1, L2 have different number of arguments then FAIL.5. For i=1 to the artity of L1, do: 5.1 Unify i:th argument of L1 with i:th argument of L2 5.2 If unification fails then FAIL. 5.3 Apply subst. to rest of L1, L2 and concatenate with SUBST.6. Success
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● Expert Systems
● Probabilities
Predicate logic example
Jack owns a dog
Every dog owner is an animal lover
No animal lover kills an animal
Either Jack or Curiosity killed the cat, who is named Tuna.
Did Curiosity kill the cat?
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● Prolog
● Other Logics
● Expert Systems
● Probabilities
Computational properties
• Resolution is sound, but not complete• Resolution is refutation complete
• When do we stop the resolution algorithm?
• We cannot generate all logical consequences – there might be infinitly many
• Example: P(x) ∨ ¬P(f(x)), ¬P(a)
• Predicate calculus is undecidable• No effective method deciding if a given formula is a
theorem (holds in every interpretation)
• Some subsets of predicate logic is decidable• eg. Hornclauses
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● Prolog
● Other Logics
● Expert Systems
● Probabilities
Prolog
• Programming language based on pred. logics
• Statements given in implication normal form
• Version of resolution for solving questions
ancestor(X,Y) : father(X,Y).ancestor(X,Y) : father(X,Z), ancestor(Z,Y).father(kalle, erik).father(erik, lars).
ancestor(A, lars)? gives A=erik or A=kalleancestor(kalle, B)? gives B=erik or B=lars
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● Prolog
● Other Logics
● Expert Systems
● Probabilities
Other logics
• Precicate logic allows us to express predicates, functions and rules involving quantifiers• Defines a static world
• How can we express changes over time?
• How can we reason about space?
• Temporal logic(s)• Defines syntax and semantics for reasoning over discrete
timesteps.
• Spatial logic(s)• Reason about space and topologies
• ...