review topics test 1. background topics definitions of artificial intelligence & turing test...
TRANSCRIPT
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Review Topics
Test 1
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Background Topics
• Definitions of Artificial Intelligence & Turing Test
• Physical symbol system hypothesis vs connectionist approaches (neural nets, fuzzy logic, genetic algorithms)
• Application Areas : game playing, automated reasoning, expert systems, natural language understanding, etc.
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AI Topics
• State Space of Problem– Graph model, States, Transitions, Problem
solution– State space search : Backtrack, A* algorithm
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‘Operates with 3 strings
‘ s is current path
‘ ns is states reached from current path
‘ de is states which are dead ends
Private Function extend() As Boolean
Dim ex As Boolean = False
Dim children As New Stack(Of String)
If ns.Count = 0 Then
lbHistory.Items.Add("Goal unreachable ")
Return ex
Exit Function
ElseIf nextV = CInt(goal) Then
lbHistory.Items.Add("Path to goal: " & showS(s))
Return ex
Exit Function
End If
ex = True
children = NextChildren()
If children.Count = 0 Then
'backtrack
While s.Count > 0 And nextV = s.Peek
de.Push(nextV)
labels(CInt(nextV)) = "D"
s.Pop() 'remove first element of s
ns.Pop() 'remove first element of ns
nextV = ns.Peek
'
'ns.Pop()
End While
s.Push(nextV)
labels(nextV) = "S"
Else
'next level
Dim nc As Stack(Of String) = NextChildren()
For Each state In nc
'save children on ns
ns.Push(state)
labels(state) = "N"
Next
nextV = ns.Peek
'get next child
s.Push(nextV)
labels(nextV) = "S" End If
Return ex
End Function
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Backtrack State Space Search
21
3
4
5
7
8
9
1000
6
Start = 1 Goal = 7
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Backtrack State Space Search
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AI Topics
• Automated Reasoning– Propositional Calculus– Predicate Calculus– Rules of Inference– Unification
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AI Topics
• Expert Systems– Database model of expert knowledge– Inference Engine– CLIPS
• Fact List• Rules which assert, modify, or retract facts
– Prolog – also has facts and rules
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English
Every CS major must take Data Structures.
Bill is a CS major.
Bill must take Data Structures.
Predicate Logic(x)( CS_Major(x) Must_Take(x,Data_Structures) )
CS_Major(Bill)Unification is substitution process of constants or variables for variables which makes predicate calculus expressions identical – e.g. Bill/x.
Must_Take(Bill,Data_Structures) (modus ponens)
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PrologCS_Major(Bill) (clause with empty body is fact)
Must_Take(X,Data_Structures) :- CS_Major(X) (rule)
?- Must_Take(Bill,Data_Structures)
CLIPS(deftemplate CSMajor (slot student))
(deftemplate must_take (slot student) (slot course))
(deffacts Majors (CSMajor (student Bill)))
(defrule must_take
(CSMajor (student ?S))
=>
(printout t ?S " must take Data Structures" crlf)
(assert (must_take (student ?S) (course Data_Structures)))
)
DataStructures.txt
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AI Topics
• Natural Language Understanding & Semantics– Syntactic models of language– Syntax directed translation
• Planning and Robotics– Motion planning using state space approach
• Neural Nets– Neuron as binary input/output device with output
depending on whether weighted sum of inputs > threshold
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CLIPS program to emulate a neuron
(deftemplate TGate (slot input1) (slot input2) (slot weight1) (slot weight2) (slot threshold))(deftemplate set1 (slot input1))(deftemplate set2 (slot input2))(deftemplate output (slot thresholdOut))
(deffacts blankInput (set1 (input1 -1)) (set2 (input2 -1)) )
(deffacts TGateKOR (TGate (input1 -1) (input2 -1) (weight1 1) (weight2 1) (threshold 1)))
Defines templates for threshold gate, for setting the inputs and for control facts to keep rules from firing until inputs are specified
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CLIPS program to emulate a neuron
(defrule setInput1 (set1 (input1 -1)) => (bind ?i1 (read)) (assert (set1 (input1 ?i1))) )
(defrule setInput2 (set2 (input2 -1)) => (bind ?i2 (read)) (assert (set2 (input2 ?i2))) )
(defrule applyInputs ?g <- (TGate (input1 -1) (input2 -1) (weight1 1) (weight2 1) (threshold 1)) (set1 (input1 ?i1)) (set2 (input2 ?i2)) (test (<> ?i1 -1)) (test (<> ?i2 -1)) => (retract ?g) (assert (TGate (input1 ?i1) (input2 ?i2) (weight1 1) (weight2 1) (threshold 1))))
Defines rules to set and apply inputs
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CLIPS program to emulate a neuron
((defrule TGateZeroOut (TGate (input1 ?i1) (input2 ?i2) (weight1 ?w1) (weight2 ?w2) (threshold ?t)) (test (<> ?i1 -1)) (test (<> ?i2 -1)) (test (< (+ (* ?i1 ?w1) (* ?i2 ?w2)) ?t)) => (printout t "Output Zero" crlf) (assert (output (thresholdOut 0)))
)
Exercise – Write rule for OneOut
Defines rule for zero output
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AI Topics
• Genetic Algorithms– Population individuals are candidate solutions– Fitness function determines whether
individuals are selected for mating– Mating produces child solutions with
operations of crossover and mutation
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AI Topics
• Knowledge Representation– Semantic Networks
• Network nodes, arcs
– Standardization of relations• Case relations
– Conceptual Dependencies• Four Primitive Concept Classes
– Actions, Objects, Action Modifiers, Object Modifiers– 12 Primitive Action Classes – Atrans, Ptrans, etc.
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AI Topics
• Knowledge Representation– Scripts formalize a stereotyped sequence of
events• Entry & termination conditions, Props, Roles,
Scenes
– Frames formalize stereotyped entities and actions
• Frame ID, Relationship to other Frames, Labeled Slots