review topics test 1. background topics definitions of artificial intelligence & turing test...

Review Topics

Test 1

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.

AI Topics

• State Space of Problem– Graph model, States, Transitions, Problem

solution– State space search : Backtrack, A* algorithm

‘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

Backtrack State Space Search

21

3

4

5

7

8

9

1000

6

Start = 1 Goal = 7

Backtrack State Space Search

AI Topics

• Automated Reasoning– Propositional Calculus– Predicate Calculus– Rules of Inference– Unification

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

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)

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

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

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

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

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

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

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.

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