1. Review Topics Test 1

2. 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.

3. AI Topics State Space of Problem –Graph model, States, Transitions, Problem solution –State space search : Backtrack, A * algorithm

4. ‘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

5. Backtrack State Space Search 2 1 3 4 5 7 8 9 10001000 6 Start = 1 Goal = 7

6. Backtrack State Space Search

7. AI Topics Automated Reasoning –Propositional Calculus –Predicate Calculus –Rules of Inference –Unification

8. 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

9. 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 )

10. Prolog CS_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))) )

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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.

17. 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