Review Topics Test 1 Background Topics

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 lb. History. Items. Add("Goal unreachable ") Return ex Exit Function Else. If next. V = CInt(goal) Then lb. History. Items. Add("Path to goal: " & show. S(s)) Return ex Exit Function End If ex = True children = Next. Children() If children. Count = 0 Then 'backtrack While s. Count > 0 And next. V = s. Peek de. Push(next. V) labels(CInt(next. V)) = "D" s. Pop() 'remove first element of s ns. Pop() 'remove first element of ns next. V = ns. Peek ' 'ns. Pop() End While s. Push(next. V) labels(next. V) = "S" Else 'next level Dim nc As Stack(Of String) = Next. Children() For Each state In nc 'save children on ns ns. Push(state) labels(state) = "N" Next next. V = ns. Peek 'get next child s. Push(next. V) labels(next. V) = "S" End If Return ex End Function

Backtrack State Space Search 8 2 1 5 9 3 6 4 7 Start = 1 Goal = 7 1 0 0 0

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)

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))) )

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 Defines templates for threshold gate, for setting the inputs and for control facts to keep rules from firing until inputs are specified (deftemplate TGate (slot input 1) (slot input 2) (slot weight 1) (slot weight 2) (slot threshold)) (deftemplate set 1 (slot input 1)) (deftemplate set 2 (slot input 2)) (deftemplate output (slot threshold. Out)) (deffacts blank. Input (set 1 (input 1 -1)) (set 2 (input 2 -1)) ) (deffacts TGate. KOR (TGate (input 1 -1) (input 2 -1) (weight 1 1) (weight 2 1) (threshold 1)))

CLIPS program to emulate a neuron Defines rules to set and apply inputs (defrule set. Input 1 (set 1 (input 1 -1)) => (bind ? i 1 (read)) (assert (set 1 (input 1 ? i 1))) ) (defrule set. Input 2 (set 2 (input 2 -1)) => (bind ? i 2 (read)) (assert (set 2 (input 2 ? i 2))) ) (defrule apply. Inputs ? g <- (TGate (input 1 -1) (input 2 -1) (weight 1 1) (weight 2 1) (threshold 1)) (set 1 (input 1 ? i 1)) (set 2 (input 2 ? i 2)) (test (<> ? i 1 -1)) (test (<> ? i 2 -1)) => (retract ? g) (assert (TGate (input 1 ? i 1) (input 2 ? i 2) (weight 1 1) (weight 2 1) (threshold 1))) )

CLIPS program to emulate a neuron Defines rule for zero output ((defrule TGate. Zero. Out (TGate (input 1 ? i 1) (input 2 ? i 2) (weight 1 ? w 1) (weight 2 ? w 2) (threshold ? t)) (test (<> ? i 1 -1)) (test (<> ? i 2 -1)) (test (< (+ (* ? i 1 ? w 1) (* ? i 2 ? w 2)) ? t)) => (printout t "Output Zero" crlf) (assert (output (threshold. Out 0))) ) Exercise – Write rule for One. Out

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