1. Artificial Intelligence as Representation and Search

2. Representation The purpose of representation is to abstract out the problem features which are non-essential to solving the problem and to capture those features which are essential. An example is “blocks worlds” where the problem is to construct a sequence of actions for a robot arm to transform the initial arrangement of blocks into a goal arrangement. –Essential Features Which blocks are on top of which blocks Which blocks are clear Which blocks are on table

3. Blocks World Representations Predicate Calculus –Predicates : Clear(a), Ontable(a), On(b,a) –Rules :  X ¬  Y on(Y,X)  Clear(X) Arrays or Strings –Stack : ABC State Space Graph –Graph with 3 blocks –Start and Goal states

4. Natural Language Understanding Semantic Net Representation vertebrate bird bluebird smallblue feathers flies isa property colorsize covering isa

5. Tic Tac Toe Representation X O O R1 O O R2 O O R3 X X C1 X X C2 C3 O O OO D1 X O XO D2

6. Blocks World Search Find path from Start State to Goal State

7. Backtrack Search for BW3 solution 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 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) Next nextV = ns.Pop 'get next child s.Push(nextV) labels(nextV) = "S" End If Return ex End Function

8. Backtrack Search for BW3 solution

9. Semantic Net Search find attributes of specified object – CLIPS implementation

11. BFS Semantic Net (deftemplate concept (slot cid) (slot name) (slot depth)) (deftemplate link (slot start) (slot end) (slot linkName)) (deffacts snet (concept (cid 1) (name bluebird) (depth -1)) (concept (cid 2) (name small) (depth -1)) (concept (cid 3) (name blue) (depth -1)) (concept (cid 4) (name feathers) (depth -1)) (concept (cid 5) (name bird) (depth -1)) (concept (cid 6) (name flies) (depth -1)) (concept (cid 7) (name vertebrate) (depth -1)) (link (start 1) (end 2) (linkName size)) (link (start 1) (end 3) (linkName color)) (link (start 1) (end 5) (linkName isa)) (link (start 5) (end 4) (linkName covering)) (link (start 5) (end 6) (linkName property)) (link (start 5) (end 7) (linkName isa)) ) (defrule initsearch (concept (cid 1) (name ?n) (depth -1)) => (assert (concept (cid 1) (name ?n) (depth 0)) ) ) (defrule search (concept (cid ?s) (name ?n) (depth ?d)) (test (> ?d -1)) (concept (cid ?e) (name ?m) (depth -1)) (link (start ?s) (end ?e) (linkName ?x)) => (bind ?dep (+ ?d 1)) (assert (concept (cid ?e) (name ?m) (depth ?dep))) (printout t "Name : " ?m ", relation: " ?x ", depth : " ?dep crlf) )

12. BFS Semantic Net CLIPS (V6.20 03/31/02) CLIPS> (load SemanticNet2.txt) Defining deftemplate: concept Defining deftemplate: link Defining deffacts: snet Defining defrule: initsearch +j Defining defrule: search +j+j+j TRUE CLIPS> (reset) CLIPS> (run) Name : bird, relation: isa, depth : 1 Name : vertebrate, relation: isa, depth : 2 Name : flies, relation: property, depth : 2 Name : feathers, relation: covering, depth : 2 Name : blue, relation: color, depth : 1 Name : small, relation: size, depth : 1 CLIPS>

13. Tic Tac Toe Search

14. If Start then Mark Center Else Find Row, Column or Diagonal which is a win for Player and Mark Else Find Row, Column or Diagonal which is a win for opponent and Mark Else Construct a Fork if possible.. Etc.