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Control Algorithms 2 Chapter 6 Production Systems

Control Algorithms 2 Chapter 6

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Control Algorithms 2 Chapter 6. Production Systems. Emil Post (40’s): production systems as a formal theory of computation. Equivalent to a Turing machine. Set of rewrite rules for strings Newell and Simon (60’s, 70’s, 80’s): General Problem Solver - PowerPoint PPT Presentation

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Page 1: Control Algorithms 2 Chapter 6

Control Algorithms 2Chapter 6

Production Systems

Page 2: Control Algorithms 2 Chapter 6

A Model of Computation

Emil Post (40’s): production systems as a formal theory of computation. Equivalent to a Turing machine. Set of rewrite rules for strings

Newell and Simon (60’s, 70’s, 80’s): General Problem Solver

John Anderson, Newell and Simon (80’s): learning models, ACT*, SOAR

Everyone (80’s): Expert systems

Page 3: Control Algorithms 2 Chapter 6

Components

1. Set of rewrite rulesS NP VPLHS: Condition PartRHS: Action Part

Page 4: Control Algorithms 2 Chapter 6

Components

2. Working Memory--Contains the current state of the world--Contains pattern that is matched against the condition of the production--When a match occurs, an action is performed

Page 5: Control Algorithms 2 Chapter 6

Components

3. Recognize-Act Cycle--Isolate a subset of productions whose conditions match patterns in working memory: conflict set--Choose one of them

---Fire---Change contents of working memory

--Stop when there are no matches

Page 6: Control Algorithms 2 Chapter 6

Example: Production system to generate the set of palindromes over the alphabet {0,1}

Productions1. N 0N02. N 1N13. N 04. N 15. N λ

Iteration Working Memory Conflict Set Fired0 N 1,2,3,4,511 0N0 1,2,3,4,512 00N00 1,2,3,4,523 001N100 1,2,3,4,534 0010100

Page 7: Control Algorithms 2 Chapter 6

Knight’s Tour As a Production System

Given a 3X3 matrixWhat squares can a knight land on

What values of X, Y satisfy mv(X,Y) X,Y are elements of {1,2,…,9}

1 2 3

4 5 6

7 8 9

1. mv(1,8) 7. mv(4,9) 13. mv(8,3)

2. mv(1,6) 8. mv(4,3) 14. mv(8,1)

3. mv(2,9) 9. mv(6,1) 15. mv(9,2)

4. mv(2,7) 10. mv(6,7) 16. mv(9,4)

5. mv(3,4) 11. mv(7,2)

6. mv(3,8) 12. mv(7,6)

Page 8: Control Algorithms 2 Chapter 6

The General Case (write on board)

),(),(),(( yxpathyzpathzxmvzyx

)),(( xxpathx

Page 9: Control Algorithms 2 Chapter 6

Changes

1. Every expression of the form mv(x,y) becomes on(x) on(y)

2. Use no path expression3. Working memory is the current state and

goal state4. Conflict set is the set of rules that match

the current state5. Apply all rules until the current state

equals the goal state

Page 10: Control Algorithms 2 Chapter 6

Productions (write on board)

1. mv(1,8) 7. mv(4,9) 13. mv(8,3)

2. mv(1,6) 8. mv(4,3) 14. mv(8,1)

3. mv(2,9) 9. mv(6,1) 15. mv(9,2)

4. mv(2,7) 10. mv(6,7) 16. mv(9,4)

5. mv(3,4) 11. mv(7,2)

6. mv(3,8) 12. mv(7,6)

1. on(1) -> on(8) 7. on(4) -> on(9) 13. on(8) -> on(3)

2. on(1) -> on(6) 8. on(4) -> on(3) 14. on(8) -> on(1)

3. on(2) -> on(9) 9. on(6) -> on(1) 15. on(9) -> on(2)

4. on(2) -> on(7) 10. on(6) -> on(7) 16. on(9) -> on(4)

5. on(3) -> on(4) 11. on(7) -> on(2)

6. on(3) -> on(8) 12. on(7) -> on(6)

Page 11: Control Algorithms 2 Chapter 6

Can We Get from 1 to 2?

Iteration --Working Memory-- Conflict Set FiredCurrent Goal

0 1 2 1,2 11 8 2 13,14 132 3 2 5,6 5

3 4 2 7,8 74 9 2 15,16 155 2 2 Halt

Page 12: Control Algorithms 2 Chapter 6

Pattern Search

path(1,2) {1/x,2/y}mv(1,z)^path(z,2) {8/z}mv(1,8)^path(8,2) mv(8,z)^path(z,2) {3/z} mv(8,3)^path(3,2) mv(3,z)^path(z,2) {4/z} mv(3,4)^path(4,2) mv(4,z)^path(z,2) {9/z} mv(4,9)^path(9,2) mv(9,z)^path(z,2) {2/z} mv(9,2)^path(2,2) t t t tt

Now look at working memory in the production system

Page 13: Control Algorithms 2 Chapter 6

Equivalences

Production System Pattern Searchproductions mvworking memory path(X,Y)Fire lowest numbered production Choose first rule that unifies

Conclusion:Production Systems and pattern search are

equivalent (almost)

Page 14: Control Algorithms 2 Chapter 6

Almost?

Loop DetectionPattern Search: global list of visited states

(closed)Production Systems: Record previously visited

states in working memory

Two new productions1. assert(X) causes X to be stored in WM2. been(X) is T if X has been visited3. assert(been(X)) records in wm that we’ve

already visited X

Page 15: Control Algorithms 2 Chapter 6

Can be expressed in PC notation like this

),(),(^

))(()^(),((

yxpathyzpath

zbeenassertzbeenzxmvzyx

)),(( xxpathx

Page 16: Control Algorithms 2 Chapter 6

Can We Get from 1 to 7?

Iteration --Working Memory-- Conflict Set FiredCurrent Goal been

0 1 7 1 1,2 11 8 7 8 13,14 132 3 7 3 5,6 53 4 7 4 7,8 74 9 7 9 15,16 155 2 7 2 3,4 3

(firing 3 causes been(9) to fail)2 7 2 4 47 7 7

Notice that this search is data driven

Page 17: Control Algorithms 2 Chapter 6

Can Also Be Goal Driven

Instead of starting with current state=1 and goal = 7

Start with current state = 7 and goal = 1

Page 18: Control Algorithms 2 Chapter 6

Works great for a 3x3 matrix

What about 8x8?Either enumerate all moves or encode them8 possible situations1. d(2),r(1) 5. u(2),r(1)2. d(2),l(1) 6. u(2),l(1)3. d(1),r(2) 7. u(1),r(2)4. d(1),l(2) 8. u(1),l(2)

Page 19: Control Algorithms 2 Chapter 6

Not applicable everywhere

Situation have preconditions:Pre: row <=6, col <=7Situation 1: d(2),r(1)

Requires 4 new functionssq(r,c) returns cell number, left to right, top to

bottom where r is row number, c is column number

plus(r,2) returns r + 2eq(X,Y) T if X = Ylte(X,Y) T if X<=Y

Page 20: Control Algorithms 2 Chapter 6

Encoding of situation 1: d(2),r(1)

mv(sq(R,C),sq(Nr,Nc))

lte(R,6)^eq(Nr,plus(R,2)) ^ %down two rows

lte(C,7)^eq(Nc,plus(c,1)) %right 1 col

There are 7 more analogous to this

Page 21: Control Algorithms 2 Chapter 6

Control Loop for Knight’s Tour

))),(),,((

))),((()),(()),(),,(((

)),(),,(((

)),(),,(((

NcNrsqZcZrsqpath

ZcZrsqbeenassertZcZrsqbeenZcZrsqCRsqmvZcZr

NcNrsqCRsqpathNcNrCR

CRsqCRsqpathCR

Page 22: Control Algorithms 2 Chapter 6

Strength of Production Systems

1. Said to model human cognition2. Separation of knowledge from control3. Natural mapping onto state space

search4. Modularity of production rules5. Simple tracing and explanation—

compare a rule with a line of c++ code6. Language independence

Page 23: Control Algorithms 2 Chapter 6

And (this is the best part)

Production systems are easily rendered in prolog

We’ll consider several versions of the knight’s tour

Page 25: Control Algorithms 2 Chapter 6

Knight2

Put Visited Squares on a List

Page 27: Control Algorithms 2 Chapter 6

Knight4 (continued next class)

Queue Displays Path to Goal

Page 28: Control Algorithms 2 Chapter 6

Cut

!◦Always succeeds the first time it is encountered◦When backtracked to, it causes the entire goal

in which it was contained to failWithout ! (4 2 path moves from 1)With ! (2 2 path moves from 1)

Page 29: Control Algorithms 2 Chapter 6

Farmer Problem

A farmer (f) has a dog (d), a goat (g),and a cabbage (c)

A river runs North and SouthThe farmer has a boat that can hold only

the farmer and one other itemWithout the farmer

◦The goat will eat the cabbage◦The dog will eat the goat

How does the farmer (and his cohort) cross the river

Page 30: Control Algorithms 2 Chapter 6

State Predicate

Define a predicate:state(F,D,G,C)Where F,D,G,C can be set to e or w

indicating the side of the river each is on.

Page 31: Control Algorithms 2 Chapter 6

As State-Space

st(w,w,w,w)

st(e,e,w,w) st(e,w,e,w) s(e,w,w,e)

st(w,w,e,w)

s(e,e,e,w) st(e,w,e,e)

etc.

Page 32: Control Algorithms 2 Chapter 6

Constructing a Move Predicate

st(e,e,-,-) st(w,w,-,-)Means Farmer and dog went from east to

westCan be rewritten:mv(st(X,X,G,C),st(Y,Y,G,C))

Page 33: Control Algorithms 2 Chapter 6

Facts

opp(e,w)opp(w,e)

Givingmv(st(X,X,G,C),st(Y,Y,G,C)) :- opp(X,Y).opp(e,w).opp(w,e).

Four of these: 3 items to move + 1 solo return trip

Page 34: Control Algorithms 2 Chapter 6

Unsafe

Goat and cabbage are together◦unsafe(st(X,D,Y,Y)) if X != Y◦unsafe(st(X,D,Y,Y)) :- opp(X,Y)

Dog and goat are together◦Unsafe(st(X,Y,Y,C) if X != Y◦Unsafe(st(X,Y,Y,C) :- opp(X,Y)

Page 35: Control Algorithms 2 Chapter 6

Never move to an unsafe state

mv(st(X,X,G,C),st(Y,Y,G,C)) :- opp(X,Y), not(unsafe(st(Y,Y,G,C))).

Page 36: Control Algorithms 2 Chapter 6

Finding a Solution

Use Move/Control Technique from Knight3

Farmer Problem