How to make fact in multiple type? like in this code get the same rank fact together.
(P X Y) means X is Y's elder member
i had tried this:
(deffacts people
(P a b)
(P b c)
(P a d)
(P d e)
(P d f)
)
(defrule ranking
(P ?x ?y)
(P ?y ?z)
=>
(assert (R ?x $?y $?z))
)
i want to make a complete seniority in the family,
and get (R a bd cef), but i just get (R a b c) (R a d e) (R a d f)
can u help me?
It's a bit more complicated than what you've attempted, particularly if you want it to work properly for more than 3 generations and/or multiple family groups.
CLIPS>
(defmethod concat$ ((?m1 MULTIFIELD) (?m2 MULTIFIELD (>= (length$ ?m1) (length$ ?m2))))
(bind ?rv (create$))
(loop-for-count (?i 1 (length$ ?m2))
(bind ?rv (create$ ?rv (sym-cat (nth$ ?i ?m1) (nth$ ?i ?m2)))))
(create$ ?rv (mv-subseq (+ 1 (length$ ?m2)) (length$ ?m1) ?m1)))
CLIPS>
(defmethod concat$ ((?m1 MULTIFIELD) (?m2 MULTIFIELD (< (length$ ?m1) (length$ ?m2))))
(bind ?rv (create$))
(loop-for-count (?i 1 (length$ ?m1))
(bind ?rv (create$ ?rv (sym-cat (nth$ ?i ?m1) (nth$ ?i ?m2)))))
(create$ ?rv (mv-subseq (+ 1 (length$ ?m1)) (length$ ?m2) ?m2)))
CLIPS>
(deffacts people
(P a b) ; Family 1
(P b c)
(P a d)
(P d e)
(P d f)
(P g h) ; Family 2
(P h j)
(P h k)
(P k l)
(P k m)
(P k n)
(P j o)
(P j p)
(P j q)
(P q r)
(P q s))
CLIPS>
(defrule copy
(P ?x ?y)
=>
(assert (R ?x ?y)))
CLIPS>
(defrule extend
?f1 <- (R $?b ?x ?ym $?e1)
?f2 <- (R ?y $?z)
(test (str-index ?y ?ym))
=>
(retract ?f1 ?f2)
(assert (R ?b ?x ?ym (concat$ ?e1 ?z))))
CLIPS>
(defrule combine
?f1 <- (R ?x $?b ?y1 $?e1)
?f2 <- (R ?x $?b ?y2&~?y1 $?e2)
=>
(retract ?f1 ?f2)
(assert (R ?x ?b (sym-cat ?y1 ?y2) (concat$ ?e1 ?e2))))
CLIPS> (reset)
CLIPS> (run)
CLIPS> (facts)
f-0 (initial-fact)
f-1 (P a b)
f-2 (P b c)
f-3 (P a d)
f-4 (P d e)
f-5 (P d f)
f-6 (P g h)
f-7 (P h j)
f-8 (P h k)
f-9 (P k l)
f-10 (P k m)
f-11 (P k n)
f-12 (P j o)
f-13 (P j p)
f-14 (P j q)
f-15 (P q r)
f-16 (P q s)
f-37 (R g h jk opqlmn rs)
f-46 (R a bd cef)
For a total of 19 facts.
CLIPS>
Related
I'm trying to find the most common element across multiple multislot entry of type symbol and I don't seem to get a decent way to extracts the content of those multislot to single entry to iterate over.
==================================
(deftemplate chain ""
(multislot edge
(type SYMBOL))
)
(assert (chain (edge a b c d e f g)))
(assert (chain (edge d e f g h k l)))
(assert (chain (edge e o p q r s f)))
(deffunction find_most_common_edge ()
(bind ?edge (create$))
(bind ?counted_edge (create$))
(bind ?largest_count 0)
(do-for-all-facts ((?s chain)) TRUE
(loop-for-count (length$ ?s:edge) (?s1 (expand$ ?s:edge))
(if (not (member$ ?s1 ?counted_edge))
then
(bind ?counted_edge (create$ ?s1 ?counted_edge))
(bind ?count (length$ (find-all-facts ((?s2 chain)) (member$ ?s1 ?s2:edge))))
(if (= ?count ?largest_count)
then
(bind ?edge (create$ ?s1 ?edge))
else
(if (> ?count ?largest_count)
then
(bind ?largest_count ?count)
(bind ?edge (create$ ?s1)))))))
(return ?edge))
Using functions:
CLIPS (6.4 2/9/21)
CLIPS>
(deftemplate chain
(multislot edge (type SYMBOL)))
CLIPS>
(deffacts start
(chain (edge a b c d e f g))
(chain (edge d e f g h k l))
(chain (edge e o p q r s f)))
CLIPS>
(deffunction get-all-edges ()
(bind ?all-edges (create$))
(do-for-all-facts ((?f chain)) TRUE
(bind ?all-edges (create$ ?all-edges ?f:edge)))
(return ?all-edges))
CLIPS>
(deffunction count-edge (?e ?all-edges)
(bind ?all-length (length$ ?all-edges))
(return (- ?all-length (length$ (delete-member$ ?all-edges ?e)))))
CLIPS>
(deffunction remove-duplicates ($?mf)
(bind ?rv (create$))
(foreach ?v ?mf
(if (not (member$ ?v ?rv))
then
(bind ?rv (create$ ?rv ?v))))
(return ?rv))
CLIPS>
(deffunction find-most-common-edge ()
(bind ?all-edges (get-all-edges))
(bind ?unique-edges (remove-duplicates ?all-edges))
(bind ?largest-count 0)
(bind ?most-common (create$))
(foreach ?e ?unique-edges
(bind ?count (count-edge ?e ?all-edges))
(if (= ?count ?largest-count)
then
(bind ?most-common (create$ ?most-common ?e))
else
(if (> ?count ?largest-count)
then
(bind ?largest-count ?count)
(bind ?most-common (create$ ?e)))))
(return ?most-common))
CLIPS> (reset)
CLIPS> (find-most-common-edge)
(e f)
CLIPS>
Using rules:
CLIPS> (clear)
CLIPS>
(deftemplate chain
(slot id (default-dynamic (gensym*)))
(multislot edge (type SYMBOL)))
CLIPS>
(deftemplate count
(slot edge)
(multislot ids))
CLIPS>
(deffacts start
(chain (edge a b c d e f g))
(chain (edge d e f g h k l))
(chain (edge e o p q r s f))
(find-common-edge))
CLIPS>
(defrule create-count
(logical (find-common-edge))
(chain (id ?id) (edge $? ?e $?))
(not (count (edge ?e)))
=>
(assert (count (edge ?e) (ids ?id))))
CLIPS>
(defrule add-to-count
(logical (find-common-edge))
(chain (id ?id) (edge $? ?e $?))
?f <- (count (edge ?e) (ids $?ids))
(test (not (member$ ?id ?ids)))
=>
(modify ?f (ids ?ids ?id)))
CLIPS>
(defrule most-common-edge
(declare (salience -10))
?f <- (find-common-edge)
(count (edge ?e) (ids $?r1))
(not (and (count (edge ~?e) (ids $?r2))
(test (> (length$ ?r2) (length$ ?r1)))))
=>
(bind ?length (length$ ?r1))
(bind ?edges (create$))
(do-for-all-facts ((?c count))
(eq (length$ ?c:ids) ?length)
(bind ?edges (create$ ?edges ?c:edge)))
(assert (most-common-edges ?edges))
(retract ?f))
CLIPS> (reset)
CLIPS> (run)
CLIPS> (facts)
f-1 (chain (id gen4) (edge a b c d e f g))
f-2 (chain (id gen5) (edge d e f g h k l))
f-3 (chain (id gen6) (edge e o p q r s f))
f-20 (most-common-edges e f)
For a total of 4 facts.
CLIPS>
I need to make complement and difference operations between two sets. I've a example, to do union between two sets, I can reuse this code to make these two other operations.
Thanks
The union example, that I've is:
(deffacts datos-iniciales
(conjunto B C A D E E B C E)
(conjunto E E B F D E))
(defrule inicio
=>
(assert (union)))
(defrule union
?h <- (union $?u)
(conjunto ? $? ?e $?)
(not (union $? ?e $?))
=>
(retract ?h)
(assert (union ?e $?u)))
Specifically, which part of the program should be changed? Thx
Here's how you can compute all three leaving the set-1 and set-2 facts unmodified, ignoring duplicate members, and sorting the results.
CLIPS (6.31 6/12/19)
CLIPS>
(deffacts datos-iniciales
(set-1 B C A D E E B C E)
(set-2 E E B F D E))
CLIPS>
(deffacts universe
(universe A B C D E F G H I J K))
CLIPS>
(deffunction str-sort (?a ?b)
(> (str-compare (sym-cat ?a) (sym-cat ?b)) 0))
CLIPS>
(defrule calcula
=>
(assert (union)
(complement)
(difference)))
CLIPS>
(defrule add-to-union
?union <- (union $?u)
(or (set-1 $? ?v $?)
(set-2 $? ?v $?))
(test (not (member$ ?v ?u)))
=>
(retract ?union)
(assert (union ?u ?v)))
CLIPS>
(defrule add-to-complement
?complement <- (complement $?c)
(universe $?u1 ?v $?u2)
(set-1 $?s)
(test (and (not (member$ ?v ?c))
(not (member$ ?v ?s))))
=>
(retract ?complement)
(assert (complement ?c ?v)))
CLIPS>
(defrule add-to-difference
?difference <- (difference $?d)
(set-1 $? ?v $?)
(set-2 $?set2)
(test (and (not (member$ ?v ?d))
(not (member$ ?v ?set2))))
=>
(retract ?difference)
(assert (difference ?d ?v)))
CLIPS>
(defrule write-union
(declare (salience -10))
(union $?u)
=>
(printout t "The union is " (sort str-sort ?u) crlf))
CLIPS>
(defrule write-complement
(declare (salience -10))
(complement $?c)
=>
(printout t "The complement is " (sort str-sort ?c) crlf))
CLIPS>
(defrule write-difference
(declare (salience -10))
(difference $?d)
=>
(printout t "The difference is " (sort str-sort ?d) crlf))
CLIPS> (reset)
CLIPS> (run)
The union is (A B C D E F)
The complement is (F G H I J K)
The difference is (A C)
CLIPS>
Can someone explain me how can I import the WEKA created rules in CLIPS and evaluate its efficience in TRS and TES data?
The data I use
I have written 7 rules out of 20 from WEKA tree. I include also 3 instances from the glass datasheet
small test code
(deftemplate glass
(slot n(type FLOAT))
(slot m(type FLOAT))
(slot a(type FLOAT))
(slot b(type FLOAT))
(slot r(type FLOAT))
(slot s(type FLOAT))
(slot k(type FLOAT))
(slot c(type FLOAT)))
(deftemplate Type
(slot type))
(deffacts instances1
(glass (n 13.00)
(m 2.28)
(a 1.00)
(b 0.00)))
(deffacts instances2
(glass (n 13.70)
(m 1.80)
(a 1.40)
(b 0.00)))
(deffacts instances3
(glass (n 13.70)
(m 1.90)
(a 1.40)
(b 0.00)))
(defrule R1
(glass (b ?b))
(test (<= ?b 0.27))
(glass (m ?m))
(test (<= ?m 2.41))
(glass (n ?n))
(test (<= ?n 13.78))
(glass (a ?a))
(test (<= ?a 1.38))
=>
(assert (Type (type buildwindnonfloat1)))
(printout t "buildwindnonfloat1 detected" crlf))
(defrule R2
(glass (b ?b))
(test (<= ?b 0.27))
(glass (m ?m))
(test (<= ?m 2.41))
(glass (n ?n))
(test (<= ?n 13.78))
(glass (a ?a))
(test (> ?a 1.38))
(glass (m ?m))
(test (<= ?m 1.88))
=>
(assert (Type (type containers2)))
(printout t "containers2 detected" crlf))
(defrule R3
(glass (b ?b))
(test (<= ?b 0.27))
(glass (m ?m))
(test (<= ?m 2.41))
(glass (n ?n))
(test (<= ?n 13.78))
(glass (a ?a))
(test (> ?a 1.38))
(glass (m ?m))
(test (> ?m 1.88))
=>
(assert (Type (type buildwindnonfloat3)))
(printout t "buildwindnonfloat3 detected" crlf))
(defrule R4
(glass (b ?b))
(test (<= ?b 0.27))
(glass (m ?m))
(test (<= ?m 2.41))
(glass (n ?n))
(test (> ?n 13.78))
=>
(assert (Type (type tableware4)))
(printout t "tableware detected" crlf))
(defrule R5
(glass (b ?b))
(test (<= ?b 0.27))
(glass (m ?m))
(test (> ?m 2.41))
(glass (a ?a))
(test (<= ?a 1.4))
(glass (m ?m))
(test (<= ?m 3.34))
(glass (a ?a))
(test (<= ?a 1.25))
=>
(assert (Type (type buildwindnonfloat5)))
(printout t "buildwindnonfloat5 detected" crlf))
(defrule R6
(glass (b ?b))
(test (<= ?b 0.27))
(glass (m ?m))
(test (> ?m 2.41))
(glass (a ?a))
(test (<= ?a 1.4))
(glass (m ?m))
(test (<= ?m 3.34))
(glass (a ?a))
(test (> ?a 1.25))
=>
(assert (Type (type buildwindfloat6)))
(printout t "buildwindfloat6 detected" crlf))
(defrule R7
(glass (b ?b))
(test (<= ?b 0.27))
(glass (m ?m))
(test (> ?m 2.41))
(glass (a ?a))
(test (<= ?a 1.4))
(glass (m ?m))
(test (> ?m 3.34))
(glass (m ?m))
(test (<= ?m 3.82))
(glass (r ?r))
(test (<= ?r 1.51707))
(glass (r ?r))
(test (<= ?r 51596))
=>
(assert (Type (type buildwindfloat7)))
(printout t "buildwindfloat7 detected" crlf))
To convert your data, it's easiest to read the data from the file when your program is running and directly assert the facts. So if your data looks like the following with each entry on its own line
1.5159,13.24,3.34,1.47,73.1,0.39,8.22,0,0,'build wind non-float'
1.5167,13.24,3.57,1.38,72.7,0.56,8.44,0,0.1,'vehic wind float'
then your can read your data by reading each line as a single string, replacing the commas with spaces, and then splitting the string into multiple values. You can then have a separate rule map the values from your file to the appropriate slots in your deftemplate facts.
Store the expected result with each glass fact and then you can compare that value to the value that your rule is proposing.
CLIPS (6.31 6/12/19)
CLIPS>
(deftemplate glass
(slot n (type FLOAT))
(slot m (type FLOAT))
(slot a (type FLOAT))
(slot b (type FLOAT))
(slot r (type FLOAT))
(slot s (type FLOAT))
(slot k (type FLOAT))
(slot c (type FLOAT))
(slot f (type FLOAT))
(slot type))
CLIPS>
(deftemplate input
(multislot data))
CLIPS>
(deffunction str-rpl (?str ?find ?replace)
(if (eq ?find "")
then
(return ?str))
(bind ?rs "")
(bind ?fl (str-length ?find))
(bind ?i (str-index ?find ?str))
(while (neq ?i FALSE)
(bind ?rs (str-cat ?rs (sub-string 1 (- ?i 1) ?str) ?replace))
(bind ?str (sub-string (+ ?i ?fl) (str-length ?str) ?str))
(bind ?i (str-index ?find ?str)))
(bind ?rs (str-cat ?rs ?str))
?rs)
CLIPS>
(defrule get-data
=>
(printout t "Input File? ")
(bind ?file (readline))
(if (not (open ?file data))
then
(printout t "Unable to open file" crlf)
(return))
(bind ?line (readline data))
(while (neq ?line EOF)
(bind ?line (str-rpl ?line "," " "))
(bind ?line (str-rpl ?line "'" "\""))
(assert (input (data (explode$ ?line))))
(bind ?line (readline data)))
(close data))
CLIPS>
(defrule convert-data
?i <- (input (data ?r ?n ?m ?a ?s ?k ?c ?b ?f ?type))
=>
(retract ?i)
(assert (glass (r ?r) (n ?n) (m ?m) (a ?a) (s ?s) (k ?k) (c ?c) (b ?b) (f ?f) (type ?type))))
CLIPS>
(defrule R1
(glass (b ?b)
(m ?m)
(n ?n)
(a ?a)
(type ?type))
(test (<= ?b 0.27))
(test (<= ?m 2.41))
(test (<= ?n 13.78))
(test (<= ?a 1.38))
=>
(printout t "buildwindnonfloat1 detected type = " ?type crlf))
CLIPS>
(defrule R2
(glass (b ?b)
(m ?m)
(n ?n)
(a ?a)
(type ?type))
(test (<= ?b 0.27))
(test (<= ?m 2.41))
(test (<= ?n 13.78))
(test (> ?a 1.38))
(test (<= ?m 1.88))
=>
(printout t "containers2 detected type = " ?type crlf))
CLIPS>
(defrule R3
(glass (b ?b)
(m ?m)
(n ?n)
(a ?a)
(type ?type))
(test (<= ?b 0.27))
(test (<= ?m 2.41))
(test (<= ?n 13.78))
(test (> ?a 1.38))
(test (> ?m 1.88))
=>
(printout t "buildwindnonfloat3 detected type = " ?type crlf))
CLIPS>
(defrule R4
(glass (b ?b)
(m ?m)
(n ?n)
(type ?type))
(test (<= ?b 0.27))
(test (<= ?m 2.41))
(test (> ?n 13.78))
=>
(printout t "tableware detected type = " ?type crlf))
CLIPS>
(defrule R5
(glass (b ?b)
(m ?m)
(a ?a)
(type ?type))
(test (<= ?b 0.27))
(test (> ?m 2.41))
(test (<= ?a 1.4))
(test (<= ?m 3.34))
(test (<= ?a 1.25))
=>
(printout t "buildwindnonfloat5 detected type = " ?type crlf))
CLIPS>
(defrule R6
(glass (b ?b)
(m ?m)
(a ?a)
(type ?type))
(test (<= ?b 0.27))
(test (> ?m 2.41))
(test (<= ?a 1.4))
(test (<= ?m 3.34))
(test (> ?a 1.25))
=>
(printout t "buildwindfloat6 detected type = " ?type crlf))
CLIPS>
(defrule R7
(glass (b ?b)
(m ?m)
(a ?a)
(r ?r)
(type ?type))
(test (<= ?b 0.27))
(test (> ?m 2.41))
(test (<= ?a 1.4))
(test (> ?m 3.34))
(test (<= ?m 3.82))
(test (<= ?r 1.51707))
(test (<= ?r 51596))
=>
(printout t "buildwindfloat7 detected type = " ?type crlf))
CLIPS> (reset)
CLIPS> (run)
Input File? weka.txt
buildwindfloat7 detected type = vehic wind float
CLIPS>
I'm trying to build a simple expert system for recommending courses and want to implement certainty factor in my program, however I'm stuck looking for a simple integration method.
I've stumbled upon this example but can't seems to figure out how to make it fire.
; Allow facts that are duplicates:
(defrule start
(declare (salience 1000))
(initial-fact)
=>
(set-fact-duplication TRUE))
(defrule combine-certainities-both-positive
?fact1 <- (organism ?attribute ?value ?C1&:(>= ?C1 0))
?fact2 <- (organism ?attribute ?value ?C2&:(>= ?C2 0))
(test (neq ?fact1 ?fact2))
=>
(retract ?fact1 ?fact2)
(bind ?C3 (- (+ ?C1 ?C2) (* ?C1 ?C2)))
(assert (organism ?attribute ?value ?C3)))
(defrule combine-certainities-both-negative
?fact1 <- (organism ?attribute ?value ?C1&:(< ?C1 0))
?fact2 <- (organism ?attribute ?value ?C2&:(< ?C2 0))
(test (neq ?fact1 ?fact2))
=>
(retract ?fact1 ?fact2)
(bind ?C3 (+ (+ ?C1 ?C2) (* ?C1 ?C2)))
(assert (organism ?attribute ?value ?C3)))
(defrule combine-certainities-with-opposite-signs
?fact1 <- (organism ?attribute ?value ?C1)
?fact2 <- (organism ?attribute ?value ?C2)
(test (< (* ?C1 ?C2) 0))
(test (neq ?fact1 ?fact2))
=>
(retract ?fact1 ?fact2)
(bind ?C3 (/ (+ ?C1 ?C2) (- 1 (min (abs ?C1) (abs ?C2)))))
(assert (organism ?attribute ?value ?C3)))
I try to assert two new organism facts to kick start the first rule:
CLIPS> (assert (organism morpholgy1 rod1 0.25)
(organism morpholgy2 rod2 0.25))
==> f-4 (organism morpholgy1 rod1 0.25)
==> f-5 (organism morpholgy2 rod2 0.25)
<Fact-5>
CLIPS> (run)
<== Focus MAIN
0 rules fired Run time is 0.00300693511962891 seconds.
0.0 rules per second.
2 mean number of facts (2 maximum).
0 mean number of instances (0 maximum).
0 mean number of activations (0 maximum).
And use the matches but still don't get how to make it match here..
CLIPS> (matches combine-certainities-both-positive)
Matches for Pattern 1
f-4
f-5
Matches for Pattern 2
f-4
f-5
Partial matches for CEs 1 - 2
None
Activations
None
(4 0 0)
The attribute and the value have to match (morpholgy1 != morpholgy2 and rod1 != rod2). That's why fact duplication has to be enabled to allow multiple copies of the same attribute/value.
CLIPS> (set-fact-duplication TRUE)
FALSE
CLIPS>
(assert (organism morpholgy rod 0.25)
(organism morpholgy rod 0.25))
<Fact-2>
CLIPS> (watch facts)
CLIPS> (run)
<== f-2 (organism morpholgy rod 0.25)
<== f-1 (organism morpholgy rod 0.25)
==> f-3 (organism morpholgy rod 0.4375)
CLIPS>
I am trying to solve a problem, where I have to fill a 5x5 matrix with letters A, B, C, D, and E. Each letter cannot occur more than once in each row and in each column. With some initial letter positions given.
I created every position as separate facts eg. "M 1 1 X".
I am struggling how to loop a defrule in way to assert a fact with correct letter and check the conditions again.
(defrule solveA5
?a <-(M 5 ?c X)
(not (M ?x ?c A))
=>
(retract ?a)
(assert (M 5 ?c A))
)
Code above for example is only to check presence of A in every position of 5th row, but the problem is that conditions are checked at the beginning only and instead of asserting correct fact and checking again it asserts A in every position.
I've tried using deffunction to loop defrule.
(deffunction solve (?letter)
(loop-for-count (?x 1 5) do
(loop-for-count (?y 1 5) do
(build (str-cat"defrule costam
?a <-(M ?x ?y X)
(not (and(M ?x ?a ?letter) (M ?b ?y ?letter))
=>
(retract ?a)
(assert (M ?x ?y ?letter))")
)
)
)
)
Unfortunately running
(solve A)
returns "FALSE" and doesn't modify any facts.
To handle iteration within rules, you must assert the iteration information as facts to allow the rules to match and modify this information. In the placement, it's not essential to do this in any particular order, so you can just assert information containing the rows, columns, and letters to place and allow the rules fire arbitrarily:
CLIPS>
(deftemplate element
(slot row)
(slot column)
(slot value))
CLIPS>
(deftemplate print
(slot row)
(slot column)
(slot end-of-row))
CLIPS>
(deffacts initial
(rows 1 2 3 4 5)
(columns 1 2 3 4 5)
(letters A B C D E))
CLIPS>
(defrule place
(rows $? ?r1 $?)
(columns $? ?c1 $?)
(letters $? ?l $?)
(not (element (row ?r1) (column ?c1)))
(not (and (element (row ?r2)
(column ?c2)
(value ?l))
(test (or (= ?r1 ?r2) (= ?c1 ?c2)))))
=>
(assert (element (row ?r1) (column ?c1) (value ?l))))
CLIPS>
(defrule print-start
(declare (salience -10))
(rows ?r $?)
(columns ?c $?rest)
=>
(assert (print (row ?r)
(column ?c)
(end-of-row (= (length$ ?rest) 0)))))
CLIPS>
(defrule print-next-column
(declare (salience -10))
?f <- (print (column ?c))
(columns $? ?c ?nc $?rest)
=>
(modify ?f (column ?nc)
(end-of-row (= (length$ ?rest) 0))))
CLIPS>
(defrule print-next-row
(declare (salience -10))
?f <- (print (column ?c) (row ?r))
(columns $?first ?c)
(rows $? ?r ?nr $?)
=>
(if (= (length$ ?first) 0)
then
(bind ?eor TRUE)
(bind ?nc ?c)
else
(bind ?eor FALSE)
(bind ?nc (nth$ 1 ?first)))
(modify ?f (row ?nr)
(column ?nc)
(end-of-row ?eor)))
CLIPS>
(defrule print-placed
(print (row ?r) (column ?c) (end-of-row ?eor))
(element (row ?r) (column ?c) (value ?l))
=>
(if ?eor
then
(printout t ?l crlf)
else
(printout t ?l " ")))
CLIPS>
(defrule print-unplaced
(print (row ?r) (column ?c) (end-of-row ?eor))
(not (element (row ?r) (column ?c)))
=>
(if ?eor
then
(printout t "?" crlf)
else
(printout t "? ")))
CLIPS> (reset)
CLIPS> (run)
E D C B A
? C D A B
? B A D C
? A B C D
A ? ? ? E
CLIPS>
In this example, the print rules iterate over the rows and columns by storing the iteration information in facts. You can see how much more complicated this is than the place rule which assigns the elements in an arbitrary manner.
Whether you assign the values arbitrarily or in a specific order, it's possible to assign values that prevent a solution, so you must implement backtracking in order to guarantee finding the solution if one exists. In this example, the facts store information about the order of the value placements and the values that have been tried:
CLIPS> (clear)
CLIPS>
(deftemplate element
(slot row)
(slot column)
(slot value (default unset))
(multislot values)
(slot placement))
CLIPS>
(deffacts initial
(placement 0)
(rows 1 2 3 4 5)
(columns 1 2 3 4 5)
(letters A B C D E))
CLIPS>
(defrule prime
(placement ?p)
(rows $? ?r $?)
(columns $? ?c $?)
(letters $?l)
(not (element (placement ?p)))
(not (element (row ?r) (column ?c)))
=>
(assert (element (placement ?p) (values ?l) (row ?r) (column ?c))))
CLIPS>
(defrule place-good
?f1 <- (placement ?p)
?f2 <- (element (placement ?p)
(value unset)
(row ?r1)
(column ?c1)
(values ?v $?rest))
(not (and (element (row ?r2)
(column ?c2)
(value ?v))
(test (or (= ?r1 ?r2) (= ?c1 ?c2)))))
=>
(retract ?f1)
(assert (placement (+ ?p 1)))
(modify ?f2 (value ?v) (values ?rest)))
CLIPS>
(defrule place-bad
(placement ?p)
?f2 <- (element (placement ?p)
(value unset)
(row ?r1)
(column ?c1)
(values ?v $?rest))
(element (row ?r2)
(column ?c2)
(value ?v))
(test (or (= ?r1 ?r2) (= ?c1 ?c2)))
=>
(modify ?f2 (values ?rest)))
CLIPS>
(defrule backtrack
?f1 <- (placement ?p)
?f2 <- (element (placement ?p)
(value unset)
(values))
?f3 <- (element (placement =(- ?p 1))
(value ~unset))
=>
(retract ?f1)
(assert (placement (- ?p 1)))
(retract ?f2)
(modify ?f3 (value unset)))
CLIPS>
(defrule print
(declare (salience -10))
(rows $?rows)
(columns $?columns)
=>
(progn$ (?r ?rows)
(progn$ (?c ?columns)
(if (not (do-for-fact ((?f element))
(and (= ?r ?f:row) (= ?c ?f:column))
(printout t ?f:value " ")))
then
(printout t "? ")))
(printout t crlf)))
CLIPS> (reset)
CLIPS> (run)
B C D E A
A B C D E
C A E B D
D E A C B
E D B A C
CLIPS>
The print rules have been simplified into a single rule that iterates over the row and columns in the actions of the rule and uses the fact query functions to retrieve values that have been assigned.
The program also works if you preassign some of the values:
CLIPS> (reset)
CLIPS> (assert (element (row 1) (column 1) (value A)))
<Fact-5>
CLIPS> (assert (element (row 3) (column 3) (value C)))
<Fact-6>
CLIPS> (assert (element (row 5) (column 4) (value E)))
<Fact-7>
CLIPS> (run)
A C E D B
B A D C E
D E C B A
E D B A C
C B A E D
CLIPS>