1. The CLIPS Expert System Shell Dr Nicholas Gibbins nmg@ecs.soton.ac.uk

2. The CLIPS Expert System Shell A framework for building expert systems –Don’t need to build basic infrastructure every time you build an expert system Commonly used and well-understood –First developed at NASA JSC in mid-1980s Provides the following facilities: –Representation of facts and rules –Search mechanism for identifying and firing matching rules

3. Facts in CLIPS Represented in CLIPS by s-expressions: –(likes fred sally) –likes, fred and sally are symbols Two types of CLIPS fact –Ordered (as above) –Unordered, specified using deftemplate (we’ll come to this later)

4. Starting and stopping CLIPS Run CLIPS from the UNIX command line: CLIPS> To quit CLIPS, type (exit)

5. Asserting facts Add a new fact to the knowledge base with assert : CLIPS> (assert (likes fred mary)) CLIPS> Check for asserted facts with facts : CLIPS> (facts) f-0 (likes fred mary) For a total of 1 fact. CLIPS> The 0 in f-0 and Fact-0 is the fact-index of the new fact

6. Retracting facts Remove a fact from the knowledge base with retract: CLIPS> (assert (likes fred mary)) CLIPS> (facts) f-0 (likes fred mary) For a total of 1 fact. CLIPS> (retract 0) CLIPS> (facts) CLIPS> Note that you need to know the fact-index to retract a fact Remove all facts from the knowledge base using (clear)

7. Watching the knowledge base Observe the activity in the knowledge base using watch : CLIPS> (watch facts) CLIPS> (assert (likes fred mary)) ==> f-0 (likes fred mary) CLIPS> (retract 0) ==> indicates that a fact is being added <== indicates that a fact is being removed Turn off watch using (unwatch facts)

8. Creating rules A simple rule: (defrule mary-loved (likes fred mary) => (assert (loved mary))) The name of the rule The rule condition The rule action

9. Rules in general (defrule “optional comment”...) Everything before => is known as the left-hand side (LHS), everything after the right-hand side (RHS) The rule is fired if all of the conditions in the LHS match When the rule fires, all of the actions are carried out

10. The Agenda The list of rules which are waiting to be fired (the conflict set) is known as the agenda This can be examined using (agenda) : CLIPS> (agenda) 0 mary-loved: f-0 For a total of 1 activation. CLIPS>

11. Running your rules Start executing your rules with run: CLIPS> (run) ==> f-1 (loved mary) CLIPS> Execution continues until there are no rules left to be fired (the agenda is empty) Can control number of rule firings with (run )

12. Variables We can have variables in the conditions of our rules: (defrule loved (likes ?sub ?obj) => (assert (loved ?obj)))

13. Variables CLIPS> (assert (likes fred mary)) ==> f-0 (likes fred mary) CLIPS> (assert (likes fred sue)) ==> f-1 (likes fred sue) CLIPS> (agenda) 0 loved: f-1 0 mary-loved: f-0 0 loved: f-0 For a total of 3 activations. CLIPS> (run) ==> f-2 (loved sue) ==> f-3 (loved mary) CLIPS>

14. Loading from files If your rules are defined in a file called myrules.clp At the shell command line: $ clips -f myrules.clp At the CLIPS command line: CLIPS> (load “myrules.clp”)

15. Retracting facts (defrule remove-liked ?fact (retract ?fact)) <- binds the address of a matching fact to a variable

16. Anonymous variables If we’re not interested in the value of a variable, we can match using a single-field wildcard variable: ? (defrule remove-liked ?fact (retract ?fact))

17. Multifield variables Consider a knowledge base with variable-length facts of the form (route town-1 town-2 town-3 … town-n) If we wish to match all routes that start at A and end at B, we can match all the intermediate towns using a multifield wildcard: $? Multifield variables match a list of objects (…) (defrule find-route (route town-a $?towns town-b) => (printout t “Route from A to B via “ ?towns crlf))

18. Multifield variables We have a fact: (route a b c d e f g h) In how many ways can this be matched by this pattern? (route $?start ?item $?end)

19. Multifield variables $?start = (), ?item = a, $?end = (b c d e f g h) $?start = (a), ?item = b, $?end = (c d e f g h) $?start = (a b), ?item = c, $?end = (d e f g h) $?start = (a b c), ?item = d, $?end = (e f g h) $?start = (a b c d), ?item = e, $?end = (f g h) $?start = (a b c d e), ?item = f, $?end = (g h) $?start = (a b c d e f), ?item = g, $?end = (h) $?start = (a b c d e f g), ?item = h, $?end = ()

20. Field Constraints We can further constraint the values taken by variables ~symbol matches anything but symbol symbol1|symbol2 matches either symbol1 or symbol2 symbol1&symbol2 matches both symbols ?var&constraint matches anything that satisfies the constraint, and assigns it to ?var

21. Test Conditions LHS of rule can contain other sorts of constraints on variable values (see BPG p. 47) For example: (defrule my-rule (age fred ?var1) (age sally ?var2) (test (> ?var1 ?var2)) => … )

22. Functions CLIPS has a number of built-in functions which can be invoked like their Scheme look-a-likes: (+ 1 2) Complete list of built-in functions starting at p.149 of the CLIPS reference manual (Basic Programming Guide)

23. Defining functions We can define a custom function using deffunction: (deffunction square (?x) (* ?x ?x))

24. Defining functions (deffunction function-name (?arg1 ?arg2... $?args) (action1 action2... actionn)) Value returned by function is that of last action Final argument may be a multifield variable

25. Binding variables We can bind the result of a function to a variable: (bind ?sum (+ ?x ?y))

26. Example: Route Finding E E C C B B D D A A 5 7 4 2 8 3

27. Represent routes as facts of the form: (route start … end distance) For example: (route town-a town-b town-d town-e 16) How can we generate all of the shortest routes from every town to every other town?

28. Writing output We can write to stdout using printout: (defrule print-likes (likes ?x ?y) => (printout t ?x “ likes “ ?y crlf)) prints: “john likes sally”

29. Reading input (defrule read-colour (initial-fact) => (printout t “Name a colour” crlf) (assert (colour (read)))) (read) reads a single symbol, integer or string (readline) reads until the next carriage return

30. Templated facts The ordered lists of terms we’ve seen so far are only one kind of fact We can also create facts containing unordered (but named) terms: (car (colour red) (doors 4) (maker vw)) is the same as: (car (doors 4) (maker vw) (colour red))

31. Templated facts We introduce a new type of templated fact as follows: (deftemplate car (slot registration (type SYMBOL)) (slot color (type SYMBOL) (allowed-symbols white red green blue)) (slot doors (type INTEGER) (default 4)) (slot maker (type SYMBOL)))

32. Templated facts Templated facts are asserted like any other fact: (assert (car (registration g885tnj) (doors 4) (maker vw) (colour green)))

33. Example: Classification We wish to build an expert system to classify the following farmyard animals: –Dog –Cows –Chickens –Ducks –Pigs

34. Example: Classification Possible means for classification: –Fur/feathers/skin –Number of legs –Swims/doesn’t swim –Eats meat/grass/grain

35. Example: Classification (deftemplate observation (slot name (type SYMBOL)) (slot value)) (deftemplate classification (slot name (type SYMBOL)))