A Clojure implementation of a simple forward chaining rule engine. An engine is created by defining rules in one or more modules and invoking engine.core/engine on keywords defining the modules. Each working memory element in the engine is a Clojure map. The name "Arete" is a pun on the greek word and "a-RETE" (i.e. not RETE), since the engine is based more on the TREAT algorithm than RETE.
Example:
In module foo.bar:
(ns foo.bar
(:require [engine.core :refer :all]))
(defrule rule1
[?service :service]
=>
(println (:name ?service)))
In another module:
(ns another
(:require [engine.core :refer :all]))
(def eng (engine :foo.bar)) ; loads rules from the foo.bar module
(eng :run [{:type :service :name "s1"}])
s1
{:service [{:type :service :name "s1"}]}
The result of invoking :run [<wme>...] on an engine is to insert the
specified working memory elements (wmes), run rules until no more will
fire and then return a map of wme types to sequences of wmes.
This engine was originally created to help with translating between different container orchestration formats (Kubernetes and Docker Compose). Both formats were changing rapidly and they don't have particularly similar structures. Rules made it easy to express global constraints and to avoid a lot of complicated and fragile navigation code. Though there are multiple Java-based rule engines, they are quite heavyweight, and are mostly oriented more toward expressing business rules than just providing an additional programming paradigm. The only Clojure engine we found (Clara Rules) is a great tool but is aimed at different use cases. It shares with this engine, however, the advantage that rules can be expressed directly in a Clojure program without any new, separate language that needs to be parsed.
Since this engine was used for translation (and not, say, cluster management) there is not much support built in for having an engine instance run forever while taking new inputs and processing them. It would actually be relatively easy to do and will probably happen at some point if there's interest.
The Arete engine is available from clojars as [arete "0.6.1"] or simply
download the repo, install leiningen if necessary, and run lein uberjar. The main class in the uberjar is the rule viewer for
debugging. The engine itself has no command line.
Currently the commands supported by an engine are:
-
(
<engine>:run[-map] [<wme>...]) - Run to completion after inserting wmes. After running, clear the engine state so that a subsequent call will encounter a fresh engine. Returns a map of wme types to collections of wme instances. -
(
<engine>:run-list [<wme>...]) - Run to completion after inserting wmes. After running, clear the engine state so that a subsequent call will encounter a fresh engine. Returns a list of wmes. -
(
<engine>:cycle [<wme>...]) - Run to completion after inserting wmes. After running, leave the engine alone so subsequent calls add to the state rather than starting fresh. -
(
<engine>:configure {<setting><value>, ...}) - Turn on/off various settings for the engine:- :log-rule-firings true/false - Whether or not to print the names of rules as they fire
- :max-repeated-firings
<n>- How many times a single rule may fire consecutively before it's considered stuck. (Defaults to 300). - :trace-set #{
<rule name>, ...} - Set of rules whose execution should be traced - :stop-before #{
<rule name>, ...} - Set of rules for which the engine should stop executing when reached (for testing) - :stop-after #{
<rule name>, ...} - Set of rules for which the engine should stop executing after firing (for testing) - :enable-perf-mon true/false - Whether or not performance statistics should be gathered during the run. Requires that the code was compiled with the NO_PERF_COMPILE environment variable unset.
- :record
<file name>- Record rule firings into a file for debugging.
-
(
<engine>:timing) - Display timing gathered by :enable-perf-mon. -
(
<engine>:wmes) - Return a map of the wmes in the engine as returned by run and run-map. -
(
<engine>:wme-list) - Return a list of the wmes in the engine as returned by run-list.
There is also a separate "viewer" that can be used to step through a recorded rule session for debugging.
Rules are very simple. Here is the complete syntax:
RULE ::= '(' 'defrule' <RULE_NAME> <CONFIG_MAP>? <LHS>? '=>' <RHS> ')'
RULE_NAME ::= *string*
CONFIG_MAP ::= '{' ':priority' <PRIORITY_VALUE> '}'
PRIORITY_VALUE ::= *integer* (can be any expression returning an
integer)
LHS ::= <CONDITION>+
CONDITION ::= <MATCH> | <NAND>
MATCH ::= '[' <OBJ_VAR> <TYPE> <TEST_EXP>* ']'
NAND ::= '[' (':not' | ':nand') <CONDITION>+ ']'
OBJ_VAR ::= '?' *string*
TYPE ::= *keyword*
TEST_EXP ::= *clojure expression referencing OBJ_VAR*
RHS ::= *clojure code*
Here is a rule showing the possible syntax:
(defrule testrule
{:priority 28}
[?f :foo (= (:val ?f) 6)]
[:not [?b :bar]]
[:nand
[?baz :baz (> (:val ?baz) (:val ?f)) (not= (rem (:val ?baz) 2) 0)]
[?quux :quux]]
=>
(remove! ?f)
(insert! {:type :result :objs (collect! :baz #(= (:val %) 100))}))There are three engine operations available for use within rule right hand sides:
- (insert!
<wme>) - add a wme to the engine - (remove!
<wme>) - remove a wme from the engine - (collect!
<fun>) | (collect!<wme type><fun>) - Collect all instances for which<fun>returns true, limited to a particular wme type if the second form is used.
It's sometimes tempting to try to use "collect!" in a rule LHS. DON'T!!! It won't work.
The basic conflict resolution strategy of the engine is simple priority. However, there is a declarative means of specifying preferences. A rule module can contain a "deforder" expression specifying how conflicts should be resolved:
(deforder (:with :x) (:without :y) :oldest)The expression above says that any instantiation containing a wme of type :x should be preferred over one that does not contain an ":x" and if neither one contains an ":x", pick the one without a ":y" over one that does contain it. Finally, if all (or no) instantiations contain an ":x" and all (or no) instantiations contain a ":y", pick the instantiation that was created first. The set of currently available checks is:
- :with
<wme type>- Prefer instantiations containing wme of type - :without
<wme type>- Prefer instantiations not containing wme of type<wme type> - :newest - Prefer the most recently created instantiation
- :oldest - Prefer the least recently created instantiation
- :from-module
<module>- Prefer an instantiation from a rule in<module>over any from other modules
Sometimes it's useful to write rules that operate on abstract categories of wmes that are otherwise of different types. Maybe you want to write a rule that deals with all "shapes" instead of specific "circles", "squares", etc. This is supported in the engine by the use of "defancestor" expressions. The following:
(defancestor [:deployment :daemonset :statefulset :cronjob] :controller)says that any rule that matches a ":controller" should also match a ":deployment", ":daemonset", ":statefulset", or ":cronjob". The ancestor relationship is transitive so any ancestor of ":controller" would also be an ancestor of its descendents.
The rule viewer allows post-mortem debugging via a recorded session. If you configure the engine to record:
(eng :configure {:record "/tmp/out"})you can run the viewer against the file after the fact. Here is a simple set of rules implementing "factorial" that we can use to demonstrate:
(ns engine.factorial
(:require [engine.core :refer :all]))
(defrule fact-base
[?arg :factarg (<= (:value ?arg) 0)]
=>
(remove! ?arg)
(insert! {:type :factor :value 1}))
(defrule fact
[?arg :factarg (> (:value ?arg) 0)]
=>
(remove! ?arg)
(insert! (update ?arg :value dec))
(insert! {:type :factor :value (:value ?arg)}))
(defrule combine
[?factor1 :factor]
[?factor2 :factor (not= ?factor1 ?factor2)]
=>
(remove! ?factor1)
(remove! ?factor2)
(insert! {:type :factor :value (* (:value ?factor1) (:value ?factor2))}))
(defrule result
[?factor :factor]
[:not [?factor2 :factor (not= ?factor ?factor2)]]
[:not [? :factarg]]
=>
(remove! ?factor)
(insert! {:type :fact-result :value (:value ?factor)}))We'll run them by hand in the repl:
lein repl
engine.viewer=> (require '[engine.core :as e])
nil
engine.viewer=> (require '[engine.factorial])
Compiling engine.factorial/fact-base
Compiling engine.factorial/fact
Compiling engine.factorial/combine
Compiling engine.factorial/result
nil
engine.viewer=> (def eng (e/engine :engine.factorial))
#'engine.viewer/eng
engine.viewer=> (eng :configure {:record "/tmp/out"})
#object[engine.core$engine$fn__1979 0x3400db7b "engine.core$engine$fn__1979@3400db7b"]
engine.viewer=> (eng :run [{:type :factarg :value 6}])
{:fact-result [{:type :fact-result, :value 720}]}
^D
Now let's run the viewer:
java -jar target/arete-0.6.1-standalone.jar /tmp/out
Instantiations:
:engine.factorial/fact (3*)
Wmes:
:_start (1*)
:factarg (2*)
(0)==>
This shows us at step 0 with one rule instantiation and two wmes (the _start wme used to trigger rules without left hand sides and our factorial argument). The "*" after each number indicates that the wme or instantiation was newly created. Let's type in the number of the argument to get a better look:
(0)==> 2
WME - (2):factarg
value: 6
(0)==>
Not too much to see here since it's a very simple wme. A '?' will tell us all our options:
(0)==> ?
Usage:
'<':
go to beginning
'>':
go to end
'?':
display this help
'.':
exit the viewer
'<cr>':
if at top level, move forward one firing; otherwise return to top level
'<number>[,<number>]*':
display insts or wmes with <number>s as ids
'ar':
display all rule firings for the run
'b':
back up one firing
'e <exp>':
evaluate expression referencing an individual wme as: :<id> and all wmes
as :0
'g <step id>':
go to step number: <step id>
'h':
display command history
'pi <str>':
display partial rule instantiations for rules with name containing <str>
'r':
display rule firings leading to this point
'ref <wme id>':
display any wmes that reference the specified wme via a UUID link
'rs <str>':
display rule with name containing <str>
'sc <wme id>':
find the firing that created the specified wme
'sd <wme id>':
find the firing that deleted the specified wme
'ss <str>':
find the next firing containing a wme whose string representation includes
<str>
'st <type fragment>':
find the next firing containing a wme whose type name includes the <type
fragment>
'sr <rule fragment>':
find the next firing for a rule whose name includes the <rule fragment>
'si <type fragment>':
find the next firing whose instantiation references a wme with type
containing the <type fragment>
'save <filename>':
save the history to a file (when running as ":record true")
'w':
display all wmes for current firing
'w <type fragment>':
display all wmes for current firing with types containing <type fragment>
'ws <str>':
display all wmes for current firing whose string representations contain
<str>
(0)==>
Let's see all the rules that fired:
(0)==> ar
0) :engine.factorial/fact
1) :engine.factorial/fact
2) :engine.factorial/combine
3) :engine.factorial/fact
4) :engine.factorial/combine
5) :engine.factorial/fact
6) :engine.factorial/combine
7) :engine.factorial/fact
8) :engine.factorial/combine
9) :engine.factorial/fact
10) :engine.factorial/combine
11) :engine.factorial/fact-base
12) :engine.factorial/combine
13) :engine.factorial/result
(0)==>
Now let's go to the end:
(0)==> >
Last Rule: :engine.factorial/result
Wmes:
:_start (1)
:fact-result (66*)
(14)==>
Notice that once you're past the initial step, the previously fired rule is also displayed. The rest of the viewer functionality will be clear with a bit of experimentation.
Timing of individual rules and more global properties of the engine can be obtained by setting the environment variable "DEBUG_COMPILE" to true and configuring the :enable-perf-mon flag to true. This will cause performance stats to be collected which can be viewed by invoking :timing on the engine after a run. With no argument or an argument of false, the timing data will get cleared after being requested. An argument of true will leave the data intact so it can be combined with data from other runs. The result of calling :timing is a CSV file written to stdout that can be imported into a spreadsheet. Something like:
finish-beta,29133401,100001,278837,209,291.33109668903313
create-instantiation,234105613,100002,127878,1705,2341.0093098138036
remove-pos-instantiation,280358199,100002,2561909,2142,2803.5259194816103
increment,434698310,200002,600855,297,2173.469815301847
rules,1018306095,400006,3263097,688,2545.727051594226
increment-body,216165513,100000,1865289,1609,2161.65513
body,250362454,100001,1866451,1882,2503.5995040049597
finish-body,53312,1,53312,53312,53312.0
remove-instantiation-inst-remove,60288971,100002,50316,443,602.8776524469511
alpha,179129354,400006,122936,323,447.81666774998376
instantiation-ordering,41489399,200004,42648,127,207.44284614307713
finish,391241373,200004,3196323,300,1956.167741645167
remove-instantiation-wme-loop,76970146,100002,2554643,522,769.6860662786744
beta,63759857,100001,2040271,470,637.5921940780593
The columns are:
Name, Total Time, Number of Invocations, Max Time, Min Time, Mean Time
Currently, the engine internal data is mixed in with the data for user rules. At some point, the perf interface will be made more user-friendly; however it is already quite useful in its current state.
The performance monitoring has very little overhead, particularly if the engine does not have it enabled and DEBUG_COMPILE was not set. However, if you want the absolute maximum speed for a deployed system, set the environment variable NO_PERF_COMPILE to true before building your application.
As in any non-toy rule engine, equal joins between LHS objects are turned into hash lookups. This means that the execution of a rule like:
(defrule join-example
[?obj1 :some-type]
[?obj2 :some-other-type
(= (:some-field ?obj1) (:some-other-field ?obj2))]
=>
(println "Found match:" ?obj1 "and" ?obj2))
doesn't need to compare every instance of :some-type with every
instance of :some-other-type. Instead, the wmes for
:some-other-type are stored in a hash map keyed by the value of
:some-other-field. When it's time to process an instance of
:some-type, it is directly combined with instances of
:some-other-type that result from a hash lookup of its :some-field
value. In general, this is always worth doing as it completely avoids
the normal cross product comparisons.
The arete engine also implements a less common optimization that is not always useful, but can be extremely so. Consider the following rule:
(defrule foo
[?ball1 :ball
(= (:pattern ?ball1) :stripe)]
[?ball2 :ball
(= (:pattern ?ball2) :solid)
(= (:color ?ball2) (:color ?ball1))
(> (:value ?ball2) (:value ?ball1))]
[?gurk :gurk (= (:value ?gurk) (:value ?ball2))]
=>
(insert! {:type :triple :ball1 (:value ?ball1) :ball2 (:value ?ball2)
:gurk (:value ?gurk)}))
and the test:
(deftest big-cross
(testing "inequality performance"
(let [data (atom [])
eng (engine :engine.big-cross-test)]
(loop [i 0 j -9998]
(when (< i 10000)
(swap! data conj {:type :ball :pattern :stripe :color :red
:value i})
(swap! data conj {:type :ball :pattern :solid :color :red
:value j})
(recur (inc i) (inc j))))
(loop [g 0]
(when (< g 5)
(swap! data conj {:type :gurk :value g})
(recur (inc g))))
(time (eng :run-list @data)))))
This inserts 9999 striped balls, 9999 solid balls, and 5 gurks. The values for striped and solid balls only overlap in one entry (0).
When run on my laptop, this test takes nearly two minutes to run because each insertion of a gurk forces a full cross product evaluation of the two ball matches. This is something of a worst case scenario for arete since it does not save intermediate join results between executions (A RETE-based engine would have a similar problem if a new wme was added in a match higher in the LHS and would do much more work if "balls" rather than "gurks" were being added and removed). If we make one minor change, however:
(defrule foo
[?ball1 :ball
(= (:pattern ?ball1) :stripe)]
[?ball2 :ball
(= (:pattern ?ball2) :solid)
(= (:color ?ball2) (:color ?ball1))
(>> (:value ?ball2) (:value ?ball1))] ;; <- replace '>' with '>>'
[?gurk :gurk (= (:value ?gurk) (:value ?ball2))]
=>
(insert! {:type :triple :ball1 (:value ?ball1) :ball2 (:value ?ball2)
:gurk (:value ?gurk)}))
it runs in less than 500 milliseconds. The optimization applied here
inserts entries being joined via an inequality into Java TreeMaps so
that the engine can iterate over the headMap of the map containing
entries that match the inequality. The process has a fair bit of
overhead, so it isn't automatically applied. To trigger the
optimization, replace '>' with '>>', '<' with '<<', '>=' with '>>='
and '<=' with '<<='. To see the worst case for the optimization,
consider:
(defrule finish
{:priority 10}
[?limit :limit]
[?counter :counter (>= ^long (:value ?counter) ^long (:value ?limit))]
=>
(remove! ?limit)
(remove! ?counter)
(insert! {:type :result :value (:value ?counter)}))
(defrule increment
[?counter :counter]
=>
(remove! ?counter)
(insert! (update ?counter :value inc)))
and:
(deftest performance-test
(testing "raw simmple rule performance"
(let [eng (engine :engine.speed-test)
_ (eng :configure {:max-repeated-firings 200000})
result (time (eng :run [{:type :limit :value 100000}
{:type :counter :value 0}]))]
(is (= (:value (first (:result result))) 100000)))))
(BTW, note the use of :max-repeated-firings.)
When run with '>=', this takes ~1.3 seconds. With '>>=', however, it takes: ~2.3 seconds. So, there's a tradeoff. If you're operating on large numbers of objects and you see slow behavior around an inequality, try replacing the operator with its TreeMap equivalent and you may see a significant improvement.
The engine is loosely based on the TREAT algorithm, though the handling of negation is different (probably worse...) However, it does correctly handle negated conjunctions. For rules without negation, the processing is quite efficient with very little allocation (only the instantiations and maps to hold wmes). Negation requires maintaining a much more elaborate tracking structure. Like TREAT, no intermediate state is saved for beta tests (other than hashes of values so that we can avoid cross-product performance).
No attempt is made at making this a purely functional implementation; Java maps and other data structures are used throughout for maximum efficiency. If immutable sessions or truth maintenance are important for your application, check out "Clara Rules" instead.
To explain how the engine works, we'll go through a briew overview and then build up from the simplest case. Most forward chaining rule engines use some variation of the RETE algorithm. The RETE algorithm was originally developed based on the insight that the firing of a forward chaining rule typically leaves most of the working data unchanged. This suggests that it's worthwhile to precompute matches and hold on to them between firings since most of the work will not need to be redone. The RETE algorithm takes this perspective to the limit by precomputing and caching everything it can including the results of comparisons between fields of distinct objects (i.e. joins). As it turns out, though, there is a significant amount of bookkeeping overhead associated with maintaining precomputed joins. TREAT (and this engine) discard join results and recompute them as necessary. A RETE engine feeds working memory elements into the top of a discrimination network and "rule instantiations" come out the bottom. TREAT places working memory elements into maps and then works from the bottom up to find matches. Compiling the rule:
(defrule two-objects
[?ball :ball (> (:radius ?ball) 10)]
[?cube :cube (= (:side ?cube) (:radius ?ball))]
=>
(println "Found match: " ?ball " and " ?cube))will produce one map to hold entries for each object match:
{1 {:type :ball :__id 1 :radius 11}
2 {:type :ball :__id 2 :radius 12}
3 {:type :ball :__id 3 :radius 28}}
{4 {:type :cube :__id 4 :side 11}
5 {:type :cube :__id 5 :side 12}
6 {:type :cube :__id 6 :side 28}}and two sets of functions, one for managing the alpha maps (adding and removing wmes) and one to manage rule matching and instantiation. The functions that manage the maps invoke the root function from the second set for each associated rule.
;; Managing maps
(defn alpha-ball-1 [wme-var]
(when <alpha-tests-succeed>
(when (empty? <ball-map>)
(swap! empty-count dec))
(put <ball-map> (:__id wme-var) wme-var)
(reset! <cur-ball-map> {(:__id wme-var) wme-var})
(main-fun)
(reset! <cur-ball-map> <ball-map>)))
(defn alpha-cube-1 [wme-var]
(let [hash-val (<hash-fun> wme-var)]
(when (empty? <cube-map>)
(swap! empty-count dec))
(let [existing (or (.get <cube-map> hash-val)
(let [m (make-map)]
(.put <cube-map> hash-val m)
m))]
(.put ^HashMap existing (:__id ^Wme wme-var) wme-var))
(reset! <cur-cube-map> {(:__id wme-var) wme-var})
(main-fun)
(reset! <cur-cube-map> <cube-map>)))
;; Matching and instantiation
(defn op-1 [hash-val result-fun]
(doseq [cube (vals (get @?cube-cur-2 hash-val))]
(result-fun cube)))
(defn upstream-2 [result-fun]
(doseq [ball (vals @?ball-cur-1)]
(result-fun ball)))
(defn upstream-1 [result-fun]
(upstream-2 (fn [?ball]
(op-1 (:radius ball)
(fn [?cube] (result-fun ?ball ?cube))))))
(defn main-fun []
(when (= empty-count-1 0)
(upstream-1 (fn [?ball ?cube]
(create-instantiation ...)))))The map functions are pseudocode because they're constructed as closures programmatically and many of the variables they reference are defined in the functions that create them. The two map functions look different because the cube function uses an extra level of hashing to allow efficient comparison of ball radii with cube sides.
When a new working memory element is added (e.g. a ball), each alpha (map) function associated with the wme type is called and, if it results in adding a new element, the current map for the type is replaced with a map containing only the new element. Then, the main function of the rule is called and it performs joint matches which will only include the one new wme for the type (since all other cross matches will have already been done when the other values were added). Afterward, the map is set back to the original map plus the new element.
Negated object matches are far more complicated and will be discussed in detail below.
Here is a very simple rule module:
(ns engine.examples
(:require [engine.core :refer :all]))
(defrule note-red-ball
[?ball :ball (= (:color ?ball) :red)]
=>
(println "Found red ball: " ?ball))If we set the environment variable: "SHOW_RULES" and compile the module we can see what the actual code for the rule looks like:
(clojure.core/fn
[]
(clojure.core/let
[note-red-ball-2953
(clojure.core/atom nil)
empty-count-2954
(clojure.core/atom 1)
?ball-2955
(engine.runtime/make-map)
?ball-cur-2956
(clojure.core/atom ?ball-2955)
op-2959
(clojure.core/fn
op-2959
[beta__2253__auto__]
(clojure.core/doseq
[cur__2254__auto__ (clojure.core/vals @?ball-cur-2956)]
(beta__2253__auto__ cur__2254__auto__)))
upstream-2960
op-2959
note-red-ball-alpha-ball-2957
(engine.runtime/alpha-fun
(clojure.core/fn
[?ball]
(clojure.core/and (= (:color ?ball) :red)))
?ball-2955
?ball-cur-2956
empty-count-2954
note-red-ball-2953)
note-red-ball-alpha-rem-ball-2958
(engine.runtime/alpha-rem-fun ?ball-2955 empty-count-2954)]
(.add
engine.runtime/*empty-counts*
[empty-count-2954 @empty-count-2954])
(clojure.core/reset!
note-red-ball-2953
(clojure.core/fn
[]
(clojure.core/when
(clojure.core/= @empty-count-2954 0)
(upstream-2960
(clojure.core/fn
body-2962
[?ball]
(engine.runtime/create-instantiation
:engine.examples
:engine.examples/note-red-ball
0
'note-red-ball-2953
[?ball]
'[]
(clojure.core/fn
[]
(println "Found red ball: " ?ball))))))))
{:ball
[[note-red-ball-alpha-ball-2957
note-red-ball-alpha-rem-ball-2958
?ball-2955]]}))If you look closely, you should be able to see the functions that are analogous to the example ones show above.
Completely general rule conditions require the ability to express arbitrary boolean logic. This engine provides that support in the form of a "negated conjunction" or "nand" match. It's well known that either "nand" or "nor" by itself is sufficient to express any boolean logic, so we add the ability to express nested nands on left hand sides. Consider a loan application rule preventing too many lines of credit from being allowed against a given mortgage. Along with other rules preventing too much leverage, we have a rule that says no more than two lines of credit per mortage:
(defrule max-lines-of-credit
[?mortgage :mortgage]
[?loc-request :loc-request (= (:mortgage ?loc-request) (:id ?mortgage))]
[:nand
[?loc1 :loc (= (:mortgage ?loc1) (:id ?mortgage))]
[?loc2 :loc
(= (:mortgage ?loc2) (:id ?mortgage))
(not= (:id ?loc2) (:id ?loc1))]]
=>
(println "Too many lines of credit against: " (:id ?mortgage)))Here is the compiled rule (with debug lines removed):
(clojure.core/fn
[]
(clojure.core/let
[max-lines-of-credit-3727
(clojure.core/atom nil)
empty-count-3728
(clojure.core/atom 2)
net-3744
(clojure.core/atom nil)
?mortgage-3729
(engine.runtime/make-map)
?mortgage-cur-3730
(clojure.core/atom ?mortgage-3729)
op-3733
(clojure.core/fn
op-3733
[beta__2089__auto__]
(clojure.core/doseq
[cur__2090__auto__ (clojure.core/vals @?mortgage-cur-3730)]
(beta__2089__auto__ cur__2090__auto__)))
upstream-3734
op-3733
max-lines-of-credit-alpha-mortgage-3731
(engine.runtime/alpha-fun-no-tests
?mortgage-3729
?mortgage-cur-3730
empty-count-3728
max-lines-of-credit-3727)
max-lines-of-credit-alpha-rem-mortgage-3732
(engine.runtime/alpha-rem-fun ?mortgage-3729 empty-count-3728)
?loc-request-3736
(engine.runtime/make-map)
?loc-request-cur-3737
(clojure.core/atom ?loc-request-3736)
op-3740
(clojure.core/fn
op-3740
[hash-val__2086__auto__ beta__2087__auto__]
(clojure.core/doseq
[cur__2088__auto__
(clojure.core/vals
(.get @?loc-request-cur-3737 hash-val__2086__auto__))]
(beta__2087__auto__ cur__2088__auto__)))
upstream-3741
(clojure.core/fn
upstream-3741
[down3743]
(upstream-3734
(clojure.core/fn
upstream-3741
[?mortgage]
(op-3740
(:id ?mortgage)
(clojure.core/fn
subfun-3742
[?loc-request]
(down3743 ?mortgage ?loc-request))))))
max-lines-of-credit-alpha-loc-request-3738
(engine.runtime/alpha-hash-fun-no-tests
(clojure.core/fn [?loc-request] (:mortgage ?loc-request))
?loc-request-3736
?loc-request-cur-3737
empty-count-3728
max-lines-of-credit-3727)
max-lines-of-credit-alpha-rem-loc-request-3739
(engine.runtime/alpha-hash-rem-fun
?loc-request-3736
(clojure.core/fn [?loc-request] (:mortgage ?loc-request))
empty-count-3728)
empty-count-3746
(clojure.core/atom 2)
?loc1-3747
(engine.runtime/make-map)
?loc2-3755
(engine.runtime/make-map)
?loc1-cur-3748
(clojure.core/atom ?loc1-3747)
?loc2-cur-3756
(clojure.core/atom ?loc2-3755)
op-3751
(clojure.core/fn
op-3751
[hash-val__2086__auto__ beta__2087__auto__]
(clojure.core/doseq
[cur__2088__auto__
(clojure.core/vals
(.get @?loc1-cur-3748 hash-val__2086__auto__))]
(beta__2087__auto__
engine.runtime/*outer-vars*
cur__2088__auto__)))
op-3759
(clojure.core/fn
op-3759
[hash-val__2086__auto__ beta__2087__auto__]
(clojure.core/doseq
[cur__2088__auto__
(clojure.core/vals
(.get @?loc2-cur-3756 hash-val__2086__auto__))]
(beta__2087__auto__
engine.runtime/*outer-vars*
cur__2088__auto__)))
upstream-3745
(clojure.core/fn
upstream-3745
[down__2121__auto__]
(clojure.core/case
(clojure.core/get engine.runtime/*nand-modes* 'net-3744)
:pass
(upstream-3741
(clojure.core/fn
neg-3764
[?mortgage ?loc-request]
(clojure.core/binding
[engine.runtime/*outer-vars* [?mortgage ?loc-request]]
(@net-3744)
(clojure.core/let
[record__2122__auto__
(engine.runtime/get-sub-nand-record
engine.runtime/*nand-records*
'net-3744
engine.runtime/*outer-vars*)]
(if
record__2122__auto__
(do
(engine.runtime/put-sub-nand-record
engine.runtime/*nand-records*
'net-3744
engine.runtime/*outer-vars*
(engine.runtime/->Nand
'net-3744
engine.runtime/*outer-vars*
(clojure.core/atom #{})
(clojure.core/atom :live)))
(down__2121__auto__ ?mortgage ?loc-request)))))))
:sub
(clojure.core/let
[nand-records__2123__auto__
(.get engine.runtime/*nand-records* 'net-3744)]
(clojure.core/doseq
[nr__2124__auto__
(clojure.core/vals nand-records__2123__auto__)]
(clojure.core/binding
[engine.runtime/*outer-vars* (:wmes nr__2124__auto__)]
(@net-3744)
(clojure.core/let
[record__2122__auto__
(engine.runtime/get-sub-nand-record
engine.runtime/*nand-records*
'net-3744
engine.runtime/*outer-vars*)]
(if
record__2122__auto__
(do
(engine.runtime/put-sub-nand-record
engine.runtime/*nand-records*
'net-3744
engine.runtime/*outer-vars*
(engine.runtime/->Nand
'net-3744
engine.runtime/*outer-vars*
(clojure.core/atom #{})
(clojure.core/atom :live)))
(clojure.core/apply
down__2121__auto__
engine.runtime/*outer-vars*)))))))
:rem
(clojure.core/let
[nand-records__2123__auto__
engine.runtime/*nand-records*
nr__2124__auto__
(engine.runtime/get-sub-nand-record
nand-records__2123__auto__
'net-3744
engine.runtime/*outer-vars*)]
(clojure.core/let
[wmes__2125__auto__ (:wmes nr__2124__auto__)]
(clojure.core/apply down__2121__auto__ wmes__2125__auto__)))))
upstream-3752
(clojure.core/fn
upstream-3752
[down3754]
(op-3751
(clojure.core/let
[[?mortgage ?loc-request] engine.runtime/*outer-vars*]
(:id ?mortgage))
(clojure.core/fn
subfun-3753
[[?mortgage ?loc-request] ?loc1]
(down3754 [?mortgage ?loc-request] ?loc1))))
upstream-3760
(clojure.core/fn
upstream-3760
[down3762]
(upstream-3752
(clojure.core/fn
upstream-3760
[[?mortgage ?loc-request] ?loc1]
(op-3759
(:id ?mortgage)
(clojure.core/fn
subfun-3761
[[?mortgage ?loc-request] ?loc2]
(clojure.core/when
(clojure.core/and (not= (:id ?loc2) (:id ?loc1)))
(down3762 [?mortgage ?loc-request] ?loc1 ?loc2)))))))
max-lines-of-credit-alpha-loc-3749
(engine.runtime/alpha-hash-fun-no-tests
(clojure.core/fn [?loc1] (:mortgage ?loc1))
?loc1-3747
?loc1-cur-3748
empty-count-3746
(clojure.core/atom
(clojure.core/fn
[]
(engine.runtime/set-nand-mode 'net-3744 :sub)
(@max-lines-of-credit-3727)
(engine.runtime/set-nand-mode 'net-3744 :pass))))
max-lines-of-credit-alpha-loc-3757
(engine.runtime/alpha-hash-fun-no-tests
(clojure.core/fn [?loc2] (:mortgage ?loc2))
?loc2-3755
?loc2-cur-3756
empty-count-3746
(clojure.core/atom
(clojure.core/fn
[]
(engine.runtime/set-nand-mode 'net-3744 :sub)
(@max-lines-of-credit-3727)
(engine.runtime/set-nand-mode 'net-3744 :pass))))
max-lines-of-credit-alpha-rem-loc-3750
(engine.runtime/alpha-hash-rem-fun
?loc1-3747
(clojure.core/fn [?loc1] (:mortgage ?loc1))
empty-count-3746)
max-lines-of-credit-alpha-rem-loc-3758
(engine.runtime/alpha-hash-rem-fun
?loc2-3755
(clojure.core/fn [?loc2] (:mortgage ?loc2))
empty-count-3746)]
(.add
engine.runtime/*empty-counts*
[empty-count-3728 @empty-count-3728])
(.add
engine.runtime/*empty-counts*
[empty-count-3746 @empty-count-3746])
(clojure.core/reset!
net-3744
(clojure.core/fn
[]
(clojure.core/when
(clojure.core/= @empty-count-3746 0)
(upstream-3760
(clojure.core/fn
neg-conj-body-3763
[[?mortgage ?loc-request] ?loc1 ?loc2]
(engine.runtime/create-neg-instantiation
"max-lines-of-credit"
[?mortgage ?loc-request]
[?loc1 ?loc2]
'net-3744
'max-lines-of-credit-3727
0))))))
(.put engine.runtime/*net-funs* 'net-3744 max-lines-of-credit-3727)
(.put engine.runtime/*nand-modes* 'net-3744 :pass)
(clojure.core/reset!
max-lines-of-credit-3727
(clojure.core/fn
[]
(clojure.core/when
(clojure.core/= @empty-count-3728 0)
(upstream-3745
(clojure.core/fn
body-3765
[?mortgage ?loc-request]
(engine.runtime/create-instantiation
:engine.examples
:engine.examples/max-lines-of-credit
0
'max-lines-of-credit-3727
[?mortgage ?loc-request]
'[[net-3744 2]]
(clojure.core/fn
[]
(println
"Too many lines of credit against: "
(:id ?mortgage)))))))))
{:mortgage
[[max-lines-of-credit-alpha-mortgage-3731
max-lines-of-credit-alpha-rem-mortgage-3732
?mortgage-3729]],
:loc-request
[[max-lines-of-credit-alpha-loc-request-3738
max-lines-of-credit-alpha-rem-loc-request-3739
?loc-request-3736]],
:loc
[[max-lines-of-credit-alpha-loc-3749
max-lines-of-credit-alpha-rem-loc-3750
?loc1-3747]
[max-lines-of-credit-alpha-loc-3757
max-lines-of-credit-alpha-rem-loc-3758
?loc2-3755]]}))The wmes flow into the rule this way:
mortgage loc loc *
\ \ \ /
\ \ \ /
\ \ .
\ \ /
\ \ /
\ .
\ /
\ /
\ /
\ /
* {+pass, sub, rem}
\
\
The branch going up to the right represents the nested "nand" with two line of credit matches. The "*" is the nand node. It takes action any time execution begins from the bottom of the graph and begins in "pass" state. If a new mortgage wme is added, the wme is stored in the mortgage alpha node for the rule and execution is started from the bottom. When execution reaches the nand node (in "pass" mode) it passes execution up the left side and does the following for any successful upstream matches:
- Bind outer-vars to all the object match variables in any outer network. In this case, that's just one variable: ?mortgage.
- Run the entire inner nand subnetwork and collect any resulting negative instantiations.
- If there are any resulting instantiations, store them in a new nand record for the nand node keyed by the wmes; otherwise, call the downward function with the wmes.
If an outer wme is removed, the nand node will not get triggered as all the instantiations, nand-records, and other related data will get removed directly without invoking the network.
If a wme is added to the inner network, we have to look for matches with each wme combination that made it to the nand node. So, we iterate through the nand records and, for each one:
- Set the nand node to "sub" mode
- Bind outer-vars to the wmes.
- Run the inner network and if there are any resulting instantiations:
If there is already a nand record for the wmes, add the instantiation to its instantiation list and stop; otherwise, create a new nand-record, add the instantiation, and remove all positive instantiations downstream whose wmes include the wmes present at the nand node as a prefix.
If a wme is removed from the inner network, we remove all instantiations and set the nand node to "rem" mode and, when control reaches it from the bottom, we retrieve all nand records that match and no longer have any instantiations. The wmes contained in these nand records are passed to the downstream function.