// https://github.com/opennars/opennars/blob/master/nars_core/nars/entity/Concept.java#L233
/**
* To accept a new judgment as belief, and check for revisions and solutions
*
* \param judg The judgment to be accepted
* \param task The task to be processed
* \return Whether to continue the processing of the task
*/
private void processJudgment(DerivationContext ctx, ClassicalConcept @this, ClassicalTask task)
{
ClassicalSentence judgement = task.sentence;
ClassicalTask oldBeliefTask = selectCandidate(task, @this.beliefs, EnumForRevision.REVISION);// only revise with the strongest
// QUESTION< how about projection? >
ClassicalSentence oldBelief = null;
if (oldBeliefTask != null)
{
oldBelief = oldBeliefTask.sentence;
Stamp newStamp = judgement.stamp;
Stamp oldStamp = oldBelief.stamp; // when table is full, the latter check is especially important, too
if (
Stamp.checkEquals(
newStamp,
oldStamp,
Stamp.EnumCompareCreationTime.NO,
Stamp.EnumCompareOccurrenceTime.YES,
Stamp.EnumCompareEvidentialBaseTime.YES) &&
task.sentence.truth == oldBelief.truth
)
{
memory.removeTask(task, "Duplicated");
return;
}
else if (LocalRules.revisible(judgement, oldBelief))
{
// commented because we have to implement revision like in the legacy system
// projection can occur either with the NARS default or the new exponential decay model
// QUESTION< which one is better ? >
ctx.setTheNewStamp(newStamp, oldStamp, memory.time);
ClassicalSentence projectedBelief = oldBelief.projection(memory.time, newStamp.occurrenceTime);
if (projectedBelief != null)
{
if (projectedBelief.stamp.occurrenceTime != oldBelief.stamp.occurrenceTime)
{
// nal.singlePremiseTask(projectedBelief, task.budget);
}
ctx.currentBelief = projectedBelief;
LocalRules.revision(ctx, judgement, projectedBelief, false);
}
}
}
if (task.isAboveThreshold)
{
foreach (var iQuestion in @this.questions)
{
LocalRules.trySolution(judgement, iQuestion, ctx, true, memory);
}
TableMaintenance.addToTable(task, false, @this.beliefs, Parameters.CONCEPT_BELIEFS_MAX, null /*ConceptBeliefAdd.class*/, null /*ConceptBeliefRemove.class*/);
// if taskLink predicts this concept then add to predictive
// TODO
}
}
public void SaveLocalRulesToModel()
{
if (_model == null)
{
return;
}
_model.RemoveAnnotation("BestPractizeAnalyzer"); // Stupid typo in earlier version
_model.SetAnnotation("BestPracticeAnalyzer", LocalRules.SerializeToJson(), false);
}
public void AddRule(BestPracticeRule rule, bool global = false)
{
rule.ID = GetUniqueId(rule.ID);
if (global)
{
GlobalRules.Add(rule);
}
else
{
LocalRules.Add(rule);
}
DoCollectionChanged(NotifyCollectionChangedAction.Add, rule);
}
public string GetUniqueId(string id)
{
int suffix = 1;
var testId = id;
while (
GlobalRules.Contains(testId) ||
LocalRules.Contains(testId)
)
{
suffix++;
testId = id + "_" + suffix;
}
return(testId);
}
public void SaveLocalRulesToModel()
{
if (_model == null)
{
return;
}
_model.RemoveAnnotation("BestPractizeAnalyzer"); // Stupid typo in earlier version
var previousAnnotation = _model.GetAnnotation(BPAAnnotation);
var newAnnotation = LocalRules.SerializeToJson();
_model.SetAnnotation(BPAAnnotation, newAnnotation, false);
if (previousAnnotation != newAnnotation)
{
UI.UIController.Current.Handler.UndoManager.FlagChange();
}
}
// https://github.com/opennars/opennars/blob/4515f1d8e191a1f097859decc65153287d5979c5/nars_core/nars/entity/Concept.java#L648
/**
* To answer a question by existing beliefs
*
* \param task The task to be processed
* \return Whether to continue the processing of the task
*/
private void processQuestion(DerivationContext ctx, ClassicalConcept @this, ClassicalTask task)
{
ClassicalTask quesTask = task;
bool newQuestion = true;
foreach (ClassicalTask t in @this.questions)
{
if (t.sentence.equalsContent(quesTask.sentence))
{
quesTask = t;
newQuestion = false;
break;
}
}
if (newQuestion)
{
if (@this.questions.Count + 1 > Parameters.CONCEPT_QUESTIONS_MAX)
{
ClassicalTask removed = @this.questions[0];
@this.questions.RemoveAt(0); // FIFO
//memory.event.emit(ConceptQuestionRemove.class, this, removed);
}
@this.questions.Add(task);
// memory.event.emit(ConceptQuestionAdd.class, this, task);
}
ClassicalSentence ques = quesTask.sentence;
ClassicalTask newAnswerT = (ques.isQuestion)
? selectCandidate(quesTask, @this.beliefs, EnumForRevision.NOREVISION)
: selectCandidate(quesTask, @this.desires, EnumForRevision.NOREVISION);
if (newAnswerT != null)
{
LocalRules.trySolution(newAnswerT.sentence, task, ctx, true, memory);
}
// commented because memory events are not implemented
//else if(task.isInput() && !quesTask.getTerm().hasVarQuery() && quesTask.getBestSolution() != null) { //show previously found solution anyway in case of input
//memory.emit(Events.Answer.class, quesTask, quesTask.getBestSolution());
//}
}
// https://github.com/opennars/opennars/blob/62c814fb0f3e474a176515103394049b2887ec29/nars_core/nars/entity/Concept.java#L440
/**
* To accept a new goal, and check for revisions and realization, then
* decide whether to actively pursue it
*
* \param judg The judgment to be accepted
* \param task The task to be processed
* \return Whether to continue the processing of the task
*/
private bool processGoal(DerivationContext ctx, ClassicalConcept @this, ClassicalTask task)
{
ClassicalSentence goal = task.sentence;
ClassicalTask oldGoalT = selectCandidate(task, @this.desires, EnumForRevision.REVISION); // revise with the existing desire values
ClassicalSentence oldGoal = null;
if (oldGoalT != null)
{
oldGoal = oldGoalT.sentence;
Stamp newStamp = goal.stamp;
Stamp oldStamp = oldGoal.stamp;
bool isStampEqual = Stamp.checkEquals(
newStamp,
oldStamp,
Stamp.EnumCompareCreationTime.NO,
Stamp.EnumCompareOccurrenceTime.NO,
Stamp.EnumCompareEvidentialBaseTime.YES);
if (isStampEqual)
{
return(false); // duplicate
}
if (LocalRules.revisible(goal, oldGoal))
{
ctx.setTheNewStamp(newStamp, oldStamp, memory.time);
ClassicalSentence projectedGoal = oldGoal.projection(task.sentence.stamp.occurrenceTime, newStamp.occurrenceTime);
if (projectedGoal != null)
{
// if (goal.after(oldGoal, nal.memory.param.duration.get())) { //no need to project the old goal, it will be projected if selected anyway now
// nal.singlePremiseTask(projectedGoal, task.budget);
//return;
// }
ctx.currentBelief = projectedGoal;
if (true /* TODO< check if not operation > !(task.sentence.term instanceof Operation)*/)
{
bool successOfRevision = LocalRules.revision(ctx, task.sentence, projectedGoal, false);
if (successOfRevision) // it is revised, so there is a new task for which this function will be called
{
return(false); // with higher/lower desire
} // it is not allowed to go on directly due to decision making https://groups.google.com/forum/#!topic/open-nars/lQD0no2ovx4
}
}
}
}
Stamp s2 = goal.stamp.clone();
s2.occurrenceTime = memory.time;
if (s2.after(task.sentence.stamp, ctx.memory.param.duration.value)) // this task is not up to date we have to project it first
{
ClassicalSentence projGoal = task.sentence.projection(memory.time, ctx.memory.param.duration.value);
if (projGoal != null && projGoal.truth.expectation > ctx.memory.param.decisionThreshold)
{
ctx.singlePremiseTask(projGoal, task.budget.clone()); // keep goal updated
// return false; //outcommented, allowing "roundtrips now", relevant for executing multiple steps of learned implication chains
}
}
if (task.isAboveThreshold)
{
ClassicalTask beliefT = selectCandidate(task, @this.beliefs, EnumForRevision.NOREVISION); // check if the Goal is already satisfied
double antiSatisfaction = 0.5; // we dont know anything about that goal yet, so we pursue it to remember it because its maximally unsatisfied
if (beliefT != null)
{
ClassicalSentence belief = beliefT.sentence;
ClassicalSentence projectedBelief = belief.projection(task.sentence.stamp.occurrenceTime, ctx.memory.param.duration.value);
LocalRules.trySolution(projectedBelief, task, ctx, true, memory); // check if the Goal is already satisfied (manipulate budget)
antiSatisfaction = task.sentence.truth.getExpDifAbs(belief.truth);
}
double satisfaction = 1.0 - antiSatisfaction;
TruthValue t = goal.truth.clone();
t.frequency = (float)(t.frequency - satisfaction); // decrease frequency according to satisfaction value
bool fullfilled = antiSatisfaction < Parameters.SATISFACTION_TRESHOLD;
ClassicalSentence projectedGoal = goal.projection(ctx.memory.time, ctx.memory.time); // NOTE< this projection is fine >
/* commented because we stil have to implement operations
* if (projectedGoal != null && task.isAboveThreshold && !fullfilled && projectedGoal.truth.expectation > nal.memory.param.decisionThreshold.get()) {
*
* try {
* Operation bestop = null;
* float bestop_truthexp = 0.0f;
* TruthValue bestop_truth = null;
* Task executable_precond = null;
* //long distance = -1;
* long mintime = -1;
* long maxtime = -1;
* for (Task t: this.executable_preconditions) {
* Term[] prec = ((Conjunction)((Implication)t.getTerm()).getSubject()).term;
* Term[] newprec = new Term[prec.length - 3];
* for (int i = 0; i < prec.length - 3; i++) { //skip the last part: interval, operator, interval
* newprec[i] = prec[i];
* }
*
* //distance = Interval.magnitudeToTime(((Interval)prec[prec.length-1]).magnitude, nal.memory.param.duration);
* mintime = nal.memory.time() + Interval.magnitudeToTime(((Interval)prec[prec.length - 1]).magnitude - 1, nal.memory.param.duration);
* maxtime = nal.memory.time() + Interval.magnitudeToTime(((Interval)prec[prec.length - 1]).magnitude + 2, nal.memory.param.duration);
*
* Operation op = (Operation)prec[prec.length - 2];
* Term precondition = Conjunction.make(newprec, TemporalRules.ORDER_FORWARD);
*
* Concept preconc = nal.memory.concept(precondition);
* long newesttime = -1;
* Task bestsofar = null;
* if (preconc != null) { //ok we can look now how much it is fullfilled
*
* //check recent events in event bag
* for (Task p : this.memory.sequenceTasks) {
* if (p.sentence.term.equals(preconc.term) && p.sentence.isJudgment() && !p.sentence.isEternal() && p.sentence.getOccurenceTime() > newesttime && p.sentence.getOccurenceTime() <= memory.time()) {
* newesttime = p.sentence.getOccurenceTime();
* bestsofar = p; //we use the newest for now
* }
* }
* if (bestsofar == null) {
* continue;
* }
* //ok now we can take the desire value:
* TruthValue A = projectedGoal.getTruth();
* //and the truth of the hypothesis:
* TruthValue Hyp = t.sentence.truth;
* //and the truth of the precondition:
* Sentence projectedPrecon = bestsofar.sentence.projection(memory.time() comment*- distance comment*, memory.time());
*
* if (projectedPrecon.isEternal()) {
* continue; //projection wasn't better than eternalization, too long in the past
* }
* //debug start
* //long timeA = memory.time();
* //long timeOLD = bestsofar.sentence.stamp.getOccurrenceTime();
* //long timeNEW = projectedPrecon.stamp.getOccurrenceTime();
* //debug end
* TruthValue precon = projectedPrecon.truth;
* //and derive the conjunction of the left side:
* TruthValue leftside = TruthFunctions.desireDed(A, Hyp);
* //in order to derive the operator desire value:
* TruthValue opdesire = TruthFunctions.desireDed(precon, leftside);
*
* float expecdesire = opdesire.getExpectation();
* if (expecdesire > bestop_truthexp) {
* bestop = op;
* bestop_truthexp = expecdesire;
* bestop_truth = opdesire;
* executable_precond = t;
* }
* }
* }
*
* if (bestop != null && bestop_truthexp > memory.param.decisionThreshold.get()) {
* Task t = new Task(new Sentence(bestop, Symbols.JUDGMENT_MARK, bestop_truth, projectedGoal.stamp), new BudgetValue(1.0f, 1.0f, 1.0f));
* //System.out.println("used " +t.getTerm().toString() + String.valueOf(memory.randomNumber.nextInt()));
* if (!task.sentence.stamp.evidenceIsCyclic()) {
* if (!executeDecision(t)) { //this task is just used as dummy
* memory.emit(UnexecutableGoal.class, task, this, nal);
* } else {
* memory.decisionBlock = memory.time() + Parameters.AUTOMATIC_DECISION_USUAL_DECISION_BLOCK_CYCLES;
* SyllogisticRules.generatePotentialNegConfirmation(nal, executable_precond.sentence, executable_precond.budget, mintime, maxtime, 2);
* }
* }
* }
* }
* catch(Exception ex) {
* System.out.println("Failure in operation choice rule, analyze!");
* }
*
*
*
* questionFromGoal(task, ctx);
*
*
* addToTable(task, false, desires, Parameters.CONCEPT_GOALS_MAX, ConceptGoalAdd.class, ConceptGoalRemove.class);
*
* InternalExperience.InternalExperienceFromTask(memory, task,false);
*
* if(nal.memory.time >= memory.decisionBlock && !executeDecision(task)) {
* // commented because we haven't implemented the observer until now memory.emit(UnexecutableGoal.class, task, this, nal);
* return true; // it was made true by itself
* }
* return false;
* } // big if
*/
return(fullfilled);
}
return(false);
}
/// <summary>
/// This is the method that actually does the work.
/// </summary>
/// <param name="DA">The DA object is used to retrieve from inputs and store in outputs.</param>
protected override void SolveInstance(IGH_DataAccess DA)
{
//Declare inputs
Curve BounderyCrv = null;
Plane?xyPlane = null;
List <GeometryBase> obstacles = new List <GeometryBase>();
int stepSize = 10;
List <double> customRules = new List <double>();
//Get inputs
DA.GetData(0, ref BounderyCrv);
DA.GetData(1, ref xyPlane);
DA.GetDataList(2, obstacles);
DA.GetData(3, ref stepSize);
DA.GetDataList(4, customRules);
//Select localrules Temporary
LocalRules localRules;
if (customRules.Count == 0)
{
localRules = new LocalRules();
}
else
{
localRules = new LocalRules(customRules);
}
//Create Parkinglots - Check if there is a plane userinput
ParkingLot parkingLot;
if (xyPlane.HasValue == false)
{
//Create Parking lot with the best Plane orientation
int[] numberOfParkinglots = new int[180];
int[] degrees = new int[180];
int maxNoLots = int.MinValue;
int degreeIndex = 0;
Parallel.For(0, 180 / stepSize, i =>
{
Plane newPlane = Plane.WorldXY;
newPlane.Rotate(i * stepSize * (Math.PI / 180.0), new Vector3d(0, 0, 1));
try
{
var tempParkingLot = new ParkingLot(BounderyCrv, localRules, newPlane, obstacles);
numberOfParkinglots.SetValue(tempParkingLot.parkingspaceNO, i);
degrees.SetValue(i * stepSize, i);
}
catch
{
}
});
for (int i = 0; i < 180 / stepSize; i++)
{
try
{
if (numberOfParkinglots[i] > maxNoLots)
{
maxNoLots = numberOfParkinglots[i];
degreeIndex = i;
}
}
catch
{
}
}
Plane bestPlane = Plane.WorldXY;
bestPlane.Rotate(degreeIndex * stepSize * (Math.PI / 180.0), new Vector3d(0, 0, 1));
parkingLot = new ParkingLot(BounderyCrv, localRules, bestPlane, obstacles);
}
else
{
//Create Parking lot with user choosen Plane
parkingLot = new ParkingLot(BounderyCrv, localRules, xyPlane.Value, obstacles);
}
List <Curve> SpaceCurves = new List <Curve>();
List <Rectangle3d> debuging = new List <Rectangle3d>();
foreach (Parkingspace lot in parkingLot.Spaces)
{
SpaceCurves.Add(lot.Curves);
//debuging.Add(lot.ManeuveringBox);
}
//Set Outputs
DA.SetDataList(0, SpaceCurves);
//DA.SetDataList(1, parkingLot.notParkingSpaces);
//DA.SetDataList(2, parkingLot.debug);
}
