/**
* Creates a starting proposal for a new negotiation of a need. In this implementation, the
* mostly preferred value for each competence attribute is selected to compose the proposal.
*
* @param competence The competence being negotiated
* @return A new proposal
*/
public Proposal createStartingProposal(Need need, Competence competence) {
Proposal proposal = new Proposal(myAgent.getAID());
proposal.setNeedType(competence.getType());
// fill attribute values
for (int j = 0; j < competence.getNumberOfAttributes(); j++) {
Attribute ownAttribute = (Attribute) competence.getAttributes().get(j);
AttributeValue valueToPropose = new AttributeValue();
valueToPropose.setName(ownAttribute.getName());
valueToPropose.setType(ownAttribute.getType());
Attribute askedAttribute = need.getAttribute(ownAttribute.getName());
if (!ownAttribute.isDiscrete()) {
float ownPreferredValue =
checkAskedContinuousDomain(
askedAttribute, Float.parseFloat(ownAttribute.getPreferredValue().toString()));
if (ownAttribute
.getType()
.equals("float")) { // check if this is really of type float, or integer
valueToPropose.setValue(String.valueOf(ownPreferredValue));
} else {
valueToPropose.setValue(String.valueOf((int) ownPreferredValue));
}
} else {
String ownPreferredValue =
checkAskedDiscreteDomain(askedAttribute, ownAttribute.getPreferredValue().toString());
valueToPropose.setValue(ownPreferredValue);
}
proposal.getAttributeValues().add(valueToPropose);
}
return proposal;
}
/**
* Creates a counterproposal, based on qualitative feedback on the previous proposal. According
* to the feedback received (on each AttributeValue of the Proposal), the enterpriseAgent
* generates a new value for each proposal's attribute. Calculating a new value for an attribute
* depends on the classification ("excellent", "sufficient", "bad", "very bad") and direction
* ("up", "down") feedback elements at an AttributeValue. In this implementation:
*
* <ul>
* <li>For discrete attributes, a new random value is selected within the domain of values for
* this attribute.
* <li>For continuous attributes:
* <ul>
* <li>If classification = "very bad", the value is changed in 10% towards the indicated
* direction.
* <li>If classification = "bad", the value is changed in 5% towards the indicated
* direction.
* <li>If classification = "sufficient", the value is changed in 0.5% towards the
* indicated direction.
* <li>If classification = "excellent", the value is not changed.
* </ul>
* </ul>
*/
public Proposal createCounterProposal(
ProposalEvaluation feedback, Need need, Competence competence) {
// final float DELTA_EXCELLENT = 0.0f;
// final float DELTA_SUFFICIENT = 0.005f;
// final float DELTA_BAD = 0.5f;
// final float DELTA_VERYBAD = 3f;
final float DELTA_EXCELLENT = 0.0f;
final float DELTA_SUFFICIENT = 0.5f;
final float DELTA_BAD = 0.7f;
final float DELTA_VERYBAD = 1.2f;
// final float DELTA_SUFFICIENT = 0.05f;
// final float DELTA_BAD = 0.5f;
// final float DELTA_VERYBAD = 1.5f;
Proposal proposal = new Proposal(myAgent.getAID());
proposal.setNeedType(feedback.getNeedType());
for (int i = 0; i < feedback.getAttributeValueEvaluations().size(); i++) {
AttributeValueEvaluation attributeValueEvaluation =
(AttributeValueEvaluation) feedback.getAttributeValueEvaluations().get(i);
Attribute askedAttribute = need.getAttribute(attributeValueEvaluation.getName());
Attribute ownAttribute = competence.getAttribute(attributeValueEvaluation.getName());
AttributeValue valueToPropose = new AttributeValue();
valueToPropose.setName(attributeValueEvaluation.getName());
valueToPropose.setType(attributeValueEvaluation.getType());
if (attributeValueEvaluation.isDiscrete()) { // discrete attribute
if (attributeValueEvaluation.getClassif()
== AttributeValueEvaluation.AttributeClassification.BAD
|| attributeValueEvaluation.getClassif()
== AttributeValueEvaluation.AttributeClassification.VERY_BAD) {
// propose a new random value for this attribute
List domain = ownAttribute.getDiscreteDomain();
String proposedValue =
(String)
domain.get(
(int)
(domain.size()
* Math
.random())); // FIXME: may get the same value or a previously
// proposed one!
valueToPropose.setValue(proposedValue);
} else {
// maintain previous value
valueToPropose.setValue(attributeValueEvaluation.getValue());
}
} else { // continuous attribute
float floatValue =
Float.parseFloat(attributeValueEvaluation.getValue()); // treat as float
float difToMaxEdge =
Float.parseFloat((String) askedAttribute.getContinuousDomainMax()) - floatValue;
float difToMinEdge =
floatValue - Float.parseFloat((String) askedAttribute.getContinuousDomainMin());
switch (attributeValueEvaluation.getClassif()) {
case EXCELLENT:
floatValue += DELTA_EXCELLENT * floatValue;
break;
case SUFFICIENT:
if (attributeValueEvaluation.getDirection()
== AttributeValueEvaluation.AttributeDirection.UP) {
floatValue += DELTA_SUFFICIENT * difToMaxEdge;
} else {
floatValue -= DELTA_SUFFICIENT * difToMinEdge;
}
break;
case BAD:
if (attributeValueEvaluation.getDirection()
== AttributeValueEvaluation.AttributeDirection.UP) {
floatValue += DELTA_BAD * difToMaxEdge;
} else if (attributeValueEvaluation.getDirection()
== AttributeValueEvaluation.AttributeDirection.DOWN) {
floatValue -= DELTA_BAD * difToMinEdge;
}
break;
case VERY_BAD:
if (attributeValueEvaluation.getDirection()
== AttributeValueEvaluation.AttributeDirection.UP) {
floatValue += DELTA_VERYBAD * difToMaxEdge;
} else if (attributeValueEvaluation.getDirection()
== AttributeValueEvaluation.AttributeDirection.DOWN) {
floatValue -= DELTA_VERYBAD * difToMinEdge;
}
break;
}
floatValue = checkOwnContinuousDomain(ownAttribute, floatValue);
floatValue = checkAskedContinuousDomain(askedAttribute, floatValue);
if (attributeValueEvaluation
.getType()
.equals("float")) { // check if this is really of type float, or integer
valueToPropose.setValue(String.valueOf(floatValue));
} else {
valueToPropose.setValue(String.valueOf(Math.round(floatValue)));
}
}
proposal.getAttributeValues().add(valueToPropose);
}
return proposal;
}