net.sf.tweety.logics.cl.reasoner

Class CReasoner

java.lang.Object

net.sf.tweety.logics.cl.reasoner.AbstractConditionalLogicReasoner

net.sf.tweety.logics.cl.reasoner.CReasoner

All Implemented Interfaces:

ModelProvider<Conditional,ClBeliefSet,RankingFunction>, QualitativeReasoner<ClBeliefSet,PropositionalFormula>, Reasoner<java.lang.Boolean,ClBeliefSet,PropositionalFormula>

public class CReasoner
extends AbstractConditionalLogicReasoner

This class models a c-reasoner for conditional logic. Reasoning is performed by computing a minimal c-representation for the given knowledge base.
A c-representation for a conditional knowledge base R={r1,...,rn} is a ranking function k such that k accepts every conditional in R (k |= R) and if there are numbers k0,k1+,k1-,...,kn+,kn- with
k(w)=k0 + \sum_{w verifies ri} ki+ + \sum_{w falsifies ri} kj- for every w. A c-representation is minimal if k0+...+kn- is minimal. This reasoner uses mathematical optimization for solving the above problem and is usually faster than the brute force approach.

See Gabriele Kern-Isberner. Conditionals in nonmonotonic reasoning and belief revision. Lecture Notes in Computer Science, Volume 2087. 2001.

Author:

Matthias Thimm

Constructor Summary

Constructors

Constructor and Description
CReasoner()

Method Summary

Modifier and Type	Method and Description
private Statement	getAcceptanceConstraint(Conditional cond, java.util.Map<PossibleWorld,IntegerVariable> ranks) For the given conditional (B|A) and the given ranks of possible worlds, this method constructs the inequation k(AB) < k(A-B) where k(AB) is the minimum of the ranks of the interpretations that satisfy AB.
RankingFunction	getModel(ClBeliefSet kb) Returns a single (dedicated) model of the given belief base.
private Statement	getRankConstraint(PossibleWorld w, IntegerVariable ranki, java.util.Map<Conditional,IntegerVariable> kappa_pos, java.util.Map<Conditional,IntegerVariable> kappa_neg) For the given interpretation "i" and the given kappas, this method computes the constraint k("i")=\sum_{"i" verifies ri} ki+ + \sum_{"i" falsifies ri} kj-

Methods inherited from class net.sf.tweety.logics.cl.reasoner.AbstractConditionalLogicReasoner

getModels, query

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

CReasoner

public CReasoner()

Method Detail

getAcceptanceConstraint

private Statement getAcceptanceConstraint(Conditional cond,
                                          java.util.Map<PossibleWorld,IntegerVariable> ranks)

For the given conditional (B|A) and the given ranks of possible worlds, this method constructs the inequation k(AB) < k(A-B) where k(AB) is the minimum of the ranks of the interpretations that satisfy AB.

Parameters:

cond - a conditional

ranks - a map from possible worlds to integer variables

Returns:

an inequation representing the acceptance of the conditional

getRankConstraint

private Statement getRankConstraint(PossibleWorld w,
                                    IntegerVariable ranki,
                                    java.util.Map<Conditional,IntegerVariable> kappa_pos,
                                    java.util.Map<Conditional,IntegerVariable> kappa_neg)

For the given interpretation "i" and the given kappas, this method computes the constraint
k("i")=\sum_{"i" verifies ri} ki+ + \sum_{"i" falsifies ri} kj-

Parameters:

i - a possible world.

ranki - the rank (an integer variable) of interpretation "i".

kappa_pos - the positive penalties for interpretations.

kappa_min - the negative penalties for interpretations.

getModel

public RankingFunction getModel(ClBeliefSet kb)

Description copied from interface: ModelProvider

Returns a single (dedicated) model of the given belief base. If the implemented method allows for more than one dedicated model, the selection may be non-deterministic.

Specified by:

getModel in interface ModelProvider<Conditional,ClBeliefSet,RankingFunction>

Specified by:

getModel in class AbstractConditionalLogicReasoner

Returns:

a selected model of the belief base.