Upgrading Ambiguous Signs in QPNs
This work addresses inference challenges for users of qualitative probabilistic networks, though it appears incremental as it builds on existing concepts to improve sign handling.
The paper tackles the problem of uninformative inference results in qualitative probabilistic networks due to ambiguous signs from non-monotonic influences, by introducing situational signs to capture effects in the current network state and providing a method to determine signs when influences reduce to monotonic, resulting in more informative outcomes.
WA qualitative probabilistic network models the probabilistic relationships between its variables by means of signs. Non-monotonic influences have associated an ambiguous sign. These ambiguous signs typically lead to uninformative results upon inference. A non-monotonic influence can, however, be associated with a, more informative, sign that indicates its effect in the current state of the network. To capture this effect, we introduce the concept of situational sign. Furthermore, if the network converts to a state in which all variables that provoke the non-monotonicity have been observed, a non-monotonic influence reduces to a monotonic influence. We study the persistence and propagation of situational signs upon inference and give a method to establish the sign of a reduced influence.