Uncertain Inheritance and Recognition as Probabilistic Default Reasoning
This paper proposes probabilistic default reasoning as a suitable approach to uncertain inheritance and recognition for uncertain object-oriented models. The uncertainty is due to the uncertain membership of an object to a class and/or the uncertain applicability of a property, i.e., an attribute or a method, to a class. Firstly, we introduce a logic-based uncertain object-oriented model where uncertain membership and applicability are measured by support pairs, which are lower and upper bounds on probability. The probability for a property being applicable to a class is interpreted as the conditional probability of the property being applicable to an object given that the object is a member of the class. Each uncertainly applicable property is then a default probabilistic logic rule, which is defeasible. In order to reduce the computational complexity of general probabilistic default reasoning, we propose to use Jeffrey's rule for a weaker notion of consistency and for local inference, then apply them to uncertain inheritance of attributes and methods. Using the same approach but with inverse Jeffrey's rule, uncertain recognition as probabilistic default reasoning is also presented.