Schaerf, Marco
On the Complexity of Finding Second-Best Abductive Explanations
Liberatore, Paolo, Schaerf, Marco
While looking for abductive explanations of a given set of manifestations, an ordering between possible solutions is often assumed. The complexity of finding/verifying optimal solutions is already known. In this paper we consider the computational complexity of finding second-best solutions. We consider different orderings, and consider also different possible definitions of what a second-best solution is.
On the size of data structures used in symbolic model checking
Liberatore, Paolo, Schaerf, Marco
Temporal Logic Model Checking is a verification method in which we describe a system, the model, and then we verify whether some properties, expressed in a temporal logic formula, hold in the system. It has many industrial applications. In order to improve performance, some tools allow preprocessing of the model, verifying on-line a set of properties reusing the same compiled model; we prove that the complexity of the Model Checking problem, without any preprocessing or preprocessing the model or the formula in a polynomial data structure, is the same. As a result preprocessing does not always exponentially improve performance. Symbolic Model Checking algorithms work by manipulating sets of states, and these sets are often represented by BDDs. It has been observed that the size of BDDs may grow exponentially as the model and formula increase in size. As a side result, we formally prove that a superpolynomial increase of the size of these BDDs is unavoidable in the worst case. While this exponential growth has been empirically observed, to the best of our knowledge it has never been proved so far in general terms. This result not only holds for all types of BDDs regardless of the variable ordering, but also for more powerful data structures, such as BEDs, RBCs, MTBDDs, and ADDs.
Compilability of Abduction
Liberatore, Paolo, Schaerf, Marco
Abduction is one of the most important forms of reasoning; it has been successfully applied to several practical problems such as diagnosis. In this paper we investigate whether the computational complexity of abduction can be reduced by an appropriate use of preprocessing. This is motivated by the fact that part of the data of the problem (namely, the set of all possible assumptions and the theory relating assumptions and manifestations) are often known before the rest of the problem. In this paper, we show some complexity results about abduction when compilation is allowed.