Among different interesting problems in XAI, explaining classifiers' decisions has attracted significant attention[Bae+10; MI22; Mar24]. Work in this area has proposed notions to explain classifiers' behavior on a specific feature vector (instance), providing so-called local explanations, as well as notions to explain the overall classifier's behavior regardless of any particular instance, providing so-called global explanations. For both notions, a key issue is to analyze the computational complexity of the problems at hand, since this is crucial to understand how to approach the development of algorithmic solutions and their inherent limits. In the field of XAI, there has been an extensive body of work addressing complexity issues[BAK24; OPS23; Are+23; CM23; CA23; CCM23; Hua+22; Aud+22; CM22; Bar+20a; Mar+20] This paper falls within this ongoing research stream. Specifically, we deal with global explanations, and focus on so-called global necessary reasons, defined as conditions that instances must satisfy in order to be classified with a class of interest. This notion has been considered by Ignatiev, Narodytska, and Marques-Silva[INM19], where an interesting relationship with another kind of global explanation is shown.

The chase procedure is a fundamental algorithmic tool in databases that allows us to reason with constraints, such as existential rules, with a plethora of applications. It takes as input a database and a set of constraints, and iteratively completes the database as dictated by the constraints. A key challenge, though, is the fact that it may not terminate, which leads to the problem of checking whether it terminates given a database and a set of constraints. In this work, we focus on the semi-oblivious version of the chase, which is well-suited for practical implementations, and linear existential rules, a central class of constraints with several applications. In this setting, there is a mature body of theoretical work that provides syntactic characterizations of when the chase terminates, algorithms for checking chase termination, precise complexity results, and worst-case optimal bounds on the size of the result of the chase (whenever is finite). Our main objective is to experimentally evaluate the existing chase termination algorithms with the aim of understanding which input parameters affect their performance, clarifying whether they can be used in practice, and revealing their performance limitations.

Explaining why a database query result is obtained is an essential task towards the goal of Explainable AI, especially nowadays where expressive database query languages such as Datalog play a critical role in the development of ontology-based applications. A standard way of explaining a query result is the so-called why-provenance, which essentially provides information about the witnesses to a query result in the form of subsets of the input database that are sufficient to derive that result. To our surprise, despite the fact that the notion of why-provenance for Datalog queries has been around for decades and intensively studied, its computational complexity remains unexplored. The goal of this work is to fill this apparent gap in the why-provenance literature. Towards this end, we pinpoint the data complexity of why-provenance for Datalog queries and key subclasses thereof. The takeaway of our work is that why-provenance for recursive queries, even if the recursion is limited to be linear, is an intractable problem, whereas for non-recursive queries is highly tractable. Having said that, we experimentally confirm, by exploiting SAT solvers, that making why-provenance for (recursive) Datalog queries work in practice is not an unrealistic goal.

It is widely acknowledged that function symbols are an important feature in answer set programming, as they make modeling easier, increase the expressive power, and allow us to deal with infinite domains. The main issue with their introduction is that the evaluation of a program might not terminate and checking whether it terminates or not is undecidable. To cope with this problem, several classes of logic programs have been proposed where the use of function symbols is restricted but the program evaluation termination is guaranteed. Despite the significant body of work in this area, current approaches do not include many simple practical programs whose evaluation terminates. In this paper, we present the novel classes of rule-bounded and cycle-bounded programs, which overcome different limitations of current approaches by performing a more global analysis of how terms are propagated from the body to the head of rules. Results on the correctness, the complexity, and the expressivity of the proposed approach are provided.

Recent years have witnessed a great deal of interest in extending answer set programming with function symbols. Since the evaluation of a program with function symbols might not terminate and checking termination is undecidable, several classes of logic programs have been proposed where the use of function symbols is limited but the program evaluation is guaranteed to terminate. In this paper, we propose a novel class of logic programs whose evaluation always terminates. The proposed technique identifies terminating programs that are not captured by any of the current approaches. Our technique is based on the idea of measuring the size of terms and atoms to check whether the rule head size is bounded by the body, and performs a more fine-grained analysis than previous work. Rather than adopting an all-or-nothing approach (either we can say that the program is terminating or we cannot say anything),  our technique can identify arguments that are "limited'' (i.e., where there is no infinite propagation of terms) even when the program is not entirely recognized as terminating. Identifying arguments that are limited can support the user in the problem formulation and help other techniques that use limited arguments as a starting point. Another useful feature of our approach is that it is able to leverage external information about limited arguments. We also provide results on the correctness, the complexity, and the expressivity of our technique.