We study monitoring of linear-time arithmetic properties against finite traces generated by an unknown dynamic system. The monitoring state is determined by considering at once the trace prefix seen so far, and all its possible finite-length, future continuations. This makes monitoring at least as hard as satisfiability and validity. Traces consist of finite sequences of assignments of a fixed set of variables to numerical values. Properties are specified in a logic we call ALTLf, combining LTLf (LTL on finite traces) with linear arithmetic constraints that may carry lookahead, i.e., variables may be compared over multiple instants of the trace. While the monitoring problem for this setting is undecidable in general, we show decidability for (a) properties without lookahead, and (b) properties with lookahead that satisfy the abstract, semantic condition of finite summary, studied before in the context of model checking. We then single out concrete, practically relevant classes of constraints guaranteeing finite summary. Feasibility is witnessed by a prototype implementation.

The use of Dynamic Epistemic Logic (DEL) in multi-agent planning has led to a widely adopted action formalism that can handle nondeterminism, partial observability and arbitrary knowledge nesting. As such expressive power comes at the cost of undecidability, several decidable fragments have been isolated, mainly based on syntactic restrictions of the action formalism. In this paper, we pursue a novel semantic approach to achieve decidability. Namely, rather than imposing syntactical constraints, the semantic approach focuses on the axioms of the logic for epistemic planning. Specifically, we augment the logic of knowledge S5n and with an interaction axiom called (knowledge) commutativity, which controls the ability of agents to unboundedly reason on the knowledge of other agents. We then provide a threefold contribution. First, we show that the resulting epistemic planning problem is decidable. In doing so, we prove that our framework admits a finitary non-fixpoint characterization of common knowledge, which is of independent interest. Second, we study different generalizations of the commutativity axiom, with the goal of obtaining decidability for more expressive fragments of DEL. Finally, we show that two well-known epistemic planning systems based on action templates, when interpreted under the setting of knowledge, conform to the commutativity axiom, hence proving their decidability.

Within the growing area of data-aware processes, Data Petri nets (DPNs) with arithmetic data have recently gained popularity thanks to their ability to balance simplicity with expressiveness. DPNs can be automatically mined from event data, but these process discovery techniques typically come without any correctness guarantees. In particular, the generated models may violate the crucial property of data-aware soundness. While data-aware soundness can be checked automatically for a large class of models, nothing is known about how to repair such processes once a violation is detected. In this paper we are concerned with repairing DPNs so that the refined model satisfies the desired soundness properties. Our approach is based on conservative behavioural changes, which are minimally invasive in the sense that the behaviour of the repaired model coincides with that of the original model except for (prefixes of) traces that caused the violation. We show experimentally that the approach can be used to repair unsound DPNs from the literature.

Conformance checking, one of the central tasks in process mining, compares the expected behavior described by a reference process model to the actual behavior recorded in an event log, with the goal of detecting deviations. Traditionally, it is assumed that the log provides a faithful and complete digital footprint of reality. However, assuming perfect logs is often unrealistic: real-life logs typically suffer from data quality issues, exposing uncertainty in their events, timestamps, and data attributes. We attack this problem by introducing a comprehensive framework for multi-perspective conformance checking dealing with uncertainty along three perspectives: control-flow, time, and data. From the modeling point of view, we consider process models formalized as Petri nets operating over data variables, and event logs presenting uncertainty at the event- and attribute-level. We cast conformance checking as an alignment problem, extending the traditional notions of alignment and cost function to deal with uncertainty along the three aforementioned perspectives. From the operational point of view, we show how (optimal) alignments can be computed through well-established automated reasoning techniques from Satisfiability Modulo Theories (SMT). Specifically, we show how previous results on data-aware SMT-based conformance checking can be lifted to this more sophisticated setting, obtaining a flexible framework that can seamlessly handle different variants of the problem. We formally prove correctness of our approach and implement it in the conformance checker cocomot. Finally, we perform a thorough experimental evaluation on synthetic and real-life logs, demonstrating the overall promising performance of our framework.

Conformance checking is a key process mining task to confront the normative behavior imposed by a process model with the actual behavior recorded in a log. While this problem has been extensively studied for pure control-flow processes, data-aware conformance checking has received comparatively little attention. In this paper, we tackle the conformance checking problem for the challenging scenario of processes that combine data and control-flow dimensions. Concretely, we adopt the formalism of data Petri nets (DPNs) and show how solid, well-established automated reasoning techniques from the area of Satisfiability Modulo Theories (SMT) can be effectively harnessed to compute conformance metrics and optimal data-aware alignments. To this end, we introduce the CoCoMoT (Computing Conformance Modulo Theories) framework, with a fourfold contribution. First, we show how SMT allows to leverage SAT-based encodings for the pure control-flow setting to the data-aware case. Second, we introduce a novel preprocessing technique based on a notion of property-preserving clustering, to speed up the computation of conformance checking outputs. Third, we show how our approach extends seamlessly to the more comprehensive conformance checking artifacts of multi- and anti-alignments. Fourth, we describe a proof-of-concept implementation based on state-of-the-art SMT solvers, and report on experiments. Finally, we discuss how CoCoMoT directly lends itself to further process mining tasks like log analysis by clustering and model repair, and the use of SMT facilitates the support of even richer multi-perspective models, where, for example, more expressive DPN guards languages are considered or generic datatypes (other than integers or reals) are employed.

Combined modeling and verification of dynamic systems and the data they operate on has gained momentum in AI and in several application domains. We investigate the expressive yet concise framework of data-aware dynamic systems (DDS), extending it with linear arithmetic, and providing the following contributions. First, we introduce a new, semantic property of “finite summary”, which guarantees the existence of a faithful finite-state abstraction. We rely on this to show that checking whether a witness exists for a linear-time, finite-trace property is decidable for DDSs with finite summary. Second, we demonstrate that several decidability conditions studied in formal methods and database theory can be seen as concrete, checkable instances of this property. This also gives rise to new decidability results. Third, we show how the abstract, uniform property of finite summary leads to modularity results: a system enjoys finite summary if it can be partitioned appropriately into smaller systems that possess the property. Our results allow us to analyze systems that were out of reach in earlier approaches. Finally, we demonstrate the feasibility of our approach in a prototype implementation.

Manufacturing is transitioning from a mass production model to a service model in which facilities `bid' for previously unseen products. To decide whether to bid for a previously unseen product, a facility must be able to synthesize, on the fly, a process plan controller that delegates abstract manufacturing tasks in a supplied process recipe to the available manufacturing resources. First-order representations of the state are commonly considered in reasoning about action in AI. Here we show that we can leverage the wide literature on the Situation Calculus automatically synthesize such controllers. We identify two important decidable cases---finite domains and bounded action theories---for which we provide practical synthesis techniques.

We study the problem of verifying whether a given parameterized multi-agent system (PMAS) is safe, namely whether none of its possible executions can lead to bad states. These are captured by a state formula existentially quantifying over agents. As the MAS is parameterized, it only describes the finite set of possible agent templates, while the actual number of concrete agent instances that will be present at runtime, for each template, is unbounded and cannot be foreseen. We solve this problem via infinite-state model checking based on satisfiability modulo theories (SMT), relying on the theory of array-based systems. We formally characterize the soundness, completeness and termination guarantees of our approach under specific assumptions. This gives us a technique that is implementable on top of third-party, SMT-based model checkers. Finally, we discuss how this approach lends itself to richer parameterized and data-aware MAS settings beyond the state-of-the-art solutions in the literature.

Temporal logics over finite traces, such as LTLf and its extension LDLf, have been adopted in several areas, including Business Process Management (BPM), to check properties of processes whose executions have an unbounded, but finite, length. These logics express properties of single traces in isolation, however, especially in BPM it is also of interest to express properties over the entire log, i.e., properties that relate multiple traces of the log at once. In the case of infinite-traces, HyperLTL has been proposed to express these ``hyper'' properties. In this paper, motivated by BPM, we introduce HyperLDLf, a logic that extends LDLf with the hyper features of HyperLTL. We provide a sound, complete and computationally optimal technique, based on DFAs manipulation, for the model checking problem in the relevant case where the set of traces (i.e., the log) is a regular language. We illustrate how this form of model checking can be used for verifying log of business processes and for advanced forms of process mining.

Traditionally Business Process Modeling has only focused on the control-flow perspective, thus allowing process designers to specify the constraints on the activities of the process: the order and potential concurrency of their execution, their mutual exclusivity, the possibility of being repeated, etc. However, activities are executed by different resources, manipulate data objects and are constrained by the state of such objects. This requires that the traditional notion of soundness, typically introduced for control-flow-only models, is extended so as to consider data. Intuitively, a (data-aware) process model is sound if (1) it does not contain deadlocks, (2) no more activities are enabled when the process instance is marked as completed and finally (3) there are no parts of the model that cannot be executed. Although several data-aware notations have been introduced in the literature, not all of these are given a formal semantics. In this paper, we propose a technique for checking the data-aware soundness for a specific class of such integrated models, with a simple syntax and semantics, building on Data Petri Nets (DPNs). These are Petri nets enriched with case variables, where transitions are guarded by formulas that inspect and update such variables, and are of the form variable-operator-variable or variable-operator-constant. Even though DPNs are less expressive than Petri nets where data are carried by tokens, they elegantly capture business processes operating over simple case data, allowing to model complex data-aware decisions. We show that, if a DPN is data-aware sound, the Constraint Graph is a finite-state automaton; however, a finite-state Constraint Graph does not guarantee data-aware soundness, but provides a finite structure through which this property can be checked. Finally, we investigate further properties beyond data-aware soundness, such as the problem of verifying that an actor participating in the business process can unilaterally enforce data-aware soundness by restricting the possible executions of a bounded DPN, assuming this actor to be able to control the firing of some transitions and decide the value of some of the case variables whenever these are updated.

This special issue contains revised and extended versions of papers presented at the 33rd Italian Conference on Computational Logic -CILC 2018 -which was held in Bolzano, Italy, on September 20-22, 2018.CILC is the annual conference organized by GULP (Group of researchers and Users of Logic Programming).Since the first event of the series, which took place in Genoa in 1986, the annual GULP conference represents the main opportunity for Italian users, researchers and developers working in the field of computational logic to meet and exchange ideas.Over the years the conference broadened its horizons from the specific field of logic programming to include declarative programming and applications in neighboring areas such as artificial intelligence and deductive databases.The authors of selected papers were invited to submit an improved, extended version to this special issue of Fundamenta Informaticae.Those papers went through a careful review by qualified international referees.The three papers in the special issue witness the multifaceted nature of CILC, covering important topics in temporal databases, description logics, and formal verification:

The integrated modeling and analysis of dynamic systems and the data they manipulate has been long advocated, on the one hand, to understand how data and corresponding decisions affect the system execution, and on the other hand to capture how actions occurring in the systems operate over data. KR techniques proved successful in handling a variety of tasks over such integrated models, ranging from verification to online monitoring. In this paper, we consider a simple, yet relevant model for data-aware dynamic systems (DDSs), consisting of a finite-state control structure defining the executability of actions that manipulate a finite set of variables with an infinite domain. On top of this model, we consider a data-aware version of reactive synthesis, where execution strategies are built by guaranteeing the satisfaction of a desired linear temporal property that simultaneously accounts for the system dynamics and data evolution.

There has recently been increasing interest in using reactive synthesis techniques to automate the production of manufacturing process plans. Previous work has assumed that the set of manufacturing resources is known and fixed in advance. In this paper, we consider the more general problem of whether a controller can be synthesized given sufficient resources. In the unbounded setting, only the types of available manufacturing resources are given, and we want to know whether it is possible to manufacture a product using only resources of those type(s), and, if so, how many resources of each type are needed. We model manufacturing processes and facilities as transducers (automata with output), and show that the unbounded orchestration problem is decidable and the (Pareto) optimal set of resources necessary to manufacture a product is computable for uni-transducers. However, for multitransducers, the problem is undecidable.

In this paper, we model manufacturing processes and facilities as transducers (automata with output). The problem of whether a given manufacturing process can be realized by a given set of manufacturing resources can then be stated as an orchestration problem for transducers. We first consider the conceptually simpler case of uni-transducers (transducers with a single input and a single output port), and show that synthesizing orchestrations for uni-transducers is EXPTIME-complete. Surprisingly, the complexity remains the same for the more expressive multi-transducer case, where transducers have multiple input and output ports and the orchestration is in charge of dynamically connecting ports during execution.

Determining the most appropriate means of producing a given product, i.e., which manufacturing and assembly tasks need to be performed in which order and how, is termed process planning. In process planning, abstract manufacturing tasks in a process recipe are matched to available manufacturing resources, e.g., CNC machines and robots, to give an executable process plan. A process plan controller then delegates each operation in the plan to specific manufacturing resources. In this paper we present an approach to the automated computation of process plans and process plan controllers. We extend previous work to support both non-deterministic (i.e., partially controllable) resources, and to allow operations to be performed in parallel on the same part. We show how implicit fairness assumptions can be captured in this setting, and how this impacts the definition of process plans.

Proper epistemic knowledge bases (PEKBs) are syntactic knowledge bases that use multi-agent epistemic logic to represent nested multi-agent knowledge and belief. PEKBs have certain syntactic restrictions that lead to desirable computational properties; primarily, a PEKB is a conjunction of modal literals, and therefore contains no disjunction. Sound entailment can be checked in polynomial time, and is complete for a large set of arbitrary formulae in logics Kn and KDn. In this paper, we extend PEKBs to deal with a restricted form of disjunction: 'knowing whether.' An agent i knows whether Q iff agent i knows Q or knows not Q; that is, []Q or []not(Q). In our experience, the ability to represent that an agent knows whether something holds is useful in many multi-agent domains. We represent knowing whether with a modal operator, and present sound polynomial-time entailment algorithms on PEKBs with the knowing whether operator in Kn and KDn, but which are complete for a smaller class of queries than standard PEKBs.

There is a rising demand for customised products with a high degree of complexity. To meet these demands, manufacturing lines are increasingly becoming autonomous, networked, and intelligent, with production lines being virtualised into a manufacturing cloud, and advertised either internally to a company, or externally in a public cloud. In this paper, we present a novel approach to two key problems in such future manufacturing systems: the realisability problem (whether a product can be manufactured by a set of manufacturing resources) and the control problem (how a particular product should be manufactured). We show how both production recipes specifying the steps necessary to manufacture a particular product, and manufacturing resources and their topology can be formalised as labelled transition systems, and define a novel simulation relation which captures what it means for a recipe to be realisable on a production topology. We show how a controller that can orchestrate the resources in order to manufacture the product on the topology can be extracted from the simulation relation, and give an algorithm to compute a simulation relation and a controller.

Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology. Our approach represents an important first step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.

Description logic Knowledge and Action Bases (KAB) are a mechanism for providing both a semantically rich representation of the information on the domain of interest in terms of a description logic knowledge base and actions to change such information over time, possibly introducing new objects. We resort to a variant of DL-Lite where the unique name assumption is not enforced and where equality between objects may be asserted and inferred. Actions are specified as sets of conditional effects, where conditions are based on epistemic queries over the knowledge base (TBox and ABox), and effects are expressed in terms of new ABoxes. In this setting, we address verification of temporal properties expressed in a variant of first-order mu-calculus with quantification across states. Notably, we show decidability of verification, under a suitable restriction inspired by the notion of weak acyclicity in data exchange.

In this paper, we review a series of agent behavior synthesis problems under full observability and nondeterminism (partial controllability), ranging from conditional planning, to recently introduced agent planning programs, and to sophisticated forms of agent behavior compositions, and show that all of them can be solved by model checking two-player game structures. These structures are akin to transition systems/Kripke structures, usually adopted in model checking, except that they distinguish (and hence allow to separately quantify) between the actions/moves of two antagonistic players. We show that using them we can implement solvers for several agent behavior synthesis problems.