Business Process Compliance is a family of methods to evaluate Business Processes in terms of the existence of one execution (one trace) that does not violate constraints superimposed on the process itself. The dual version is formulated as the superimposition of a set of constraints and consequent evaluation of the process for all the executions . These problems are relevant to a large part of actual applications, especially those in the context of regulatory compliance where we aim at verifying the process against a normative background (including, for instance, soft ones, such as guidelines, product specification, and product standards) or goals fixed by the owner of the process. In this paper we discuss one new type of compliance, that is impact compliance , devised to verify when a process respects a set of constraints, to establish that certain amounts, measuring the undesired effects of the tasks executed to implement the process, are below given limits . In the current literature on Business Process Management, Business Process Analysis, and Business Process Compliance, this type of compliance checking process has not yet been addressed. As we demonstrate in this paper, this problem is significant and complex to address. In particular, we show that the checking problems described above are, under certain structural conditions, polynomially solvable on deterministic machines. In general, however, the first problem is NP-complete whilst the second is polynomially solvable on deterministic machines.

Abstract In the setting of Defeasible Logic, we deal with the problem of revising and contracting a non-monotonic theory while minimizing the number of rules to be removed from the theory itself. The process is based on the notions of a set of rules being necessary and sufficient in order to prove a claim. The substantial difference among classical and non-monotonic reasoning processes makes this issue significant in order to achieve the correct revision processes. We show that the process is however computationally hard, and can be solved in polynomial time on non-deterministic machines.

The problem of positioning cameras for optimal monitoring of a given surface, while given a budget, is quite evidently hard. Moreover, it is very difficult to collect information that could reduce the span of admissible solutions and therefore downsize the problem itself. In this paper, as referred to a specific case in a critical infrastructure, the harbour of the Ventotene Island in southern Italy, how the above mentioned problem can be solved in an effective way by employing a Genetic algorithm. The approach is also generalised in order to solve similar problems.

We present the architecture of Houdini-2.0, a reasoning system that computes the extension of a defeasible deontic theory given as input, the process of computing the consequences of the rules expressed in the theory itself. The decision process is a sceptical, non-monotonic, and it allows us to determine which prescriptive behaviours are in force (obligations, permissions, prohibitions) along with propositional ones. The system is based on pre-existing algorithmic solutions, and it is implemented as an online platform to deploy the results of a computation in several use cases, including those that pertain legal domain.

Modelling the concept of explanation is a central matter in AI systems, as it provides methods for developing eXplainable AI (XAI). When explanation applies to normative reasoning, XAI aims at promoting normative trust in the decisions of AI systems: in fact, such a trust depends on understanding whether systems predictions correspond to legally compliant scenarios. This paper extends to normative reasoning a work by Governatori et al. (2022) on the notion of stable explanations in a non-monotonic setting: when an explanation is stable, it can be used to infer the same normative conclusion independently of other facts that are found afterwards.

The notion of Business Process Compliance has been widely discussed as one of the most important issues to be solved when comparing the description of a business process against a normative background. Many changes have been provided to the basic notion above. The introduction of changes to the normative background has been considered, as well as the idea of Compliance by Design. In this paper, we discuss how to devise a business process that is compliant to an impact constraint set. We shall show that the technical problems determined by this concept are all within the horizon of adding to the logical framework the notions of Resource and Product.

This paper develops a new comprehensive computational framework for reasoning about private international law that encompasses the reasoning patterns modeled by previous works [3,8,9]. The framework is a multi-modal extension of [10] preserving some nice properties of the original system, including some efficient algorithms to compute the extensions of normative theories representing legal systems.

There is a growing attention, in the research communities of political economics, onto the potential of text analytics in classifying documents with economic content. This interest extends the data analytics approach that has been the traditional base for economic theory with scientific perspective. To devise a general method for prediction applicability, we identify some phases of a methodology and perform tests on a large well-structured repository of resource contracts containing documents related to resources. The majority of these contracts involve mining resources. In this paper we prove that, by the usage of text analytics measures, we can cluster these documents on three indicators: fairness of the contract content, transparency of the document themselves, and applicability of the clauses of the contract intended to guarantee execution on an international basis. We achieve these results, consistent with a gold-standard test obtained with human experts, using text similarity b (More)

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In this paper we discuss a transition model from commonly adopted models of data gathering, transfer and management for environmental monitoring towards more sophisticated ones based on Artificial Intelligence and IoT. The transition model is based on the paradigm of multiple agent systems. The adoption of this transition model is motivated by the need to improve effectiveness, efficiency and interoperability of environmental monitoring by simultaneously guaranteeing its sustainability in economic terms.

Graph-based network modeling is becoming increasingly pervasive touching very different fields. Among these are social networks analysis and brain connectivity modeling. Though apparently very far apart, these two domains share the same questions about how the underlying network is structured and h ow this can be measured. This determines an a-priori unexpected convergence of the research efforts of two different communities, that is neurosciences and information technology. In this work, we put forth some basic issues emerging from the overlaps of the two domains and propose a first simple measure allowing to capture one among the features of interest: the transtopic closeness centrality. To this end, the related concepts are briefly recalled and two case studies are considered. Then, relying on social network analysis principles, the transposition to functional brain networks is proposed highlighting and discussing some of the inherent critical issues.

Lexical classification is one of the most widely investigated fields in (computational) linguistic and Natural language Processing. Adjectives play a significant role both in classification tasks and in applications as sentiment analysis. In this paper a simple algorithm for lexical classification of comparable adjectives, called MORE (coMparable fORm dEtector), is proposed. The algorithm is efficient in time. The method is a specific unsupervised learning technique. Results are verified against a reference standard built from 80 manually annotated lists of adjective. The algorithm exhibits an accuracy of 76%.

Meteorological forecasting is the process of providing reliable prediction about the future weathear within a given interval of time. Forecasters adopt a model of reasoning that can be mapped onto an integrated conceptual framework. A forecaster essentially precesses data in advance by using some models of machine learning to extract macroscopic tendencies such as air movements, pressure, temperature, and humidity differentials measured in ways that depend upon the model, but fundamentally, as gradients. Limit values are employed to transform these tendencies in fuzzy values, and then compared to each other in order to extract indicators, and then evaluate these indicators by means of priorities based upon distance in fuzzy values. We formalise the method proposed above in a workflow of evaluation steps, and propose an architecture that implements the reasoning techniques.

We present the new methodology ONTO-PLC to deliver software programs on system-on-chip or single-board computers used to control industrial plants, as substitutes for programmable logic control technologies. The methodology is ontology-driven based on the abstract description of the plant at a level in which the plant itself is viewed as a set of instruments, each instrument being a set of machineries coordinated in functional terms by a control system, formed by sensors and actuators, under the control of an abstract model of behavior delivered by means of an extended finite state machine.

Normative systems accommodate temporary norms of several types, which can also be modified in different, and codified ways. In this paper we address the problem of modifying temporary norms that are represented by means of the combination of two known formalisms in the current literature. The framework evolves from a known one, which provides a system of norms at two distinct layers, and represents changes at the two layers as means to provide room for the codified change types. This results in four novel operators that anticipate and extend norms in two different combined ways, by preserving or not the effects of the norms in the period of time generated by the temporal modifications. We study these new operators and show how they relate to the operators of annulment and abrogation analysed elsewhere.

Conflicting rules and rules with exceptions are very common in natural language specification to describe the behaviour of devices operating in a real-world context. This is common exactly because those specifications are processed by humans, and humans apply common sense and strategic reasoning about those rules. In this paper, we deal with the challenge of providing, step by step, a model of energy saving rule specification and processing methods that are used to reduce the consumptions of a system of devices. We argue that a very promising non-monotonic approach to such a problem can lie upon Defeasible Logic. Starting with rules specified at an abstract level, but compatibly with the natural aspects of such a specification (including temporal and power absorption constraints), we provide a formalism that generates the extension of a basic defeasible logic, which corresponds to turned on or off devices.

Social Network Analysis is employed widely as a means to compute the probability that a given message flows through a social network. This approach is mainly grounded upon the correct usage of three basic graph-theoretic measures: degree centrality, closeness centrality and betweeness centrality. We show that, in general, those indices are not adapt to foresee the flow of a given message, that depends upon indices based on the sharing of interests and the trust about depth in knowledge of a topic. We provide an extended model, that is a simplified version of a more general model already documented in the literature, the Semantic Social Network Analysis, and show that by means of this model it is possible to exceed the drawbacks of general indices discussed above.

Abstract The paper proposes a fresh look at the concept of goal and advances that motivational attitudes like desire, goal and intention are just facets of the broader notion of (acceptable) outcome. We propose to encode the preferences of an agent as sequences of “alternative acceptable outcomes”. We then study how the agent's beliefs and norms can be used to filter the mental attitudes out of the sequences of alternative acceptable outcomes. Finally, we formalise such intuitions in a novel Modal Defeasible Logic and we prove that the resulting formalisation is computationally feasible.

Energy saving is one of the most challenging aspects of modern ambient intelligence technologies, for both domestic and business usages. In this paper we show how to combine Ambient Intelligence and Artificial Intelligence techniques to solve the problem of scheduling a set of devices under a given set of constraints, like limits to the maximal energy usage (Energy Span) and maximal energy absorption (Energy Peak). We provide a method that can be used to schedule the usage of devices in a given environment in a way that respects the input constraints. We adapt an existent approach to scheduling for Ambient Intelligence to a specific framework and exhibit a sample usage for a real life system, Elettra, that is in use in an industrial context.

We study the complexity of the Strategic Argumentation Problem for 2-player dialogue games where a player should decide what move (set of rules) to play at each turn in order to prove (disprove) a given thesis. We show that this is an NP-complete problem.

In many real-life situations making a decision entails evaluating the risks associated with the decision, which in turn requires reasoning about events and their relations. In addition to the simpler and better-understood notions of causation and precondition, in this paper we focus on block (or prevention), which is the relation established between an event φ1and another event φ2such that the number of occurrences of φ2decreases whenever φ1occurs, and mitigation, where the occurrence of φ1reduces the “negative” (for the particular decision we are considering) consequences of the occurrence of φ2. By introducing two further counting operators and the notion of interval of observation, we give here a sound and complete tableau system along with a systematic tableau construction procedure.

Several recent investigations in Artificial Intelligence and Law have dealt with the problem of "contract clause negotiation", often seen as a specific type of "meaning negotiation". Though a consistent effort has been spent in modelling situations in which two agents mediate the rules to govern a cooperation stipulated in a contract, there is still a lack of formalisation for such a task from a logical viewpoint, and specifically, no model exists, to the best of our knowledge, in the current literature, that represents the negotiation process directly using techniques of the Theory of zero-sum Games, although the majority of scholars admit that the behaviour of agents negotiating in contract definition are quite well modelled by that approach. In particular, we propose to model peer-to-peer meaning negotiation process by a zerosum game, known in Game Theory literature as Bargaining. This approach shows its usefulness in the development of a methodology for obtaining shared theories from distinct ones, and we apply it directly in a framework in which it is possible to represent Contract Clause Negotiation processes.

In the current literature of knowledge management and artificial intelligence, several different approaches to the problem have been carried out of developing domain ontologies from scratch. All these approaches deal fundamentally with three problems: (1) providing a collection of general terms describing classes and relations to be employed in the description of the domain itself; (2) organizing the terms into a taxonomy of the classes by the ISA relation; and (3) expressing in an explicit way the constraints that make the ISA pairs meaningful. Though a number of such approaches can be found, no systematic analysis of them exists which can be used to understand the inspiring motivation, the applicability context, and the structure of the approaches. In this paper, we provide a framework for analyzing the existing methodologies that compares them to a set of general criteria. In particular, we obtain a classification based upon the direction of ontology construction; bottom-up are those methodologies that start with some descriptions of the domain and obtain a classification, while top-down ones start with an abstract view of the domain itself, which is given a priori. The resulting classification is useful not only for theoretical purposes but also in the practice of deployment of ontologies in Information Systems, since it provides a framework for choosing the right methodology to be applied in the specific context, depending also on the needs of the application itself.

In the recent literature of Artificial Intelligence, an intensive research effort has been spent, for various algebras of qualitative relations used in the representation of temporal and spatial knowledge, on the problem of classifying the computational complexity of reasoning problems for subsets of algebras. The main purpose of these researches is to describe a restricted set of maximal tractable subalgebras, ideally in an exhaustive fashion with respect to the hosting algebras. In this paper we introduce a novel algebra for reasoning about Spatial Congruence, show that the satisfiability problem in the spatial algebra MC-4 is NP-complete, and present a complete classification of tractability in the algebra, based on the individuation of three maximal tractable subclasses, one containing the basic relations. The three algebras are formed by 14, 10 and 9 relations out of 16 which form the full algebra.