Abstract In many logical rule-based systems, it often happens that opposite statements can be concluded, hence superiorities (or priorities) among rules are needed to solve such conflicts. Since the specific circumstances describing the scenario at hand determine which rules may apply, we thus need a mechanism able to compute such superiorities dynamically, case-by-case. We formalise a non-monotonic logic able to dynamically compute superiorities over rules. Finally, we advance efficient algorithms to compute the (positive and negative) conclusions of such theories, and prove their computational properties.

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.

Legal documents often contain references to either other documents, or other parts (of the same document). The use of references is meant to reduce the complexity of the documents; however, they pose serious concerns for the formal (logical) representation of the norms stipulated in the document itself. We propose an approach to directly model the references in a logic language and to resolve them during the computation of the legal effects in force in a case. The approach is proved to be computationally feasible and to have an efficient algorithmic implementation.

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)

Organisations have to cope with large numbers of business rules and existing regulations governing the business in which they operate. Such rules are difficult to maintain due to their size and complexity, and it is increasingly challenging to ensure that each business process adheres to those rules. As such, automated extraction of business processes from rules has three clear advantages: (1) visualisation of all possible executions allowed by the rules, (2) automated execution and compliance by design, (3) identification of “inefficiencies” in the business rules. Existing approaches, however, only allow for the generation of partial traces based on input specifications and cannot handle many different input cases resulting in a full process. This paper presents a formal method to visualise and operationalise such sets of rules as a verifiable business process that is compliant by design, which allows us to analyse all possible execution paths. Additionally, we formally prove correctness of the business processes generated by our method. The approach is implemented in a tool and evaluated on both performance and correctness, showing that even for highly complex sets of rules the approach performs well and outperforms a well-known state-of-the-art approach. Evaluation on a real-life process shows the feasibility of the presented approach.

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.

Organisations typically have to cope with large numbers of business rules and existing regulations governing the business in which they operate. Due to the size and complexity of those rules, maintenance is difficult and it is increasingly complicated to ensure that each business process adheres to those rules. As such, automated extraction of business processes from rules has a number of clear advantages: (1) visualisation of all possible executions allowed by the rules, (2) automated execution and compliance by design, (3) identification of "inefficiencies" in the business rules. Existing approaches, however, only allow to generate partial traces based on input specifications and cannot handle many different input cases resulting in a full process. This paper presents a formal method to visualise and operationalise such sets of rules as a verifiable business process that is compliant by design and allows us to analyse all possible execution paths. In addition, it maintains information of all distinct input cases, to preserve dependencies between consecutive exclusive paths.

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.

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 strategic argumentation players exchange arguments to prove or reject a claim. This paper discusses and reports on research about two basic issues regarding the game-theoretic understanding of strategic argumentation games in the law: whether such games can be reasonably modelled as zero-sum games and as games with complete information.

This paper systematically investigates how to model legal contraction in an expressive variant of Defeasible Deontic Logic. We argue that legal contraction is an umbrella concept that includes operations which are conceptually and technically different: removing rules, adding exceptions, and modifying rule priorities. The peculiarities of deleting legal conclusions show that an extension of those operations is sometimes needed, which works on the indirect conclusions from which the target effect of the contraction is obtained. The proposed techniques are discussed in the context of a new version for the logic of AGM postulates of contraction.

We propose a framework for reconstructing the arguments supporting the restrictive interpretations of legal provisions. The idea is that the interpretation of legal concepts may require to change the counts-as rules defining them. Some connections with revision theory techniques are considered.

In this paper we propose an extension of Defeasible Logic to represent different concepts of defeasible permission. Special attention is paid in particular to permissive norms that work as exceptions to opposite obligations.