Privacy policies often fall short of providing a comprehensive account of how personal data is used, thus failing to comply with GDPR requirements. By doing so, they hamper the users’ ability to make informed decisions about using services while ensuring that their data is used properly and fairly. This calls for automatic tools that can effectively identify potentially unlawful policies. Here we present a new corpus of Italian privacy policies, with clauses labelled by experts in data protection law, to indicate the level of comprehensiveness of information. We focus on the categories of data processed, classifying each clause as either sufficiently or insufficiently informative (“vague”). We perform 6 different classification and detection tasks, comparing the performance of BERT-based models and generative Large Language Models. Addressing multilingualism is crucial in the EU, whose 24 spoken languages are an integral part of its cultural heritage. Consequentely, we also perform cross-language experiments to evaluate whether a pre-existing English corpus or classifiers can be leveraged for Italian and, vice versa, whether our corpus is informative enough to generalize to other languages.

Causation plays a central role in the attribution of responsibility, especially in the legal domain, where complex causal scenarios frequently arise. Traditionally, legal reasoners have relied on the idea that a cause must be a necessary condition of its effect, which falls short in scenarios involving overdetermination, preemption, or omission, thereby failing to adequately identify causes-in-fact. In this paper, we present a novel analysis of selected legal cases, each exemplifying common causal dilemmas discussed in causal literature. We employ three different notions of cause in our analysis: abductive explanation (AXp), the NESS test (Necessary Element of a Sufficient Set) and actual cause. We express the three notions and some of their variants in a modal language for causal reasoning that we interpret on a rule-based semantics. We provide a model checking algorithm for our modal language relying on a reduction into TQBF as well as an implementation of the legal cases in our causal model checker to automatically verify “what is the cause of what” and what types of causes apply in each legal case. Our interdisciplinary approach highlights the usefulness of logic-based methods for legal analysis, offering a fully transparent model-checking toolbox that could potentially support legal reasoners in disentangling complex factual scenarios.

Unfair clause detection is an extremely useful AI application for consumer protection. Artificial intelligence has recently been successful in building systems capable to automatically detect unfair clauses in Terms of Service, and also to identify their unfairness categories. Since Large Language Models (LLMs) are nowadays bringing a revolution to the field of artificial intelligence, and in particular to natural language processing and understanding, in this paper we compare several different prompt strategies for LLMs with more traditional BERT-based fine-tuned models. Our extensive experimental evaluation aims to investigate whether it is worth using LLMs also for this challenging domain-specific task.

We propose to model cause-in-fact in legal cases through fresh argumentation-theoretic notions of explanation and support, meant to capture the set of arguments that contribute to making a conclusion justified. This novel argumentation-based approach to causality in law goes beyond the traditional idea of a cause as a necessary antecedent condition (the conditio-sine-qua-non idea), to handle concurrent causal processes leading to overdetermination and preemption. It also provides sound analyses of cases involving omission and ennoblement. Finally, by relying on defeasible argumentation it can capture causal inferences based on defeasible generalisations, which are very often used in judicial reasoning. Through the analysis of causal puzzles in legal cases, we illustrate the framework’s effectiveness in handling complex causal reasoning, and demonstrate its potential to support legal reasoners with structured and intuitive analysis.

Autonomous vehicles (AVs) must comply with regulatory frameworks to ensure road safety and predictability. Current proposed AV systems predominantly rely on machine learning models that lack explicit, computable representations of traffic laws, raising concerns about accountability and robustness in complex scenarios. This study proposes a novel pipeline that embeds formal logic rules in the autonomous agent to ensure legal compliance. To address the knowledge acquisition bottleneck, we propose using Large Language Models (LLMs), robust prompt engineering, and Logical English (LE) to translate traffic rules from natural language into a human-readable, executable rule-based framework. The pipeline includes an error correction phase to refine the process of extracting legal rules, which are then integrated into a simulation environment. Our approach successfully performed the translation of legal text into a structured, computable format, improving the transparency and interpretability of the high level decision making. The error correction phase improves rule accuracy, while simulations further validate rule compliance and performance in dynamic traffic scenarios.

Deploying Large Language Models (LLMs) for legal question-answering requires ensuring factual accuracy and logical coherence. Current evaluation metrics inadequately capture legal reasoning complexity, while expert assessments lack scalability. We propose a two-dimensional framework that independently measures Truthfulness and Reasoning Soundness in model outputs, applied to Italian asylum proceedings requiring evidence-based analysis. This dual-axis approach reveals critical issues—such as legally correct answers derived through unsound or hallucinatory reasoning—that standard metrics fail to detect. To enable large-scale application, we implement an automated LLM-as-a-Judge system with bias-mitigation techniques. Experimental results demonstrate strong correspondence between automated judgments and expert evaluations, confirming framework reliability. This work advances diagnostic methodology for assessing LLMs in legal domains, offering both theoretical insight and practical applicability toward more trustworthy and accountable legal AI systems.

Despite some improvements in compliance metrics after the implementation of the European General Data Protection Regulation (GDPR), privacy policies have become longer and more ambiguous. They often fail to fully meet GDPR requirements, thus leaving users without a reliable way to understand how their data is processed. We present a novel corpus composed by 30 privacy policies of online platforms and a new set of annotation guidelines, to assess the level of comprehensiveness of information. We focus on the processed categories of data, classifying each clause either as fully informative or as insufficiently informative. In our experimental evaluation, we perform 6 different classification and detection tasks, comparing BERT models and generative Large Language Models.

Computable legal contracts offer a formal structure and semantics that can help identify incompatibilities among clauses, such as clauses that will never be used or clauses whose simultaneous application is impossible. In this paper we study a methodology for spotting incompatibilities in contracts written in Stipula, a domain-specific language for legal contracts. Drawing on real case laws, we identify recurring incompatible code patterns and propose techniques for integrating their analysis in the Stipula toolchain.

Causality is vital for establishing legal liability, but traditional analyses often fail to address complex scenarios and conflate causation with legal responsibility. This paper presents a novel approach to cause-in-fact based on argumentation theory which also assesses whether a cause-in-fact lacks legal relevance.

Causal reasoning is a challenging topic not only in philosophy, science and in theories of human mind, but also in legal reasoning. Causality is indeed a key precondition for civil and criminal liability, in all cases dealing with the connection between human actions or omissions and harmful events. Only a partial overlap exists between natural causality (cause-in-fact) and legal causality: there are instances in which what appears to be a natural cause is not recognised as a legal one, as well as instances in which causality may be presumptively ascribed by the law in the absence of decisive evidence for natural causality. Legal policy considerations may explain these puzzling divergences, as we will discuss in the following. In this paper, we use argumentation schemes to provide simple and intuitive patterns for assessing causality in the legal domain. The analysis of these argument schemes will enable us to clarify some connections between natural and legal causation. Our schemes will include the necessary condition (but-for), overdetermination (NESS), preemption, presumptions based on the increase of risks or presumption based on statutory obligations, and interruption of causality due to unexpected events (Actus Novus). These approaches are tested on the basis of real legal cases in different domains.

This paper proposes a novel argumentation-based approach to combine legal-reasoning methods that each solve a subproblem of an overall legal problem. The methods can be of any nature (for instance, logical, case-based or probabilistic), as long as their input-output behaviour can be described at the metalevel with deductive or defeasible rules. The model is formulated in the ASPIC+ framework, to profit from its metatheory and explanation methods, and to allow for disagreement about how to solve a subproblem. The model is not meant to be directly implementable but to serve as a semantics for architectures and implementations.

Argument structure prediction aims to identify the relations between arguments or between parts of arguments. It is a crucial task in legal argument mining, where it could help identifying motivations behind judgments or even fallacies or inconsistencies. It is also a very challenging task, which is relatively underdeveloped compared to other argument mining tasks, owing to a number of reasons including a low availability of datasets and a high complexity of the reasoning involved. In this work, we address argumentative link prediction in decisions by Court of Justice of the European Union on fiscal state aid. We study how propositions are combined in higher-level structures and how the relations between propositions can be predicted by NLP models. To this end, we present a novel annotation scheme and use it to extend a dataset from literature with an additional annotation layer. We use our new dataset to run an empirical study, where we compare two architectures and explore different combinations of hyperparameters and training regimes. Our results indicate that an ensemble of residual networks yields the best results.

Although permissions are of crucial importance in several settings, they have garnered less attention within the deontic logic community than obligations. In previous work we showed how to reconstruct deontic logic using Kelsen’s quasi-causal conception of norms, restricting ourselves to the notion of obligation. Here we extend the account to permission, and show how to analyse the notion of strong permission through a Kelsenian lens. In our framework various forms of conflicts between obligation and permission are disentangled.

This article continues the research initiated in [1,2], which established a connection between Boolean classifiers and legal case-based reasoning. We relax the assumption that case bases are such that all situations have been decided in favour of the defendant or the plaintiff and we introduce an inductive strategy for assigning plausible outcomes to undecided cases. Using counterfactual reasoning, we propose a method to determine whether, at each step of the induction, a feature is a factor, i.e., it consistently favours a single outcome, or is irrelevant, i.e., it is does not favour any outcome, or is ambiguous, i.e., it favours opposite outcomes.

Abstract In this paper, we provide a formal framework for modeling the burden of persuasion in legal reasoning. The framework is based on abstract argumentation, a frequently studied method of non-monotonic reasoning, and can be applied to different argumentation semantics; it supports burdens of persuasion with arbitrary many levels, and allows for the placement of a burden of persuasion on any subset of an argumentation framework’s arguments. Our framework can be considered an extension of related works that raise questions on how burdens of persuasion should be handled in some conflict scenarios that can be modeled with abstract argumentation. An open source software implementation of the introduced formal notions is available as an extension of an argumentation reasoning library. A theoretical analysis shows that our approach can be generalized to a novel method for the preference-based selection of extensions from argumentation frameworks.

We present Stipula, a domain specific language that may assist legal practitioners in programming legal contracts through specific patterns. The language is based on a small set of programming abstractions that correspond to common patterns in legal contracts. We illustrate the language by means of two paradigmatic legal contracts: a bike rental and a bet contract. Stipula comes with a formal semantics, an observational equivalence and a type inference system, that provide for a clear account of the contracts' behaviour and illustrate how several concepts from concurrency theory can be adapted to automatically verify the properties and the correctness of software-based legal contracts. We also discuss a prototype centralized implementation of Stipula.

In this paper we formalise a meta-argumentation framework as an ASPIC+ extension which enables reasoning about conflicts between formulae of the argumentation language. The result is a standard abstract argumentation framework that can be evaluated via grounded semantics.

This study aims at predicting the outcomes of legal cases based on the textual content of judicial decisions. We present a new corpus of Italian documents, consisting of 226 annotated decisions on Value Added Tax by Regional Tax law commissions. We address the task of predicting whether a request is upheld or rejected in the final decision. We employ traditional classifiers and NLP methods to assess which parts of the decision are more informative for the task.

This paper brings together factor-based models of case-based reasoning (CBR) and the logical specification of classifiers. Horty [8] has developed the factor-based models of precedent into a theory of precedential constraint. In this paper we combine binary-input classifier logic (BCL) to classifiers and their explanations given by Liu & Lorini [13, 14] with Horty’s account of factor-based CBR, since both a classifier and CBR map sets of features to decisions or classifications. We reformulate case bases in the language of BCL, and give several representation results. Furthermore, we show how notions of CBR can be analyzed by notions of classifier explanation.

The increase in computational and communication power of pervasive devices is also enabling to embed increasing intelligence in devices, there included the capability to act in autonomy, and possibly interacting with each other, in order to achieve specific goals, thus leaving humans out of the decision loop. In this context, the goal of the panel is thus reasoning about the possible implications (technical, ethical, and lega) of assigning great and often critical decision power to pervasive autonomous systems.

This short paper reports the summary of the panel held in the context of the PerCom 2022 conference.

Abstract In this paper we present the computational model of Arg2P, a logic-based argumentation framework for defeasible reasoning and agent conversation particularly suitable for explaining agent intelligent behaviours. The model is reified as the Arg2P technology, which is presented and discussed both from an architectural and a technological perspective so as to point out its potential in the engineering of intelligent systems. Finally, an illustrative application scenario is discussed in the domain of computable law for autonomous vehicles.

Abstract We present a formal semantics for deontic logic based on the concept of ceteris paribus preferences. We introduce notions of unconditional obligation and permission as well as conditional obligation and permission that are interpreted relative to this semantics. We show that these notions satisfy some intuitive properties and, at the same time, do not encounter some problems and paradoxes that have been extensively discussed in the deontic logic literature. We prove that the satisfiability problem for our logic is in NP. Finally, we show that the fragment of our logic in which the content of a deontic operator is a literal has an equivalent representation based on conditional preference networks (CP-nets).

In this paper we provide an account of the burden of persuasion in the context of structured argumentation. A formal model for the burden of persuasion is defined, discussed, and used to capture the role of the burden of persuasion in adjudicating conflicts between conflicting arguments and in determining the dialectical status of arguments. We consider how our model can also capture adversarial burdens of proof, namely, those cases in which failure to establish an argument for a proposition burdened with persuasion entails establishing the complementary proposition.

We propose an additive model for balancing the impacts of actions on values, where factors intensify or attenuate impacts on values, and values are assigned degrees of relative importance (weights). The balancing model induces axiological rules, consisting in prohibitions or permissions that are justified according to the impacts of the prohibited or permitted action on the values at stake. We also propose eight different revision operators, which shift the balance - and thus induce different norms - by expanding or contracting either the set of factors or the set of values. We provide the construction and prove some success properties of those operators.

Inspired by Kelsen’s view that norms establish causal-like connections between facts and sanctions, we develop a deontic logic in which a proposition is obligatory iff its complement causes a violation. We provide a logic for normative causality, define non-contextual and contextual notions of illicit and duty, and show that the logic of such duties is well-behaved and solves the main deontic paradoxes.

We present a study aimed at testing the CLAUDETTE system’s ability to generalise the concept of unfairness in consumer contracts across diverse market sectors. The data set includes 142 terms of services grouped in five sub-sets: travel and accommodation, games and entertainment, finance and payments, health and well-being, and the more general others. Preliminary results show that the classifier has satisfying performance on all the sectors.

We present a logical analysis of influence and control over the actions of others, and address consequential causal and normative responsibilities. We first account for the way in which influence can be exercised over the behaviour of autonomous agents. On this basis we determine the conditions under which influence leads to control on the implementation of positive and negative values. We finally define notions of causal and normative responsibility for the action of others. Our logical framework is based on STIT logic and is complemented with a series of examples illustrating the application. Our analysis applies to interactions between humans as well as to those involving autonomous artificial agents.

Private international law (PIL) addresses overlaps and conflicts between legal systems by distributing cases between the authorities of such systems (jurisdiction) and establishing what rules these authorities have to apply to each case(choice of law). A modular argumentation tool, Arg-tuProlog, is here presented that enables reasoning with rules and interpretations of multiple legal systems.

This work provides a formal model for the burden of persuasion in legal proceedings. The model shows how the allocation of the burden of persuasion may induce a satisfactory outcome in contexts in which the assessment of conflicting arguments would, without such an allocation, remain undecided. The proposed model is based on an argumentation setting in which arguments may be accepted or rejected according to whether the burden of persuasion falls on the conclusion of such arguments or on its complements. Our model merges two ideas that have emerged in the debate on the burden of persuasion: the idea that allocation of the burden of persuasion makes it possible to resolve conflicts between arguments, and the idea that its satisfaction depends on the dialectical statuses of the arguments involved. Our model also addresses cases in which the burden of persuasion is inverted, and cases in which burdens of persuasion are inferred through arguments.

As Autonomous vehicles (AVs) are entering shared roads, the challenge of designing and implementing a completely autonomous vehicle is still open. Aside from technological issues regarding how to manage the complexity of the environment, AVs raise difficult legal issues and ethical dilemmas, especially in unavoidable accident scenarios. In this context, a vast speculation depicting moral dilemmas has developed in recent years. A new perspective was proposed: an “Ethical Knob” (EK), enabling passengers to ethically customise their AVs, namely, to choose between different settings corresponding to different moral approaches or principles. In this contribution we explore how an AV can automatically learn to determine the value of its “Ethical Knob” in order to achieve a trade-off between the ethical preferences of passengers and social values, learning from experienced instances of collision. To this end, we propose a novel approach based on a genetic algorithm to optimize a population of neural networks. We report a detailed description of simulation experiments as well as possible applications.

Recent years have been tainted by market practices that continuously expose us, as consumers, to new risks and threats. We have become accustomed, and sometimes even resigned, to businesses monitoring our activities, examining our data, and even meddling with our choices. Artificial Intelligence (AI) is often depicted as a weapon in the hands of businesses and blamed for allowing this to happen. In this paper, we envision a paradigm shift, where AI technologies are brought to the side of consumers and their organizations, with the aim of building an efficient and effective counter-power. AI-powered tools can support a massive-scale automated analysis of textual and audiovisual data, as well as code, for the benefit of consumers and their organizations. This in turn can lead to a better oversight of business activities, help consumers exercise their rights, and enable the civil society to mitigate information overload. We discuss the societal, political, and technological challenges that stand before that vision.

In many legal disputes, determining and evaluating cause-in-fact is a crucial step in the liability attribution. It is, however, difficult and opaque. In this paper, we analyse the cases of overdetermination, where there is more than one cause for the outcome. The proposed framework (FCA) employs logic-based argument modelling. It distinguishes individual contributors in overdetermination cases by using a new set of critical questions based on argument schemes from effect-to-cause. To illustrate the use of the FCA, the Heneghan v Manchester Dry Docks lung cancer case with multi-party contributions is analysed.

The research aims at a formal definition of constructive interpretation in law as the dynamic of revision of theories about the normative system, embedding a model of balancing values [13] into an architecture of i/o logics representing conceptual, deontological and axiological rules [11]. We also introduce new revision operators which are relevant in the context of value assessments.

Different formalisms for defeasible reasoning have been used to represent legal knowledge and to reason with it. In this work, we provide an overview of the following logic-based approaches to defeasible reasoning: Defeasible Logic, Answer Set Programming, ABA+, ASPIC+, and DeLP. We compare features of these approaches from three perspectives: the logical model (knowledge representation), the method (computational mechanisms), and the technology (available software). On this basis, we identify and apply criteria for assessing their suitability for legal applications. We discuss the different approaches through a legal running example.

Consumer contracts often contain unfair clauses, in apparent violation of the relevant legislation.In this paper we present a new methodology for evaluating such clauses in online Terms of Services.We expand a set of tagged documents (terms of service), with a structured corpus where unfair clauses are liked to a knowledge base of rationales for unfairness, and experiment with machine learning methods on this expanded training set.Our experimental study is based on deep neural networks that aim to combine learning and reasoning tasks, one major example being Memory Networks.Preliminary results show that this approach may not only provide reasons and explanations to the user, but also enhance the automated detection of unfair clauses.

Artificial Intelligence and Law is undergoing a critical transformation. Traditionally focused on the development of expert systems and on a scholarly effort to develop theories and methods for knowledge representation and reasoning in the legal domain, this discipline is now adapting to a sudden change of scenery. No longer confined to the walls of academia, it has welcomed new actors, such as businesses and companies, who are willing to play a major role and seize new opportunities offered by the same transformational impact that recent AI breakthroughs are having on many other areas. As it happens, commercial interests create new opportunities but they also represent a potential threat to consumers, as the balance of power seems increasingly determined by the availability of data. We believe that while this transformation is still in progress, time is ripe for the next frontier of this field of study, where a new shift of balance may be enabled by tools and services that can be of service not only to businesses but also to consumers and, more generally, the civil society. We call that frontier consumer-empowering AI.

Two years after its entry into force, the EU General Data Protection Regulation became applicable on the 25th May 2018. Despite the long time for preparation, privacy policies of online platforms and services still often fail to comply with information duties and the standard of lawfulness of data processing. In this paper we present a new methodology for processing privacy policies under GDPR's provisions, and a novel annotated corpus, to be used by machine learning systems to automatically check the compliance and adequacy of privacy policies. Preliminary results confirm the potential of the methodology.

Consumer contracts too often present clauses that are potentially unfair to the subscriber. We present an experimental study where machine learning is employed to automatically detect such potentially unfair clauses in online contracts. Results show that the proposed system could provide a valuable tool for lawyers and consumers alike.

This paper offers a new logical machinery for reasoning about interpretive canons. We identify some options for modelling reasoning about interpretations and show that interpretative argumentation has a distinctive structure where the claim that a legal text ought or may be interpreted in a certain way can be supported or attacked by arguments, whose conflicts may have to be assessed according to further arguments.

We present a logical analysis of the relation between social influence and responsibility. In particular, we precisely characterise a notion of influence-based responsibility, namely, a responsibility that depends on the fact that an agent causes a primary violation by another agent. This notion captures the core of the idea of indirect (also called secondary or accomplice) responsibility in legal systems, and can be useful in the governance of multiagent systems. Our analysis uses the STIT logic of action (the logic of seeing to it that). On this basis we shall first formalise a notion of influence between agents, and then the idea of influence-based responsibility.

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 shows how defeasible argumentation schemes can be used to represent the logical structure of the arguments used in statutory interpretation. In particular we shall address the eleven kinds of argument identified MacCormick and Summers [6] and the thirteen kinds of argument by Tarello [11]. We show that interpretative argumentation has a distinctive structure where the claim that a legal text ought or may be interpreted in a certain way can be supported or attacked by arguments, whose conflicts may have to be assessed according to further arguments.

This paper presents a set of argumentation schemes that can be used to identify, analyze and evaluate types of arguments characteristically used in cases of contested statutory interpretation in law. These schemes represent forms of argument already identified in the literature as leading forms of argument used in cases of statutory interpretation where legal disputes about how to interpret a statute have generally arisen.

We propose a model of vicarious reinforcement in rule-based learning agents. The influence of this reinforcement is investigated in a population where a law is enforced ex ante. The norm-governed population of learning agents is formalised and simulated in an executable probabilistic rule-based argumentation framework. Vicarious experiences are expressed with rules and their learning effects are integrated into reinforcement learning. So, agents learn not only from their own experiences but also by taking into account the experiences of others. We show that simulation results differ from traditional calculus based on expected utilities.

In this paper we present an application of argument maps for assessing liability in the field of Air Traffic Management (ATM), developed within the ALIAS (Addressing the Liability Impact of Automated Systems) project. Such maps are used for presenting legal concepts and norms to lawyers and non lawyers (engineers, software developers and other technical personnel), within the cooperative design and assessment of new technologies for ATM.

In most attempts to model legal systems as formal argumentation systems, legal norms are viewed as an argumentation's system inference rules. Since in formal argumentation systems inference rules are generally assumed to be fixed and independent from the inferences they enable, this approach fails to capture the dialectical connection between norms and arguments, where on the one hand legal arguments are based on norms, and on the other hand the validity of norms depends on arguments. The validity of a new norm can be supported by referring to authoritative sources, such as legislation or precedent, but also through interpretations of such sources, or through analogies or a contrario arguments based on existing authoritative norms. In this contribution arguments about norms are modelled as the application of argument schemes to knowledge bases of facts and norms.

There are occasions in which an agent lengthens its own action through the implementation of a foreign activity for its own interests. We focus on the occasional dependence relation between a principal agent and a helper agent. In particular, we are interested in the helper's harmful performance that has its origin in extra contractual situations e.g. factual and/or occasional situations based on trust or courtesy which may lead to the emergence of an obligation to compensate third parties.

This paper proposes to integrate an argumentation framework with techniques from Temporal Constraint Satisfaction. Temporal constraints are thus embedded into legal argumentation to account for temporal aspects of legal reasoning. Through the accommodation of temporal constraints, the validity of arguments and of their conclusions is made relative to the time-points when the applied norms are alive, according to the adopted temporal perspective. A fixed-point semantics and an associated dialogue game are given.

A formal model is proposed of argumentation with burdens and standards of proof, overcoming shortcomings of earlier work. The model is based on a distinction between default and inverted burdens of proof. This distinction is formalised by adapting the definition of defeat of the ASPIC+ framework for structured argumentation. Since ASPIC+ generates abstract argumentation frameworks, the model is thus given a Dungean semantics. It is shown to adequately capture shifting proof burdens as well as Carneades' definitions of proof standards.

We shall argue that burdens of proof are relevant also to monological reasoning, i.e., for deriving the conclusions of a knowledge-base allowing for conflicting arguments. Reasoning with burdens of proof can provide a useful extension of current argument-based non-monotonic logics, at least a different perspective on them. Firstly we shall provide an objective characterisation of burdens of proof, assuming that burdens concerns rule antecedents (literals in the body of rules), rather than agents. Secondly, we shall analyse the conditions for a burden to be satisfied, by considering credulous or skeptical derivability of the concerned antecedent or of its complement. Finally, we shall develop a method for developing inferences out of a knowledge base merging rules and proof burdens in the framework of defeasible logic.

We address some forms of temporal reasoning within normative MAS, focusing on the combination of temporal logics with multi-modal multi-agent logics. We suggest perspectives on how these combinations can be used for modelling aspects of time within lawful provisions, obligations, and legal principles. The main contributions are the new variant of deontic tense logic using hybrid logic, and the combination of time and obligations.

This contribution introduces a novel approach to study legal interactions, legal professions, and legal institutions, by combining argumentation, game theory and evolution. We consider a population of lawyers, having different postures, who engage in adversarial argumentation with other lawyers, obtaining outcomes according the existing context and their chosen strategies. We examine the resulting games and analyse the evolution of the population.

This paper proposes a novel approach to study legal interactions. In particular, we focus on cases of medical liability and investigate the mechanisms governing legal litigations in different judicial environments. To do that, we use an agent-based model where lawyers are explicitly and individually represented in the model. Lawyers in the model are heterogeneous in the sense that they may follow different argumentation strategies to try to win the cases of medical liability they are assigned. They may also change their strategy if they observe that other strategies work better in the particular context they are embedded. In this way, our agent-based model offers a complementary approach to understanding legal interactions within an evolutionary framework. More concretely, we explore how various factors, such as the magnitude of legal expenses and the accuracy of the judicial system, affect the type of litigation strategies that are successful and prevail in a certain judicial context.

This paper extends our previous logical analysis of presumptions and burden of proof by studying the force of a presumption once counterevidence has been offered. In the jurisprudential literature different accounts of this issue have been given: some have argued that a presumption is nullified by counterarguments while others have maintained that this gives presumptions a force that is too slight. We argue that these differences largely are not a matter of logic but of legal policy, and we show how the various accounts can be logically formalised.

This paper proposes some variants of Temporal Defeasible Logic (TDL) to reason about normative modifications. These variants make it possible to differentiate cases in which, for example, modifications at some time change legal rules but their conclusions persist afterwards from cases where also their conclusions are blocked.

This paper presents an argument-based logic for reasoning about allocations of the burden of persuasion. The logic extends the system of Prakken (2001), which in turn modified the system of Prakken & Sartor (1996) with the possibility to distribute the burden of proof over both sides in an argument game. First the (2001) system is put in the context of a distinction of three types of proof burdens and it is argued that the proof burdens of that system are in fact burdens of persuasion. Then the (2001) system is modified to allow for defeasible reasoning about allocations of such burdens within the logic. The usefulness of the resulting system is illustrated with applications to real legal cases.

This paper presents HARE, a rule-based system addressing a section of the Italian law, and in particular the taxes to be paid to start legal proceedings. HARE uses an Italian version of Softlaw's STATUTE Expert. We will then shortly describe the main aspects of STATUTE Expert approach and some recent developments of this system.

We propose a computationally oriented non-monotonic multi-modal logic arising from the combination of temporalised agency and temporalised normative positions. We argue about the defeasible nature of these notions and then we show how to represent and reason with them in the setting of Defeasible Logic.

In this paper we put forward a formal description of theories which can be used to record understanding of, and explain decisions in, case law domains. We believe that reasoning with cases involves all of theory construction, use and evaluation, and that awareness of the theory which provides a context for case based arguments is essential to understanding such arguments. Moreover, our account of these theories includes a systematic link between factors and values, which we believe is necessary to explain why some arguments prove to be more persuasive than others. We begin by formalising the various elements that the theories contain, and then provide a set of theory constructors which allow theories to built up from the background of decided cases. We show how such theories can be used to explain decisions on particular cases. We discuss how theories can be compared and evaluated. We then show how the argument moves of HYPO and CATO can be understood in terms of our framework. We conclude with a brief discussion of an implementation of the framework, and a summary of the major features of our approach.

Case-based reasoning in HYPO'For simplicity we will in this paper assume, unlike HYPO, that all factors are twovalued, i.e. either true or false.

This paper investigates how of legal language determine tack and defend argu rnents argumentation framework in the logical aspects possibilities to atin a dispute.An logic-programming style is presented which can cope with the hterplay between assumptions, applicability statements and reasoning about, preference relations between norms.The theory is given a solid semantic foundation, and can be used to formalise a wide range of realistic legal examples.

In many commonsense contexts only incoherent and conflicting information is available. In such contexts reasonable conclusions must be derived from inconsistent sets of premises. This is especially the case in legal reasoning: legal norms can be issued by different authorities, in different times, to reach incompatible socio-political objectives, and the meaning of those norms can be semantically indeterminate.

Article An application of the expert system shell Σkilagi to the study of benefits regulation of Bologna University Share on Authors: Giovanni Dallara Cirfid, Bologna University, Via Galliera 3/5, 40100, Bologna Cirfid, Bologna University, Via Galliera 3/5, 40100, BolognaView Profile , Carlo Gattei Cirfid, Bologna University Cirfid, Bologna UniversityView Profile , Giovanni Sartor Cirfid, Bologna University Cirfid, Bologna UniversityView Profile Authors Info & Claims ICAIL '91: Proceedings of the 3rd international conference on Artificial intelligence and lawMay 1991 Pages 86–91https://doi.org/10.1145/112646.112657Online:01 May 1991Publication History 0citation181DownloadsMetricsTotal Citations0Total Downloads181Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Article Free Access Share on The structure of norm conditions and nonmonotonic reasoning in law Author: Giovanni Sartor Cirfid, University of Bologna, Via Galliera 3/5, 40100, Bologna Cirfid, University of Bologna, Via Galliera 3/5, 40100, BolognaView Profile Authors Info & Claims ICAIL '91: Proceedings of the 3rd international conference on Artificial intelligence and lawMay 1991 Pages 155–164https://doi.org/10.1145/112646.112665Published:01 May 1991Publication History 24citation346DownloadsMetricsTotal Citations24Total Downloads346Last 12 Months41Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF