Формализация споров о законодательных инициативах в виде практического рассуждения

In this paper the ASPIC+ framework for argumentation-based inference is used for formally reconstructing two legal debates about law-making proposal: an opinion of a legal scholar on a Dutch legislative proposal and a US common-law judicial decision on whether an existing common law rule should be followed or distinguished. Both debates are formalized as practical reasoning, with versions of the argument schemes from good and bad consequences. These case studies aim to contribute to an understanding of the logical structure of debates about law-making proposals. Another aim of the case studies is to provide new benchmark examples for comparing alternative formal frameworks for modelling argumentation. In particular, this paper aims to illustrate the usefulness of two features of ASPIC+: its distinctions between deductive and defeasible inference rules and its ability to express arbitrary preference orderings on arguments.В этой статье структура ASPIC+ для аргументационного вывода используется для формальной реконструкции двух дискуссий о законодательных инициативах: позиции правоведа по поводу одного голландского законопроекта и решения общегражданского суда США о том, надлежит ли вынести постановление на основе существующей нормы или необходимо выделить в ней исключения. Обе дискуссии формализованы как практические рассуждения на основе версий использования схемы от позитивных и негативных последствий. Эти два случая вносят вклад в понимание логической структуры дискуссий о законодательных инициативах. Другая цель исследования этих двух случаев — сформулировать новые показательные примеры в целях сравнения альтернативных формальных структур для моделирования аргументации. В частности, эта статья нацелена на то, чтобы проиллюстрировать полезность двух характеристик структуры ASPIC+: возможность различать между дедуктивными и отменяемыми правилами и возможность выразить произвольные упорядочивания аргументов на основе отношения предпочтения

Algorithms for argument systems

Argument systems are computational models that enable an artificial intelligent agent to reason via argumentation. Basically, the computations in argument systems can be viewed as search problems. In general, for a wide range of such problems existing algorithms lack five important features. Firstly, there is no comprehensive study that shows which algorithm among existing others is the most efficient in solving a particular problem. Secondly, there is no work that establishes the use of cost-effective heuristics leading to more efficient algorithms. Thirdly, mechanisms for pruning the search space are understudied, and hence, further pruning techniques might be neglected. Fourthly, diverse decision problems, for extended models of argument systems, are left without dedicated algorithms fine-tuned to the specific requirements of the respective extended model. Fifthly, some existing algorithms are presented in a high level that leaves some aspects of the computations unspecified, and therefore, implementations are rendered open to different interpretations. The work presented in this thesis tries to address all these concerns. Concisely, the presented work is centered around a widely studied view of what computationally defines an argument system. According to this view, an argument system is a pair: a set of abstract arguments and a binary relation that captures the conflicting arguments. Then, to resolve an instance of argument systems the acceptable arguments must be decided according to a set of criteria that collectively define the argumentation semantics. For different motivations there are various argumentation semantics. Equally, several proposals in the literature present extended models that stretch the basic two components of an argument system usually by incorporating more elements and/or broadening the nature of the existing components. This work designs algorithms that solve decision problems in the basic form of argument systems as well as in some other extended models. Likewise, new algorithms are developed that deal with different argumentation semantics. We evaluate our algorithms against existing algorithms experimentally where sufficient indications highlight that the new algorithms are superior with respect to their running time

Representation Of Case Law For Argumentative Reasoning

Modelling argumentation based on legal cases has been a central topic of AI and Law since its very beginnings. The current established view is that facts must be determined on the basis of evidence. Next, these facts must be used to ascribe legally significant predicates (factors and issues) to the case, on the basis of which the outcome can be established. This thesis aims to provide a method to encapsulate the knowledge of bodies of case law from various legal domains using a recent development in AI knowledge representation, Abstract Dialectical Frameworks (ADFs), as the central feature of the design method. Three legal domains in the US Courts are used throughout the thesis: The domain of the Automobile Exception to the Fourth Amendment, which has been freshly analysed in terms of factors in this thesis; the US Trade Secrets domain analysed from well-known legal case-based reasoning systems (CATO and IBP); and the Wild Animals domain analysed extensively in AI and Law. In this work, ADFs play a role akin to that of Entity-Relationship models in the design of database systems to design and implement programs intended to decide cases, described as sets of factors, according to a theory of a particular domain based on a set of precedent cases relating to that domain. The ADFs in this thesis are instantiated from different starting points: factor-based representation of oral dialogues and factor-based analysis of legal opinions. A legal dialogue representation model is defined for the US Supreme Court Oral Hearing dialogues. The role of these hearings is to identify the components that can form the basis of an argument that will resolve the case. Dialogue moves used by participants have been identified as the dialogue proceeds to assert and modify argument components in term of issues, factors and facts, and to produce what are called Argument Component Trees (ACTs) for each participant in the dialogue, showing how these components relate to one another. The resulting trees can be then merged and used as input to decide the accepted components using an ADF. The model is illustrated using two landmark case studies in the Automobile Exception domain: Carney v. California and US v. Chadwick. A legal justification model is defined to capture knowledge in a legal domain and to provide justification and transparency of legal decisions. First, a legal domain ADF is instantiated from the factor hierarchy of CATO and IBP, then the method is applied to the other two legal domains. In each domain, the cases are expressed in terms of factors organised into an ADF, from which an executable program can be implemented in a straightforward way by taking advantage of the closeness of the acceptance conditions of the ADF to components of an executable program. The proposed method is evaluated to test the ease of implementation, the efficacy of the resulting program, the ease of refinement, transparency of the reasoning and transferability across legal domains. This evaluation suggests ways of improving the decision by incorporating the case facts, and considering justification and reasoning using portions of precedents. The final result is ANGELIC (ADF for kNowledGe Encapsulation of Legal Information from Cases), a method for producing programs that decide the cases with a high degree of accuracy in multiple domains