Online reviews now have a considerable influence on consumer choices. However, little work has focused on what features of review platforms influence review quality. We present a novel approach to identify the features that encourage quality reviews. By interpreting reviews as arguments for or against the product, an argument scheme can be used to simulate the emergent reliability of reviews resulting from different setups of the online review platform. Our results show that if the most recent, helpful, or polarised reviews are promoted over quality, then good quality reviews will almost never be shown to users.

We report on a study undertaken to analyse AI performance on two tasks involved in automating processing of cases from the European Court of Human Rights: classification of legal case outcomes and keyword prediction. Results show variation across Articles and Court levels, and challenge the common viewpoint that larger legal corpora combined with larger models will be sufficient for effective automated legal reasoning. Legal summarisation, as reflected with keyword prediction, proved more challenging than outcome classification. Our results suggest the need for improved case law retrieval and understanding of contextual factors for effective automated legal decision support.

Argumentation is often an attempt to resolve disagreement, but it is not always possible to reach a resolution. This is illustrated in law where multi-judge trials often end with a split decision. Not only do the judges disagree as to outcome (dissenting opinions), but also as to the reasons for a given outcome (concurring opinions). These disagreements can be explained in terms of different values held by the judges concerned. But while the role of values in determining which arguments are accepted has been widely explored, values can also determine which arguments can be constructed. The paper provides an analysis of this phenomenon.

The purpose of this paper is to provide a definitive, up-to-date account of a methodology has that been proven successful for representing and reasoning about legal domains. The ANGELIC (ADF for kNowledGe Encapsulation of Legal Information for Cases) methodology was originally developed to exploit then recent developments in knowledge representation techniques that lend themselves well to capturing factor-based reasoning about legal cases. The methodology is situated firmly within the tradition of research in AI and Law that aims to build systems that are knowledge rich in terms of the domain expertise that is emulated within the systems. When the methodology was first introduced, it was demonstrated on academic examples, but it was subsequently used in and evaluated on a variety of real world domains for external clients. This set of evaluation exercises yielded a variety of learning points as the methodology was applied to different legal domains with their own particular features. These learning points, and the extensions to the methodology that follow from them, urge a consolidation exercise to provide an updated version of the methodology that reflects how it has matured over time. This paper represents a milestone in the development of the methodology in that it presents the ANGELIC II Domain Model, along with a description of its constituent parts, and demonstrates its application through a case study in a key evaluation domain.

In the early days of AI and Law, the use of hypotheticals - imaginary cases constructed to test or explore a particular point - was seen as an important part of legal reasoning. Hypotheticals, have however, attracted increasingly less interest, and have hardly been seen for a decade or more. In this short paper we discuss why hypotheticals disappeared, and the need to reintroduce them if a comprehensive account of reasoning with legal cases is to be produced. The paper includes a discussion of how hypotheticals can be deployed within a recent framework of argumentation schemes describing reasoning with legal cases.

Recent years have witnessed significant progress in the deployment of advanced Natural Language Processing (NLP) techniques based on transformer technology, across many domains and applications. However, in legal domains, due to the complexity, length, and sparsity of legal case documents, the use of these advanced NLP techniques has offered comparatively slight returns. Perhaps even more importantly, such methods are critically lacking in explainability and justification of outputs, which are essential for many legal applications. We propose that the direction of these NLP techniques should be aimed at ascription to a legal knowledge model, which can then provide the necessary and auditable justifications for the rationale of any case outcome. In this paper we investigate the effectiveness of using Hierarchical Bidirectional Encoder Representations from Transformers (H-BERT) models to ascribe to an Angelic Domain Model (ADM) that is able to represent the legal knowledge of a domain in a structured way, enabling justifications and improving performance. Our study involved an annotation task on a popular domain, cases from the European Court of Human Rights, to gain an understanding of the balance of complaints in the domain. The data set produced from this study enabled training of models for factor ascription using the classification targets derived from the annotations. We present results of experiments conducted to evaluate the performance of the ascription task at three different levels of abstraction within the structured model.

In recent years a considerable amount of research has been devoted to formal theories of precedential constraint. In this note I consider a recent paper which explores the use of factor hierarchies in this connection. In that work it was shown both that cases constrained with the use of a hierarchy may be unconstrained if the hierarchy is flattened, and that cases unconstrained with a hierarchy may be constrained when the hierarchy is flattened. I discuss the nature of factor hierarchies and attempt to explain these results.

Current theories of precedential constraint attempt to incorporate dimensions into the reasons for decisions. We argue that this is an unnecessary complication, and precedential constraint can be handled using only factors. In our account the role of dimensions is to organise facts, and their effect operates at the factor ascription level, prior to precedential constraint being applied.

We report a study undertaken to analyse human performance on the verdict classification task. Several approaches have addressed this task with outcomes compared against the outcomes from actual legal cases. Results vary and we investigate how classification is done by humans. A key finding is that fact descriptions alone are insufficient for accurate classification, independent of legal background.

In this paper we report on a significant research project undertaken to design, implement and evaluate explainable decision-support tools for deciding legal cases. We provide a model of a legal domain, Article 6 of the European Convention on Human Rights, constructed using a methodology from the field of computational models of argument. We describe how the formal model has been developed, extended and transformed into practical tools, which were then used in evaluation exercises to determine the effectiveness and usability of the tools. The underpinning AI techniques used yield a level of explanation that is firmly grounded in legal reasoning and is also digestible by the target end users, as demonstrated through our evaluation activities. The results of our experimental evaluation show that on the first pass, our tool achieved an accuracy rate of 97% in matching the actual decisions of the cases and the user studies conducted gave highly encouraging results with respect to usability. As such, our project demonstrates how trustworthy AI tools can be built for a real world legal domain where critical needs of the end users are accounted for.

Reasoning with legal cases by balancing factors (reasons to decide for and against the disputing parties) is a two stage process: first the factors must be ascribed and then these reasons for and against weighed to reach a decision. While the task of determining which set of reasons is stronger has received much attention, the task of factor ascription has not. Here we present a set of argument schemes for factor ascription, illustrated with a detailed example.

We describe a system for constructing, evaluating and visualising arguments based on a theory of a legal domain, developed using the Angelic methodology and the Carneades argumentation system. The visualisation can be used to explain particular cases and to refine and maintain the theory. A full implementation of the well known US Trade Secrets Domain is used to illustrate the process.

Although argumentation is often studied in AI using abstract frameworks, actual debate often shows a dynamic interaction between argument structure and attack. Often intermediates steps in the reasoning are omitted, but it may be these intermediate steps which are the vulnerable parts of the argument. Inspired by Loui and Norman’s work on the rationale of arguments, we study the relation between argument structure and attack in terms of the unpacking of arguments. The paper provides an analysis of two kinds of rationales discussed by Loui and Norman. Example dialogues inspired by Dutch tort law are used for illustration.

Reasoning with legal cases has long been modelled using symbolic methods. In recent years, the increased availability of legal data together with improved machine learning techniques has led to an explosion of interest in data-driven methods being applied to the problem of predicting outcomes of legal cases. Although encouraging results have been reported, they are unable to justify the outcomes produced in satisfactory legal terms and do not exploit the structure inherent within legal domains; in particular, with respect to the issues and factors relevant to the decision. In this paper we present the technical foundations of a novel hybrid approach to reasoning with legal cases, using Abstract Dialectical Frameworks (ADFs) in conjunction with hierarchical BERT. ADFs are used to represent the legal knowledge of a domain in a structured way to enable justifications and improve performance. The machine learning is targeted at the task of factor ascription; once factors present in a case are ascribed, the outcome follows from reasoning over the ADF. To realise this hybrid approach, we present a new hybrid system to enable factor ascription, envisioned for use in legal domains, such as the European Convention on Human Rights that is used frequently in modelling experiments.

One approach to building legal support systems is to run an executable model of the relevant knowledge through an interface designed to collect information from the user and provide explanations. The usability of such systems depends on the terms used in the law being represented: often only users familiar with the practice and application of the law will be able to provide the required information. Earlier work applied this approach to the European Convention on Human Rights (ECHR). Although the performance of the tool built for that domain was good, the questions posed to the user demanded a good deal of knowledge and experience of the ECHR. Here we use the knowledge of an expert with extensive experience of the ECHR to extend the model, through intermediate levels, to identify questions that are appropriate to the target user. We have undertaken a pilot evaluation in which a small number of lawyers have used the prototype program and provided very positive feedback, showing that they are receptive to AI solutions that give effective, explainable decision support.

Horty, Rigoni and Prakken have developed formal characterisations of precedential constraint based on dimensions and factors as introduced in HYPO and CATO. We discuss the relation between dimensions and factors and also describe the current models of precedential constraint based on factors, along with some criticisms of them. We argue that problems arise from ignoring the structure of legal cases that is provided by the notion of issues, and that seeing precedential constraint in terms of issues rather than whole cases provides a more effective approach and better reflects legal practice. The advantages of the issue based approach are illustrated with a concrete example. We then discuss how dimensions should be accommodated, suggesting that this is best done by seeing reasoning with legal cases as a two stage process: first factors are ascribed to cases and then factor based reasoning can be used to arrive at a decision. Thus precedential constraint can be described in terms of factors, dimensions being handled at the first stage. Both stages are constrained, in different ways, by precedents: we identify three types of precedent: framework precedents which structure cases into issues, preference precedents which resolve conflicts between opposing sets of factors within these issues, and ascription precedents which constrain the mapping from facts to factors.

Explanation and justification of legal decisions has become a highly relevant topic in light of the explosion of interest in the use of machine learning (ML) approaches to predict legal decisions. Current suggestions are to use the established factor based explanations developed in AI and Law as the basis for explaining such programs. We, however, identify factor ascription as an important aspect of explanation of case outcomes not currently covered, and argue that explanations must also include this aspect. Finally, we outline our proposal for a hybrid system approach that combines ML and Abstract Dialectical Framework (ADF) layers to engender an explainable process.

In this paper we present an argumentation-based approach to representing and reasoning about a domain of law that has previously been addressed through a machine learning approach. The domain concerns cases that all fall within the remit of a specific Article within the European Court of Human Rights. We perform a comparison between the approaches, based on two criteria: ability of the model to accurately replicate the decision that was made in the real life legal cases within the particular domain, and the quality of the explanation provided by the models. Our initial results show that the system based on the argumentation approach improves on the machine learning results in terms of accuracy, and can explain its outcomes in terms of the issue on which the case turned, and the factors that were crucial in arriving at the conclusion.

Modelling reasoning with precedents has been a central concern of AI and Law since its inception. A recent paper has provided a discussion (in jurisprudential terms) of whether such reasoning is best seen as rule application or analogy. We review some of the prominent AI and Law approaches and find that over the years there has been a move away from analogy to rule application. Even in those approaches which do use analogy, however, the analogies handled concern only analogies between cases represented as sets of factors, and do not consider analogies between the elements of the fact situations peculiar to particular cases. In actual practice, however, analogies are used to determine which factors are relevant in a case, and which party is favoured by particular aspects of the case situation. Such analogies relate not to factors, but to real-world elements of the case and are hard to make and critique without a comprehensive common sense ontology. Thus while we may be able to construct specific ontologies to model past examples of such analogical reasoning, which can be useful for simulation and teaching, the ability to perform analogical reasoning on novel situations is, and is likely to remain, infeasible. This conclusion suggests that there will always be limits to our ability to construct systems able to handle new cases presenting novel situations.

This paper discusses reasoning about norms using Extended Argumentation Frameworks. We argue that norms emerge from a process of value based practical reasoning. We model practical reasoning using Action Based Alternating Transition Systems with Values, with the resulting arguments evaluated in an Extended Argumentation Framework, which permits arguments to attack attacks as well as arguments, and so provides a natural way of representing exceptions. Following our consideration of how norms are justified, we consider their application. Our representation of norms in EAFs enables us to offer an accessible account of permissions and exceptions. We illustrate our approach with an example relating to some US Supreme Court Fourth Amendment cases.

This paper considers dynamic aspects of the development of case law. The underlying approach is to see law as a "moving classification system" based on Levi's notion of a three stage life cycle for case law. Our aim is to provide foundations for computational support for consideration of these dynamic aspects. We first use a fictional example to show how our approach works, and then illustrate the approach by applying it to sequences of real cases: Levi's cases starting from Dixon v Bell, and cases concerning the automobile exception to the US 4th Amendment, focusing on those involving luggage.

© 2019 The authors and IOS Press. ANGELIC is a methodology for encapsulating knowledge of a body of case law. Logiak is a system intended to support the development of logic programs by domain experts, and provides an excellent environment for the rapid realisation of ANGELIC designs. We report our use of Logiak to realise ANGELIC designs, using both Boolean factors and factors with magnitude.

We relate the ANGELIC methodology for acquiring and encapsulating domain knowledge to the ASPIC+ framework for structured argumentation. In so doing we hope to facilitate the building of applications in concrete domains by linking a successful methodology to a proven theoretical framework. We use an example from the ASPIC+ literature to illustrate the relationship.

We discuss the lessons learned from implementing a CATO style system using factors with magnitude. In particular we identify that giving factors magnitudes enables a diversity of reasoning styles and arguments. We distinguish a variety of ways in which factors combine to determine abstract factors. We discuss several different roles for values. Finally we identify the additional value related information required to produce a working program: thresholds and weights as well as a simple preference ordering.

A development environment for the Angelic Methodology.

This paper describes a computational procedure for interpreting contracts in accordance with the English common law rules of interpretation of contract as stated by Lord Hoffmann. Our approach makes extensive use of an ontology of legal terms, specialised for the context in which the contract was made. We illustrate the approach using three examples closely based on actual cases decided by Lord Hoffmann.

We describe the use of the ANGELIC methodology, developed to encapsulate knowledge of particular legal domains, to build a full scale practical application for internal use by a firm of legal practitioners. We describe the application, the sources used, the stages in development and the application. Some evaluation of the project and its potential for further development is given. The project represents an important step in demonstrating that academic research can prove useful to legal practitioners confronted by real legal tasks.

We build on two recent attempts to formalise reasoning with dimensions which effectively map dimensions into factors. These enable propositional reasoning, but sometimes a balance between dimensions needs to be struck, and to permit trade offs we need to keep the magnitudes and so reason more geometrically. We discuss dimensions and values, arguing that values can play several distinct roles, both explaining preferences between factors and indicating the purposes of the law.

In practical reasoning, it is important to take into consideration what other agents will do, since this will often influence the effect of actions performed by the agent concerned. In previous treatments, the actions of others must either be assumed, or argued for using a similar form of practical reasoning. Such arguments, however, will also depend on assumptions about the beliefs, values and preferences of the other agents, and so are difficult to justify. In this paper we capture, in the form of argumentation schemes, reasoning about what others will do, which depends not on assuming particular actions, but through consideration of the expected utility (based on the promotion and demotion of values) of particular actions and alternatives. Such arguments depend only on the values and preferences of the agent concerned, and do not require assumptions about the beliefs, values and preferences of the other relevant agents. We illustrate the approach with a running example based on Prisoner's Dilemma.

Practical reasoning, reasoning about what actions should be chosen, is highly dependent both on the individual values of the agent concerned and on what others choose to do. We discuss how value based argumentation about what to do can be performed without making assumptions about the preferences of the other agents. We then show how expected utility calculations relate to the value-based argumentation approach, and express the reasoning as arguments and objections, so that they can be integrated value-based practical reasoning. We illustrate our discussion with examples of value based reasoning in public goods games as used in experimental economics and present an initial evaluation of the approach in terms of these experiments.

Norms are designed to guide choice of actions. Value-based practical reasoning is an approach to explaining and justifying choice of actions in terms of value preferences. Here we explore how value-based practical reasoning can be related to norms and their evolution. Starting from a basic model of a society and the norms that can arise from it, we consider how additional values, and a more sophisticated model, with more detailed states and a history, and a finer grained description of actions, can accommodate more complex norms, and a correspondingly more complex social order.

In this paper we present an overview of the process of argumentation with legal cases, from evidence to verdict. We identify the various different types of statement involved in the various stages, and describe how the various types relate to one another. In particular we show how we can obtain the legally accepted facts which form the basis for consideration of the law governing the cases from facts about the world. We also explain how we can determine which particular facts are relevant. In so doing we bring together several important pieces of AI and Law research and clarify their relationships.

The ANGELIC (ADF for kNowledGe Encapsulation of Legal Information from Cases) project provided a methodology for implementing a system to predict the outcome of legal cases based on a theory of the relevant domain constructed from precedent cases and other sources. The method has been evaluated in several domains, including US Trade Secrets Law. Previous systems in this domain were based on factors, which are either present or absent in a case, and favour one of the parties with the same force for every factor. Evaluations have, however, suggested that the ability to represent different degrees of presence and absence, and different strengths, could improve performance. Here we extend the methodology to allow for different degrees of presence and support, by using dimensions as a bridge between facts and factors. This new program is evaluated using a standard set of test cases.

There is an increasing need for norms to be embedded in technology as the widespread deployment of applications such as autonomous driving and warfare becomes ever closer. Current approaches to norms in multi-agent systems tend either to simply make prohibited actions unavailable, or to provide a set of rules (principles) which the agent is obliged to follow. We argue that both these approaches are inadequate: in order to meet unexpected situations agents must be capable of violating norms, when it is appropriate to do so. This in turn requires that agents be able to reason about what they should do without reference to the norms. One way to achieve this is to conduct value based reasoning using an argumentation scheme designed for practical reasoning. Such reasoning requires that agents have an acceptable set of values and an acceptable ordering on them. We discuss what might count as an acceptable ordering on values, and how such an ordering might be determined. Law breaking is illustrated through a simple road traffic example.

In this article, I offer an interpretation of cycles in Dung-style argumentation frameworks in which even length cycles are treated as dilemmas and odd length cycles as paradoxes. The different properties of cycles with different parities arising from the use of preferred semantics are argued to be coherent with this interpretation.

In this paper we revisit reasoning with legal cases, with a view to articulating the relationships between issues, factors, facts and values, and to identifying areas for future work on these topics. We start from the different ways in which attempts have been made to go beyond a fortori reasoning from the precedent base, so that conclusions not fully justified by the precedents can be drawn. We then use a particular example domain taken from the literature to illustrate our preferred approach and to relate factors and values. From this we observe that much current work depends critically on the ascription of factors to cases in a Boolean manner, while in practice there are compelling reasons to see the presence of factors as a matter of degree. On the basis of our observations we make suggestions for the directions of future work on this topic.

Abstract Dialetical Frameworks (ADFs) are a recent development in computational argumentation which are, it has been suggested, a fruitful way of implementing theories of case law expressed in terms of factors. In this paper we evaluate this proposal, by representing the CATO analysis using ADFs. We evaluate the ease of implementation, the efficacy of the resulting program, ease of refinement of the program, transparency of the reasoning, relation to formal argumentation techniques, and transferability across domains.

Stories and legal cases have much in common, but there are also differences. Both can be seen as a sequence of events, but in a legal case the facts and events are legally qualified. Moreover, the point of a story is usually implicit, whereas the outcome of a legal case is explicitly explained. Stories have been mainly used in AI and Law to explore the evidence presented in legal cases, but here we will explore the relationship on the assumption the facts of the case have already been established, and so include legal qualification and the decision. We illustrate our approach the well known wild animals and Popov v Hayashi cases.

In this paper we discuss tools for rapid prototyping of legal CBR. We describe how a recent highly quantitative analysis can be realised using a spreadsheet, while a more nuanced approach at a finer level of granularity can be prototyped with the web service Carneades, which displays its results as an argument graph.

In this article we offer a formal account of reasoning with legal cases in terms of argumentation schemes. These schemes, and undercutting attacks associated with them, are formalized as defeasible rules of inference within the ASPIC+ framework. We begin by modelling the style of reasoning with cases developed by Aleven and Ashley in the CATO project, which describes cases using factors, and then extend the account to accommodate the dimensions used in Rissland and Ashley's earlier HYPO project. Some additional scope for argumentation is then identified and formalized.

In this paper we describe a tool which supports the analysis of arguments in the legal domain for the purpose of building computational models that use factor-based reasoning (FBR).

In this paper we extend an argumentation scheme for practical reasoning with values based on Action-based Alternating Transition Systems. While the original scheme considers only arguments arising from the immediately next state, our proposals will enable long term considerations to be taken into account. We consider the various reasons for and against performing an action that arise from these longer term considerations, and develop a new set of argumentation schemes for practical reasoning which allows a clearer separation between facts, values and preferences, and more precise targeting of attacks.

In this paper, we show two important connections between computational models of narrative and computational models of argumentation. First, we show how argumentation techniques can be applied to enrich story understanding, especially where an understanding the story requires understanding of the motives of its characters. This also helps to explain how stories can themselves be seen as as arguments for a particular ordering on values within a value based argumentation framework. We illustrate our discussion using biblical parables, taking as our main example the parable of the Good Samaritan.

In recent years a powerful generalisation of Dung's abstract argumentation frameworks, Abstract Dialectical Frameworks (ADF), has been developed. ADFs generalise the abstract argumentation frameworks introduced by Dung by replacing Dung's single acceptance condition (that all attackers be defeated) with acceptance conditions local to each particular node. Such local acceptance conditions allow structured argumentation to be straightforwardly incorporated. Related to ADFs are prioritised ADFs, which allow for reasons pro and con a node. In this paper we show how these structures provide an excellent framework for representing a leading approach to reasoning with legal cases.

The design and analysis of norms is a somewhat neglected topic in AI and Law. In recent years powerful techniques to model and analyse norms have been developed in the Multi-Agent Systems community. In this paper I consider these techniques from and AI and Law perspective, and suggest a framework for the exploration of these issues.

This paper describes an on-going project investigating the use of crowdsourcing in policy consultation. We see this as particularly useful in the early consultation stages (e.g. White Paper) when the opinions of the public are sought to determine policy objectives. The project involves a number of discrete stages: thus far we have looked especially at question design, the generation of suitable test data and the suitability of various aggregation algorithms. On the basis of these results we can design software to collect opinions, and generate arguments. The test data will allow the arguments to be evaluated with respect to variously composed populations.

Argument schemes can provide a means of explicitly describing reasoning methods in a form that lends itself to computation. The reasoning required to distinguish cases in the manner of CATO has been previously captured as a set of argument schemes. Here we present argument schemes that encapsulate another way of reasoning with cases: using preferences between social values revealed in past decisions to decide cases which have no exact matching precedents when the cases are described in terms of factors. We provide a set of schemes, with variations to capture different ways of comparing sets and varying degrees of promotion of values; we formalise these schemes; and we illustrate them with some examples.

Citizens have a variety of ways to consult with their representatives about policy proposals, seeking justifications, objecting to all or part of it, or making a counter-proposal. For the first, the representative needs only to state a justification. For the second, the representative would want to understand the objections, which may involve asking some questions. For the third, the citizen would have to provide a well formulated proposal that can then be critiqued from the standpoint of the government's own policy proposal. At the end of such a consultation, users will have aired their proposals, understood the implications, and received feedback on how their proposals contrast to that of the government.

In this paper we provide a structured analysis of US Supreme Court Oral Hearings to enable identification of the relevant issues, factors and facts that can be used to construct a test to resolve a case. Our analysis involves the production of what we term ‘argument component trees’ (ACTs) in which the issues, facts and factors, and the relationship between these, are made explicit. We show how such ACTs can be constructed by identifying the speech acts that are used by the counsel and Justices within their dialogue. We illustrate the application of our analysis by applying it to the oral hearing that took place for the case of Carney v. California, and we relate the majority and minority opinions delivered in that case to our ACTs. The aim of the work is to provide a formal framework that addresses a particular aspect of case-based reasoning: enabling the identification and representation of the components that are used to form a test to resolve a case and guide future behaviour.

In previous work we presented argumentation schemes to capture the CATO and value based theory construction approaches to reasoning with legal cases with factors. We formalised the schemes with ASPIC+, a formal representation of instantiated argumentation. In ASPIC+ the premises of a scheme may either be a factor provided in a knowledge base or established using a further argumentation scheme. Thus far we have taken the factors associated with cases to be given in the knowledge base. While this is adequate for expressing factor based reasoning, we can further investigate the justifications for the relationship between factors and facts or evidence. In this paper we examine how dimensions as used in the HYPO system can provide grounds on which to argue about which factors should apply to a case. By making this element of the reasoning explicit and subject to argument, we advance our overall account of reasoning with legal cases and make it more robust.

Open multiagent systems (MAS) typically require the participating agents to comply with system-level rules, or norms, and may punish non-compliance. An interesting challenge for designers of such systems is how to provide for dynamic, run-time allocation of norms. In this article, we present a novel, framework-independent approach enabling a norm architecture to be overlaid on any type of multiagent system. We achieve this by defining rights, responsibilities and sanctions in a manner which allows for their run-time assignment and re-assignment to agents in a normative multiagent system. The overlay architecture we propose requires the system to include a normative knowledge (KB) base containing explicit representation of norms as conditional rules, where the preconditions for instantiation of a norm are various run-time occurrences, such as the execution of some agent action, or the occurrence of some event. Our method has both wide generality and flexibility, allowing at one extreme few or no agent roles, each role having many conditional norms, or allowing, at the other extreme, many roles, each having only a small number of conditional norms, or allowing, indeed, any position between these two extremes.

We introduce a derivative of Dung's seminal abstract argumentation frameworks (afs) through which distinctive features both of Dung's semantics and so-called “value-based” argumentation frameworks (vafs) may be captured. These frameworks, which we describe as uniform afs, thereby recognise that, in some circumstances, arguments may be deemed acceptable, not only as a consequence of subjective viewpoints (as are modelled by the concept of audience in vafs) but also as a consequence of “value independent” acceptance of other arguments: for example in the case of factual statements. We analyse divers acceptability conditions for arguments in uniform afs and obtain a complete picture for the computational complexity of the associated decision questions. Amongst other results it is shown that reasoning in uniform afs may pose significantly greater computational challenges than either standard or value-based questions, a number of problems being complete for the third level of the polynomial hierarchy.

In this paper I review my engagement with argumentation over the past forty years. I describe the perspective I brought from philosophy and the Civil Service, and consider a number of aspects of computational argumentation: knowledge based systems, explanation, context, audiences, schemes and models. A key feature of argumentation is that it is an activity which has to be actively engaged with, whereas a proof is an object to be understood and admired.

In this paper we offer a proposal to enable agents to discuss the suitability of plans based on an argumentation scheme and associated critical questions. The detail encompassed by the argumentation scheme means that there is a large number of critical questions, and so dialogues may in principle be very lengthy. To improve the efficiency of dialogues we present two strategies for selecting questions. We have implemented the system and here present results showing how both strategies are effective in reducing the number of questions required to reach agreement, although their relative effectiveness is dependent on characteristics of the problem.

Argumentation is key to understanding and evaluating many texts. The arguments in the texts must be identified; using current tools, this requires substantial work from human analysts. With a rule-based tool for semi-automatic text analysis support, we facilitate argument identification. The tool highlights potential argumentative sections of a text according to terms indicative of arguments (e.g. ‘suppose’ or ‘therefore’) and domain terminology (e.g. camera names and properties). The information can be used by an analyst to instantiate argumentation schemes and build arguments for and against a proposal. The resulting argumentation framework can then be passed to argument evaluation tools.

Since the 1980s, AI and Law has attempted to capture legal expertise in computer programs. But what is this expertise? This paper reviews a number of approaches, from the 1980s to the present day, which represent different answers to this question. It argues that our notion, and understanding, of expertise has developed and improved over the decades. As yet, however, only a few rather specific aspects have been addressed in detail, in particular the move from intermediate predicates to legal consequences, and the distinguishing of precedents. Much more, including the moves from evidence to facts and from facts to intermediate predicates, awaits exploration.

Domain models have proven useful as the basis for the construction and evaluation of arguments to support deliberation about policy proposals. Using a model provides the means to systematically examine and understand the fine-grained objections that individuals might have about the policy. While in previous approaches, a justification for a policy proposal is presented for critique by the user, here, we reuse the domain model to invert the roles of the citizen and the Government: a policy proposal is elicited from the citizen, and a software agent automatically and systematically critiques it relative to the model and the Government's point of view. Such an approach engages citizens in a critical dialogue about the policy actions, which may lead to a better understanding of the implications of their proposals and that of the Government. A web-based tool that interactively leads users through the critique is presented.

Semantic models have received little attention in recent years, much of their role having been taken over by developments in ontologies. Ontologies, however, are static, and so have only a limited role in reasoning about domains in which change matters. In this paper, we focus on the domain of policy deliberation, where policy decisions are designed to change things to realise particular social values. We explore how a particular kind of state transition system can be constructed to serve as a semantic model to support reasoning about alternative policy decisions. The policy making process includes stages that support the construction of a model, which can then be exploited in reasoning. The reasoning itself will be driven by a particular argumentation scheme for practical reasoning, and the ways in which arguments based on this scheme can be attacked and evaluated. The evaluation provides alternative policy positions. The semantics underpin a current web-based implementation, designed to solicit structured feedback on policy proposals.

A formal two-phase model of democratic policy deliberation is presented, in which in the first phase sufficient and necessary criteria for proposals to be accepted are determined (the `acceptable' criteria) and in the second phase proposals are made and evaluated in light of the acceptable criteria resulting from the first phase. Such a separation gives the discussion a clear structure and prevents time and resources from being wasted on evaluating arguments for proposals based on unacceptable criteria. Argument schemes for both phases are defined and formalised in a logical framework for structured argumentation. The process of deliberation is abstracted from and it is assumed that both deliberation phases result in a set of arguments and attack and defeat relations between them. The acceptability status of criteria and proposals within the resulting argumentation framework is then evaluated using preferred semantics. For cases where preferences are required to choose between proposals, inference rules for deriving preferences between sets from an ordering of their elements are given.

In this paper we offer an account of reasoning with legal cases in terms of argumentation schemes. These schemes, and undercutting attacks associated with them, are expressed as defeasible rules of inference that will lend themselves to formalisation within the AS-PIC+ framework. We begin by modelling the style of reasoning with cases developed by Aleven and Ashley in the CATO project, which describes cases using factors, and then extend the account to accommodate the dimensions used in Rissland and Ashley's earlier HYPO project. Some additional scope for argumentation is then identified and formalised.

In recent years it has become quite usual to view legal decisions in terms of consideration of the values affected by deciding the case for or against a particular party. Often deciding for, say, the plaintiff will promote one value at the expense of another. Precedents are then supposed to guide the way in which this conflict is resolved. In this paper we will consider a series of cases exploring the so-called automobile exception to the requirement of the Fourth Amendment protecting against unreasonable search of persons, houses, papers, and effects. These cases highlight a conflict between the value of law enforcement and the value of privacy as protected by the Fourth Amendment, and will be used to illuminate questions about the treatment of value conflicts arising from previous work in AI and Law.

When reasoning about the facts of a case, we typically use stories to link the known events into coherent wholes. One way to establish coherence is to appeal to past examples, real or fictitious. These examples can be chosen and critiqued using the case-based reasoning (CBR) techniques from the AI and Law literature. In this paper, we apply these techniques to factual stories, assessing a story about the facts using precedents. We thus show how factual and legal reasoning can be combined in a CBR model.

The paper addresses the extraction, formalisation, and presentation of public policy arguments. Arguments are extracted from documents that comment on public policy proposals. Formalising the information from the arguments enables the construction of models and systematic analysis of the arguments. In addition, the arguments are represented in a form suitable for presentation in an online consultation tool. Thus, the forms in the consultation correlate with the formalisation and can be evaluated accordingly. The stages of the process are outlined with reference to a working example.

The abstract nature of Dung’s theory of argumentation accounts for its wide-spread application as a general framework for various species of non-monotonic reasoning, and, more generally, reasoning in the presence of conflict. In this pa-per we formalise reasoning about argumentation within the Dung argumentation paradigm itself. A metalevel Dung argumentation framework is itself instantiated by arguments that make statements about arguments, their interactions, and their evaluation in an object-level argumentation framework. We show how Dung’s theory, and object level extensions of Dung’s theory, such as those intended to accommodate preferences, can then be uniformly characterised by metalevel argu-mentation in a Dung framework. We then discuss how this provides for application of the full range of theoretical and practical developments of Dung’s theory, to ex-tensions of Dung’s theory, and provides for integration and further augmentation of these extensions.

This paper argues that accrual should be modelled in terms of reasoning about the application of preferences to sets of arguments, and shows how such reasoning can be formalised within metalevel argumentation frameworks. These frameworks adopt the same machinery and level of abstraction as Dung's argumentation framework. We thus provide a dialectical argumentation semantics that integrates accrual, and illustrate our approach by instantiating our framework with the arguments and attacks defined by an object level formalism that accommodates reasoning about priorities over sets of rules.

Our focus in this paper is to explore how emotional factors can complement rationality in decision making. Our approach is to develop a model of the situation and use this model to generate arguments for and against the actions that an agent can perform. Actions are then chosen by evaluating this set of arguments according to the subjective preferences and emotional state of the agent concerned. A mechanism to control and balance the extent of emotional effects is also introduced. We illustrate our approach with an extended case study based on an implemented system embodying this approach.

We have previously introduced the notion of arguing from experience, whereby agents debate a classification problem using arguments based on association rules mined “on the fly” from their individual datasets. In this paper we extend PISA, which allows for n agents to argue about cases which have n possible classifications. By allowing any number of agents to participate all the agents supporting a given classification can form a collaborative group for the purposes of the dialogue. We describe how the system is organised, give an example, and report results which suggest that allowing groups in this way has a beneficial effect on the quality of the result.

A formal two-phase model of democratic policy deliberation is presented, in which in the first phase sufficient and necessary criteria for proposals to be accepted are determined (the ‘admissible’ criteria') and in the second phase proposals are made and evaluated in light of the admissible criteria resulting from the first phase. Argument schemes for both phases are defined and formalised in a logical framework for structured argumentation. The process of deliberation is abstracted from and it is assumed that both deliberation phases result in a set of arguments and attack and defeat relations between them. Then preferred semantics is used to evaluate the acceptability status of criteria and proposals.

As knowledge representation tools become more sophisticated, and computer systems increase in power and ubiquity, the prospects of building practical applications based on the representation of large amounts of legislation draw closer. In this paper we reflect on our experience with developing a knowledge representation language for legal rules and an inference engine for this language in the Estrella project, in order to reconsider the principles which should guide the representation of legislation. One common demand, based largely on software engineering considerations relating to maintenance, verification and validation, is that representations should be isomorphic to their sources. We explore this notion by representing a fragment of German Family Law using our tools. We show that there are several different ways of representing even this small and simple fragment of law in an isomorphic fashion. Moreover these differences matter, in terms of where the burden of proof is allocated, in terms of the explanations produced, and in terms of the operational procedures that are reflected.

In this paper we discuss how recent developments in argumenta-tion frameworks, most notably Extended Argumentation Frame-works, can inform the representation of a body of case law using abstract argumentation techniques. This builds on previous work which has first used abstract Argumentation Frameworks, and then Value based Argumentation Frameworks for this purpose. Extended Argumentation Frameworks augment Argumentation Frameworks to not only allow arguments to be attacked, but also attacks to be attacked. This allows argumentation based reason-ing about information normally assumed to be metalevel to the ob-ject level domain of argumentation, including argumentation over preferences, values and the audience based ranking of values pro-moted by arguments. The Extended Argumentation Frameworks can then be rewritten as standard Argumentation Frameworks, so that cases, and values and their rankings relevant to the cases, can be reasoned about using standard dialogue games for Argumenta-tion Frameworks. In this way precedents can be represented as col-lections of arguments and dialogues using these arguments. Now, when confronted with a new case, these dialogues may be used to identify ways of deploying the arguments in the new case so as to reach a favourable position. 1.

This paper studies the use of hypothetical and value-based reasoning in US Supreme-Court cases concerning the United States Fourth Amendment. Drawing upon formal AI & Law models of legal argument a semi-formal reconstruction is given of parts of the Carney case, which has been studied previously in AI & law research on case-based reasoning. The result is compared with Rissland's (1989) analysis in terms of dimensions and Ashley's (2008) analysis in terms of his process model of legal argument with hypotheticals.

Trevor Bench-Capon, Henry Prakken; Editorial: Logic and Law Corner, Journal of Logic and Computation, Volume 19, Issue 6, 1 December 2009, Pages 899, https://do

Much work using argumentation frameworks (AFs) treats arguments as entirely abstract, related by a uniform attack relation that always succeeds unless the attacker can itself be defeated. However, this does not seem adequate for legal argumentation. Some proposals have suggested regulating attack relations using preferences or values on arguments and that filter the attack relation, so that, depending on the audience addressed, some attacks fail and so can be removed from the framework. This does not, however, capture a central feature of legal reasoning: how a decision with respect to the same facts and legal reasoning varies as the judicial context varies. Nor does it capture related context-dependent features of legal reasoning, such as how an audience can prefer or value an argument, yet be constrained by precedent or authority not to accept it. Nor does it explain how certain types of attack may not be allowed in a particular procedural context. For this reason, evaluation of the status of arguments within a given framework must be allowed to depend not only on the attack relations along with the preference or value of arguments, but also on the nature of the attacks and the context in which they are made. We present a means to represent these features, enabling us to account for a number of factors currently considered to be beyond the remit of formal AFs. We give several examples of the use of approach including: appealing a case, overruling a precedent and rehearing of a case as a civil rather than criminal proceeding.

In this paper we present an approach to abductive reasoning in law by examining it in the context of an argumentation scheme for practical reasoning. We present a particular scheme, based on an established scheme for practical reasoning, that can be used to reason abductively about how an agent might have acted to reach a particular scenario, and the motivations for doing so. Plausibility here depends on a satisfactory explanation of why this particular agent followed these motivations in the particular situation. The scheme is given a formal grounding in terms of Action-based Alternating Transition Systems and we illustrate the approach with a running legal example.

Error rates in the assessment of routine claims for welfare benefits have been found to be very high in Netherlands, USA and UK. This is a significant problem both in terms of quality of service and financial loss through over payments. These errors also present challenges for machine learning programs using the data. In this paper we propose a way of addressing this problem by using a process of moderation, in which agents argue about the classification on the basis of data from distinct groups of assessors. Our agents employ an argument based dialogue protocol (PADUA) in which the agents produce arguments directly from a database of cases, with each agent having their own separate database. We describe the protocol and report encouraging results from a series of experiments comparing PADUA with other classifiers, and assessing the effectiveness of the moderation process.

In this article we introduce the Logic and Law corner of this journal. We will discuss a number of ways in which logic has been used in AI and Law, and give some of the key references to previous work on these topics. We will also list some important questions which we see as ready for further exploration. We encourage contributions on these, and other, Logic and Law issues.

In this paper we examine some previous AI and Law attempts to characterise standards of proof, and relate these to the notions of acceptability found in argumentation frameworks, an approach which forms the basis of much recent work on argumentation. We distinguish between the justification of facts and the justication of choices relating to the law and its interpretation. Standards of proof most naturally arise in connection with facts, but points of law have analogous degrees of justification.

In this paper we apply a general account of practical reasoning to arguing about legal cases. In particular, we describe how the reasoning in one very well known property law case can be reconstructed in terms of our account. We begin by summarising our general approach which uses instantiations of an argumentation scheme to provide presumptive justifications for actions, and critical questions to identify arguments which attack these justifications. These arguments and attacks are organised into argumentation frameworks to identify the status of individual arguments. Different beliefs about, and perspectives on, the issue are represented by different agents based on the Belief-Desire-Intention model, and conditions under which these agents may advance justifications and attack them are described. We model the different views of our case in these terms, describe the resulting argumentation frameworks, and relate them to the original majority and dissenting opinions. We contend that this approach both shows the worth of the general approach and its applicability to the legal domain.

In this paper we describe AGATHA, a program designed to automate the process of theory construction in case based domains. Given a seed case and a number of precedent cases, the program uses a set of argument moves to generate a search space for a dialogue between the parties to the dispute. Each move is associated with a set of theory constructors, and thus each point in the space can be associated with a theory intended to explain the seed case and the other cases in the domain. The space is large, and so a heuristic search method based on A* is used to guide the selection of moves and precedent cases. The paper describes a series of experiments designed to explore the appropriateness of different evaluation functions, the most useful precedents and the quality of the resulting theories.

In this paper we discuss the application of a new machine learning approach - argumentation based machine learning - to the legal domain. Argumentation based machine learning is particularly suited to law as it makes use of the justifications of decisions to guide its learning. Importantly, where a large number of decided cases are available, it provides a way of identifying which need to be considered, so that only decisions which will have an influence are examined.

In this paper we consider legal reasoning as a species of practical reasoning. As such it is important both that arguments are considered in the context of competing, attacking and supporting arguments, and that the possibility of rational disagreement is accommodated. We present two formal frameworks for considering systems of arguments: the standard framework of Dung, and an extension which relates arguments to values allowing for rational disagreement. We apply these frameworks to modelling a body of case law, explain how the frameworks can be generated to reconstruct legal reasoning in particular cases, and describe some tools to support the extraction of the value related knowledge required from a set of precedent cases.

In this note we report a project exploring the notion of reasoning with legal cases as a process of theory construction, evaluation and application, as described in work by Bench-Capon and Sartor.

In this paper we attempt to give an account of reasoning with legal cases contextualised within a general theory of persuasion in practical reasoning. We begin by presenting our general theory, concentrating on the variety of ways in which a particular position can be attacked. We then apply our theory to the legal domain, illustrating our approach by a case study based on the well known CATO system. From this we conclude that it is possible to see reasoning with legal cases as a particular instantiation of our general theory. We identify some points of interest for discussion, and conclude by stating our intended directions for future work.

In many cases of disagreement, particularly in situations involving practical reasoning, it is impossible to demonstrate conclusively that either party is wrong. The role of argument in such cases is to persuade rather than to prove, demonstrate or refute. Following Perelman, we argue that persuasion in such cases relies on a recognition that the strength of an argument depends on the social values that it advances, and that whether the attack of one argument on another succeeds depends on the comparative strength of the values advanced by the arguments concerned. To model this we extend the standard notion of Argumentation Frameworks (AFs) to Value-based Argumentation Frameworks (VAFs). After defining VAFs we explore their properties, and show how they can provide a rational basis for the acceptance or rejection of arguments, even where this would appear to be a matter of choice in a standard AF. In particular we show that in a VAF certain arguments can be shown to be acceptable however the relative strengths of the values involved are assessed. This means that disputants can concur on the acceptance of arguments, even when they differ as to which values are more important, and hence that we can identify points for which persuasion should be possible. We illustrate the above using an example moral debate. We then show how factual considerations can be admitted to our framework and discuss the possibility of persuasion in the face of uncertainty and disagreement as to values.

This paper presents and motivates an extended ontology knowledge model which explicitly represents semantic information about concepts. This knowledge model is grounded on the meta-properties of formal ontological analysis and it results from enriching the usual conceptual model with semantic information which precisely characterises the concept's properties and expected ambiguities, including which properties are prototypical of a concept and which are exceptional, the behaviour of properties over time and the degree of applicability of properties to subconceptsThis enriched conceptual model permits a precise characterisation of what is represented by class membership mechanisms and helps a knowledge engineer to determine, in a straightforward manner, the meta-properties holding for a concept. Meta-properties are recognised to be the main tool for a formal ontological analysis that allows building ontologies with a clean and untangled taxonomic structure. Moreover, this enriched semantics facilitates the development of reasoning mechanisms on the state of affairs that instantiates the ontologies. Such reasoning mechanisms can be used in order to solve ambiguities that can arise when ontologies are integrated and one needs to reason with the integrated knowledge.

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.

In this paper we describe an approach to reasoning with cases which takes into account the view that case law evolves through a series of decisions. This is in contrast to approaches which take as a starting point a set of decided cases, with no account taken of the order in which they were decided. The model of legal reasoning we follow is based on Levi's account which shows how decided cases often need to be reinterpreted in the light of subsequent decisions, so that features of cases wax and wane in importance. Our aim is to reproduce the arguments that could have been used in a given case, rather than to apply a retrospective understanding of the law to them. A second novel feature is that we use a general purpose ontology to describe the cases, rather than one developed specifically to model the pertinent cases. The paper describes a prototype implementation, and uses an example to illustrate how our approach works. After this case by case description we make some remarks on the insights gained, and draw some conclusions.

Knowledge fusion refers to the process of locating and extracting knowledge from multiple, heterogeneous on-line sources, and transforming it so that the union of the knowledge can be applied in problem-solving. The KRAFT project has defined a generic agent-based architecture to support fusion of knowledge in the form of constraints expressed against an object data model. KRAFT employs three kinds of agent: facilitators locate appropriate on-line sources of knowledge; wrappers transform heterogeneous knowledge to a homogeneous constraint interchange format; mediators fuse the constraints together with associated data to form a dynamically-composed constraint satisfaction problem, which is then passed to an existing constraint solver engine to compute solutions. The paper presents the KRAFT architecture and the three kinds of agent, and includes a description of a demonstration KRAFT application in the domain of telecommunications service provision.

In this paper, the author gives some preliminary examination of the ways in which an ontology—an explicit specification of the conceptualization of the domain—can support the verification and validation of a knowledge-based system. The discussion is focused on a simple, well-known, example relating to the identification of animals. Key elements of the support provided by the ontology relate to attempting to give coherence to the domain conceptualization; making the role of experts in verification and validation more structured and less at the mercy of interpretation; constraining the number of test cases required to give good coverage of the possible cases; and structuring the testing to give better assurance of its efficacy, and a possible basis for greater automation of the testing process. Finally, the author makes some concluding remarks. © 2001 John Wiley & Sons, Inc.

In this paper we discuss the role of explicit specifications of domain conceptualisations - now popularly called "ontologies" - in legal information systems. We describe the advantages that accrue from producing an ontology for such systems, as well as the ontologies so far developed which are directed at the legal domain. We then illustrate how taking an ontological perspective can give insight into what is common and what is disparate in apparently different approaches. We conclude by offering some findings as to the nature of ontologies for legal information systems, and some ideas concerning the creation of a library of legal ontologies. 1. Introduction In the last five years there has been a growing recognition of the need to create explicit specifications of how knowledge in a domain is conceptualised. Such an explicit specification is now normally termed an "ontology" in the literature, following Gruber (1992). Whether this term is helpful or not - certainly it cannot be unders...

In this article we present a four-phased method for the development of legal knowledge systems.We set out from the well-studied CommonKADS method for the development of knowledge systems and tailor this method to the legal domain.In particular, we propose a generic legal ontology, and describe the creation of statute-specific ontologies to make the method more suitable for our purposes.In the construction of these ontologies we start from a theoretical analysis of the legal domain.The well-known example of the Imperial College Library Regulations (ICLR) is used to illustrate the method.

Article PLAID: proactive legal assistance Share on Authors: T. J. M. Bench-Capon Department of Computer Science, The University of Liverpool, Liverpool, England Department of Computer Science, The University of Liverpool, Liverpool, EnglandView Profile , G. Staniford Department of Computer Science, The University of Liverpool, Liverpool, England Department of Computer Science, The University of Liverpool, Liverpool, EnglandView Profile Authors Info & Claims ICAIL '95: Proceedings of the 5th international conference on Artificial intelligence and lawMay 1995 Pages 81–88https://doi.org/10.1145/222092.222142Published:24 May 1995 15citation212DownloadsMetricsTotal Citations15Total Downloads212Last 12 Months2Last 6 weeks1 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

Abstract Verification and validation are terms that have been used for several years in software engineering, and there are now many verification and validation techniques, plus considerable experience and expertise in using them. However, applying these techniques to knowledge‐based system is not straightforward. The essential differences between conventional systems and knowledge‐based systems suggest that these techniques must be expanded and adapted, but new techniques are also needed. This article has two major goals: first, it makes some comparisons between verification and validation as found in traditional software engineering and knowledge‐based systems, pointing out what is special about the latter as compared with the former; second, it provides a framework for a discussion of the various European work on verification and validation of knowledge‐based systems. The perspective put forward in this article allows for a vast amound of work to be surveyed and analysed beyond the implementation level, by differentiating the symbol level and the knowledge level within a knowledge‐based system.

In this paper some experiments designed to explore the suitability of using neural nets to tackle problems of open texture in law are described. Three key questions are investigated: can a net classify cases successfully; can an acceptable rationale be uncovered by an examination of the net; and can we derive rules describing the problem from an examination of the net?

Legal KBSs are based on knowledge contained in legal texts such as legislation, regulations and case histories and the practice of domain experts charged with operationalising this legislation. Legal texts and their opertionalisation can be analysed using textual analysis tools which lead to the production of a rule base which can be manipulated to establish a desired goal. In this paper we describe an approach to the development of legal KBSs where the legal texts include visual conditions which do not lend themselves to simple interpretation using textual analysis tools. The approach focuses on the use of preprocessors to generate descriptors derived from the geometrical interpretation of the visual data in question. These descriptors can then be used as direct input to a KBS without the need to include complex mathematics, to which KBS representations are not well suited, within individual rules. The approach was developed as part of a much larger project concerned with the production of a legal KBS to advise the navigators of ocean going vessels on how best to avoid collision with other vessels as prescribed by international maritime law. A fragment of the legislation on which this KBS is based is used as an example.

No abstract available.

This paper discusses the role of deep models and deontic logic in legal expert systems. Whilst much research work insists on the importance of both these features, legal expert systems are being built using shallow models and no more propositional logic, and are claimed to be successful in use. There is then a prima facie conflict between findings of research and commercial practice, which this paper explores, and attempts to explain.

A view of human-human interaction is presented and its implications for human-computer interaction discussed. Two propositions are advanced. The first is that interaction with computer systems is properly seen as mediated discourse, more akin to reading books and notices than interpersonal dialogues. The second proposition is a consequence of this, namely that unconstrained natural language is unlikely to provide an ideal form of interface.

The roles played in an illocutionary act by models of the means of communication and the communicator are distinguished, and qualitative differences between the models appropriate in the two cases identified. Applied to human-computer interaction, this means that a user must have models of the computer both as a communications device and a communications medium, and of the system author as interlocutor.

INTRODUCIIONThe papct describes a project which began in April 1986 on the representation in logic of a large portion of the United Kingdom's welfare law; specifically the legislation relating to 'Supplementary Benefit'.The paper describes the nature of the legislation and the kinds of tasks which the ,epftsentation is intended to support, reports on the progress of the project so far, and describes some of the knowledge representation problems that have arisen and how we propose to overcome them.The principal aim of the paper is to elaborate on our use of logic programming techniques for applications in law, and on our view of how executable logical models of law can be used to support various aspects of legal reasoning.