Artificial intelligence and distance learning philosophy in support of PfP mandate

Computers have long been utilised in the legal environment. The main use of computers however, has merely been to automate office tasks. More exciting is the prospect of using artificial intelligence (AI) technology to create computers that can emulate the substantive legal jobs performed by lawyers, to create computers that can autonomously reason with the law to determine legal solutions, for example: structuring and support of Partnership for Peace (PfP) mandate. Such attempts have not been successful jet. Modelling the law and emulating the processes of legal reasoning have proved to be more complex and subtle than originally envisaged. The adoption by AI researchers specialising in law of new AI techniques, such as case based reasoning, neural networks, fuzzy logic, deontic logics and non-monotonic logics, may move closer to achieving an automation of legal reasoning. Unfortunately these approaches also suffer several drawbacks that will need to be overcome if this is to be achieved. Even if these new techniques do not achieve an automation of legal reasoning however, they will still be valuable in better automating office tasks and in providing insights about the nature of law. An idea to apply the technology of intelligent multi-agent systems to the computer aided learning (CAL) in law, is currently being developed as a research project by the author of this article (see e.g. [Antoliš, 2002.]). Similar projects are usually based on the most modern technologies of multimedia and hypermedia, as it was implemented in this article. The theoretical foundations of the design and architecture of intelligent system for decision support process in law and distance-learning environment are, however, at their early stage of development

A neural cognitive model of argumentation with application to legal inference and decision making

Formal models of argumentation have been investigated in several areas, from multi-agent systems and artificial intelligence (AI) to decision making, philosophy and law. In artificial intelligence, logic-based models have been the standard for the representation of argumentative reasoning. More recently, the standard logic-based models have been shown equivalent to standard connectionist models. This has created a new line of research where (i) neural networks can be used as a parallel computational model for argumentation and (ii) neural networks can be used to combine argumentation, quantitative reasoning and statistical learning. At the same time, non-standard logic models of argumentation started to emerge. In this paper, we propose a connectionist cognitive model of argumentation that accounts for both standard and non-standard forms of argumentation. The model is shown to be an adequate framework for dealing with standard and non-standard argumentation, including joint-attacks, argument support, ordered attacks, disjunctive attacks, meta-level attacks, self-defeating attacks, argument accrual and uncertainty. We show that the neural cognitive approach offers an adequate way of modelling all of these different aspects of argumentation. We have applied the framework to the modelling of a public prosecution charging decision as part of a real legal decision making case study containing many of the above aspects of argumentation. The results show that the model can be a useful tool in the analysis of legal decision making, including the analysis of what-if questions and the analysis of alternative conclusions. The approach opens up two new perspectives in the short-term: the use of neural networks for computing prevailing arguments efficiently through the propagation in parallel of neuronal activations, and the use of the same networks to evolve the structure of the argumentation network through learning (e.g. to learn the strength of arguments from data)

Arguing Using Opponent Models

Peer reviewedPostprin

Logics for modelling collective attitudes

We introduce a number of logics to reason about collective propositional attitudes that are defined by means of the majority rule. It is well known that majoritarian aggregation is subject to irrationality, as the results in social choice theory and judgment aggregation show. The proposed logics for modelling collective attitudes are based on a substructural propositional logic that allows for circumventing inconsistent outcomes. Individual and collective propositional attitudes, such as beliefs, desires, obligations, are then modelled by means of minimal modalities to ensure a number of basic principles. In this way, a viable consistent modelling of collective attitudes is obtained

Legal Ontologies for the spanish e-Government

The Electronic Government is a new field of applications for the semantic web where ontologies are becoming an important research technology. The e-Government faces considerable challenges to achieve interoperability given the semantic differences of interpretation, complexity and width of scope. In this paper we present the results obtained in an ongoing project commissioned by the Spanish government that seeks strategies for the e-Government to reduce the problems encountered when delivering services to citizens. We also introduce an e-Government ontology model; within this model a set of legal ontologies are devoted to representing the Real-estate transaction domain used to illustrate this paper

Intuitions and the modelling of defeasible reasoning: some case studies

The purpose of this paper is to address some criticisms recently raised by John Horty in two articles against the validity of two commonly accepted defeasible reasoning patterns, viz. reinstatement and floating conclusions. I shall argue that Horty's counterexamples, although they significantly raise our understanding of these reasoning patterns, do not show their invalidity. Some of them reflect patterns which, if made explicit in the formalisation, avoid the unwanted inference without having to give up the criticised inference principles. Other examples seem to involve hidden assumptions about the specific problem which, if made explicit, are nothing but extra information that defeat the defeasible inference. These considerations will be put in a wider perspective by reflecting on the nature of defeasible reasoning principles as principles of justified acceptance rather than `real' logical inference.Comment: Proceedings of the 9th International Workshop on Non-Monotonic Reasoning (NMR'2002), Toulouse, France, April 19-21, 200

Knowledge Cartography: Software tools and mapping techniques

Knowledge Cartography is the discipline of mapping intellectual landscapes.The focus of this book is on the process by which manually crafting interactive, hypertextual maps clarifies one’s own understanding, as well as communicating it.The authors see mapping software as a set of visual tools for reading and writing in a networked age. In an information ocean, the primary challenge is to find meaningful patterns around which we can weave plausible narratives. Maps of concepts, discussions and arguments make the connections between ideas tangible and disputable. With 17 chapters from the leading researchers and practitioners, the reader will find the current state–of-the-art in the field. Part 1 focuses on educational applications in schools and universities, before Part 2 turns to applications in professional communitie

An Object-Oriented Approach to Knowledge Representation in a Biomedical Domain

An object-oriented approach has been applied to the different stages involved in developing a knowledge base about insulin metabolism. At an early stage the separation of terminological and assertional knowledge was made. The terminological component was developed by medical experts and represented in CORE. An object-oriented knowledge acquisition process was applied to the assertional knowledge. A frame description is proposed which includes features like states and events, inheritance and collaboration. States and events are formalized with qualitative calculus. The terminological knowledge was very useful in the development of the assertional component. It assisteed in understanding the problem domain, and in the implementation stage, it assisted in building good inheritance hierarchies