This book, which has appeared October 1997, is a revised and extended version of my Doctoral dissertation `Logical Tools for Modelling Legal Argument', which I defended on 14 January 1993 at the Free University Amsterdam. The first five chapters of the thesis have remained almost completely unchanged but the other chapters have undergone considerable revision and expansion. Most importantly, I have replaced the formal argument-based system of the old Chapters 6, 7 and 8 with a revised and extended system, which I have developed during the last three years in collaboration with Giovanni Sartor. Apart from some technical improvements, the main additions to the old system are the enrichment of its language with a nonprovability operator, and the ability to formalise reasoning about preference criteria. Moreover, the new system has a very intuitive dialectical form, as opposed to the rather unintuitive fixed-point appearance of the old system.
Another important revision is the split of the old Chapter 9 into two new chapters. The old Section 9.1 on related research has been updated and expanded into a whole chapter, while the rest of the old chapter is now in revised form in Chapter 10. This chapter also contains two new contributions, a detailed discussion of Gordon's Pleadings Game, and a general description of a multi-layered overall view on the structure of argumentation, comprising a logical, dialectical, procedural and strategic layer. Finally, in the revised conclusion I have paid more attention to the relevance of my investigations for legal philosophy and argumentation theory.
Assuming that logic can provide theoretical foundations for Artificial Intelligence research, this book has aimed at giving a logical analysis of two important aspects of legal reasoning which are sometimes believed to escape such an analysis: of defeasible reasoning, i.e. of reasoning with rules which are implicitly subject to exceptions, and of reasoning with inconsistent information. A secondary aim has been to clarify the role of logic in legal reasoning, particularly, to show that logic can also be useful in the analysis of noninferential kinds of reasoning, like analogical reasoning.
To break the ground for the main investigations of this book, I started in Chapter 2 with a discussion of the role of logic in legal reasoning. Both in legal theory and AI-and-law research the usefulness of logic in analysing legal reasoning has been disputed. It became apparent that some of the arguments raised against logic are based on misconceptions of what logic is and how it can be used. Other doubts on logic, however, turned out to be based on the idea that the kinds of reasoning which are traditionally studied by logic are the only ones which can be logically analyzed: particularly reasoning with rules which are subject to exceptions, and nontrivial reasoning with inconsistent information would fall outside the scope of a logical analysis. And since, because of the open, unpredictable nature of the world to which the law applies, and the many competing interests and opinions involved in legal disputes, these kinds of information are abundant in the legal domain, a logical analysis of legal reasoning would be of little use, so it is said.
In Chapter 3 we saw in detail that legal reasoning often indeed operates on defeasible and inconsistent information. In addition, we saw that the way in which legal texts separate general rules from exceptions cannot be accounted for with standard logical means. However, much of the rest of this book was devoted to showing that these phenomena do not escape a logical analysis at all, while, moreover, they are logically related to each other.
First, in Chapter 4, I gave a brief sketch of new logical developments on modelling the two investigated kinds of reasoning, most of which are the result of AI research on modelling common-sense reasoning. After that I studied the application of some of the new developments to the legal domain. It appeared that reasoning with rules which are subject to exceptions can be modelled in two ways: firstly, as reasoning with explicit exception clauses which are assumed false unless the contrary is shown, which method was investigated in Chapter 5; and secondly, as choosing the most specific of conflicting conclusions. After observing that this second way of modelling defeasible reasoning is in fact a special case of reasoning with inconsistent information, I made in the Chapters 6, 7 and 8 a contribution to the new logical developments themselves: I showed that both defeasible reasoning and inconsistency tolerant reasoning can be modelled as instances of the process of constructing and comparing arguments for incompatible conclusions. Although the main source of inspiration for this part of the research has been the legal domain, it is stated in a sufficiently general way to make it a contribution to general AI research on modelling common-sense reasoning.
In Chapter 9 it turned out that the system was an instance of a new, argument-based development in AI research on nonmonotonic reasoning, and in Chapter 10 I applied both this general approach and my particular system to various issues, in particular to issues in knowledge representation and implementation, to Toulmin's criticism of standard logic, and to the role of logic in noninferential forms of reasoning. Then I tried to justify the assumption of this book that logic can be used as a standard for implemented systems, in using my system, and the general idea of argumentation systems, in a logical analysis of some implemented AI-and-law systems. Finally, I discussed how from this book a four-layered picture of legal argumentation has emerged, connecting the logical, dialectical, procedural and strategic aspects of legal reasoning.