This paper presents a formalism for representing knowledge in the legal domain with an explicit account of time. We do not propose an original contribution on temporal representation, but we present a temporal representation method for Legal Reasoning Systems based on some techniques proposed in the area of temporal logics for AI which happen to adequately fulfill some other representational requirements from the legal domain as well.

A central notion of our approach is that of temporal token. It allows to make direct reference to specific temporal occurrences, supports talking about temporal occurrence without the technical problems of reification, avoids the ontological problems of temporal types, allows for a neat separation of the temporal component - which eventually will be processed by a specialized temporal resoner -, and turns out to be very appropriate for expressing references to legal propositions, which is an important issue in representing legal knowledge, while staying within standard many-sorted first-order logics.

In this work we combine logic programming and temporal constraint processing techniques. We propose TCLP, which augments logic programs in Temporal Constraint Networks are applied. We identify a decidable fragment called, Simple TCLP, which can be viewed as extending Datalog with limited functions to accomodate intervals of occurrence and temporal constraints between them. Some of the restrictions introduced by Simple TCLP are overcome by a syntactic structure which provides with the benefits of reification. The latter allows quantification on temporal occurrences and relation symbols.

Time is fundamental in representing and reasoning about changing domains. A proper temporal representation requires characterizing two notions: (1) time itself, and (2) temporal incidence, i.e. the domain-independent properties for the truth-value of fluents and events through-out time. Formally defining them involves some problematic issues such as (i) the expression of instantaneous events and instantaneous holding of fluents, (ii) the dividing instant problem and (iii) the formalization of the properties for non-instantaneous holding of fluents.

This paper discusses how previous attempts fail to address all these issues and presents a simple theory of time and temporal incidence which satisfactorily overcomes all of them.

Our theory of time, called IP, is based on having instants and periods at equal level. Our theory of temporal incidence is defined upon IP. Its key insight is the distinction between continuous and discrete fluents.

Despite the ubiquity of time and temporal references in legal texts, their formalization has often been disregarded or addressed in an ad hoc manner. We address this issue from the standpoint of the research done in temporal representation and reasoning in AI. We identify the temporal requirements of legal domains and propose a temporal representation framework for legal reasoning independent of (i) the underlying representation language and (ii) the specific legal reasoning application. The approach is currently being used in a rule-based language for an application in commercial law.