George Vossos

The IKBALS project: Multi-modal reasoning in legal knowledge based systems

In attempting to build intelligent litigation support tools, we have moved beyond first generation, production rule legal expert systems. Our work integrates rule based and case based reasoning with intelligent information retrieval.When using the case based reasoning methodology, or in our case the specialisation of case based retrieval, we need to be aware of how to retrieve relevant experience. Our research, in the legal domain, specifies an approach to the retrieval problem which relies heavily on an extended object oriented/rule based system architecture that is supplemented with causal background information. We use a distributed agent architecture to help support the reasoning process of lawyers.Our approach to integrating rule based reasoning, case based reasoning and case based retrieval is contrasted to the CABARET and PROLEXS architectures which rely on a centralised blackboard architecture. We discuss in detail how our various cooperating agents interact, and provide examples of the system at work. The IKBALS system uses a specialised induction algorithm to induce rules from cases. These rules are then used as indices during the case based retrieval process.Because we aim to build legal support tools which can be modified to suit various domains rather than single purpose legal expert systems, we focus on principles behind developing legal knowledge based systems. The original domain chosen was theAccident Compensation Act 1989 (Victoria, Australia), which relates to the provision of benefits for employees injured at work. For various reasons, which are indicated in the paper, we changed our domain to that ofCredit Act 1984 (Victoria, Australia). This Act regulates the provision of loans by financial institutions.The rule based part of our system which provides advice on the Credit Act has been commercially developed in conjunction with a legal firm. We indicate how this work has lead to the development of a methodology for constructing rule based legal knowledge based systems. We explain the process of integrating this existing commercial rule based system with the case base reasoning and retrieval architecture.

An example of integrating legal case based reasoning with object-oriented rule-based systems: IKBALS II

Although case based reasoning (CBR) is still a research paradigm, it has been shown to offer significant advantages over conventional rulebased expert systems when attempting to reason through experience. This paper discusses extending an existing legal object-oriented/rulebased system to successfully handle CBR. In particular, we discuss the architecture of IKBALS II, a legal knowledge based system that performs statutory interpretation in the area of accident compensation. IKBALS II combines reasoning with rules and reasoning with previously tried precedent cases in order to determine if an injured employee is entitled to compensation under the Accident Compensation Act, 1985, Victoria. A worker is entitled to compensation only if the Accident Compensation Commission is satisfied that the worker falls within certain statutory definitions.

The Credit Act Advisory System (CAAS): conversion from an expert system prototype to a C++ commercial system

CAAS is a rule-based expert system, which provides advice on the Victorial Credit Act 1984. It is currently in commercial use, and has been developed in conjunction with a law firm. It uses an object-oriented hybrid reasoning approach. The system was initially prototyped using the expert system shell NExpert Object, and was then converted into the C++ language. In this paper we describe the advantages that this methodology has, for both commercial and research development.

Designing intelligent litigation support tools : The IKBALS perspective

In the legal domain, it is rare to find solutions to problems by simply applying algorithms or invoking deductive rules in some knowledge‐based program. Instead, expert practitioners often supplement domain‐specific knowledge with field experience. This type of expertise is often applied in the form of an analogy. This research proposes to combine both reasoning with precedents and reasoning with statutes and regulations in a way that will enhance the statutory interpretation task. This is being attempted through the integration of database and expert system technologies. Case‐based reasoning is being used to model legal precedents while rule‐based reasoning modules are being used to model the legislation and other types of causal knowledge. It is hoped to generalise these findings and to develop a formal methodology for integrating case‐based databases with rule‐based expert systems in the legal domain.

Improving Automated Litigation Support by Supplementing Rule-Based Reasoning with Case-Based Reasoning

We are currently developing concepts and software tools designed to aid legal practitioners in the process of statutory interpretation: the process of determining the meaning of a statute or regulation and applying it to a particular set of facts. This is being attempted through the integration of database and expert system technologies. Case-Based Reasoning (CBR) is being used to model legal precedents while Rule-Based Reasoning (RBR) modules are being used to model the legislation and other types of causal knowledge. It is hoped to generalise these findings and to develop a formal methodology for integrating case-based databases with rule-based expert systems in the legal domain.