Components of a legal expert system include the `inferencing mechanism’, the `knowledge base’, the application developer interface, the user interface and the user-supplied problem facts.
The inferencing mechanism of an expert system is independent of any particular application of the mechanism to an area of knowledge. The same inferencing mechanism may be able to be used to create advisory systems in epidemiology and in contract law. The inference mechanism may be thought of as the part of the expert system which processes the statements of legal knowledge in the knowledge-base, and the user-supplied facts concerning a particular problem. The inferencing mechanism may operate according to logical rules such as `if A is true and is B true and (A and B implies C) is true, then conclude C is true’. It may also implement procedural criteria such as the program always requiring a first goal of the system to be specified by the application writer, with the system always seeks to prove that that goal is true first. The inferencing mechanism may also embody certain tactical approaches, such as always testing simple rules first.
A common type of inferencing mechanism, backward-chaining (or `goal directed’) production rules, requires a knowledge-base in the form of statements of knowledge expressed as `IF premise THEN conclusion’ rules. It operates by requiring an initial goal (conclusion) to be specified. It seeks to prove that that goal is true, first by looking in its knowledge-base for a rule which has that goal as its conclusion. When it finds such a rule it seeks to establish that the premises of that rule are true. If it cannot establish that a premise is true, it first looks for a second rule which has that premise as its conclusion, and it then seeks to prove that the premises of that second rule are true, and so on. This is the `backward-chaining’ aspect. When it finds a premise which does not appear as the conclusion to another rule, it has no option but to ask the user for the value (true, false or otherwise) of that premise. The value of this premise, once established, can then be used to prove that the conclusions of any rule in which it appears as a premise are true. Rules are said to `fire’ when their conclusions are shown to be true.
Legal knowledge must be represented in a formalism appropriate to the inference mechanism. In the above example, it must be represented in `IF premise THEN conclusion’ form. Different inference mechanisms require different forms of knowledge representation. Writing legal knowledge in the appropriate formalism is what a person writing an application for a particular area of law must do.
With some expert systems, an application to a particular area of law is created by the application developer using a normal word-processing program to state the legal knowledge in the required formalism. However, some expert system development programs assist the application developer by providing simple methods of stating legal knowledge which do not require the application developer to remember all of the formal rules for how the knowledge must be stated. A legal expert system `shell’ is an expert system with an empty knowledge base. It consists of the inference mechanism, the corresponding requirements of the knowledge formalism, any developer interface which may be provided, and the user interface. A shell doesn’t deal with any particular area of law. The application developer uses the shell to develop applications in particular areas of law for use by end-users.
The user interface is that part of the program through which the end-user of a particular legal application communicates with the expert system. Most obviously, it is through the user interface that the user is asked questions about the particular problem which the user is attempting to solve, and is provided with answers by the system. However, a user interface may also provide the user with interpretative aids, such as definitions, to aid in the answering of questions, and provide explanations and justifications concerning the conclusions which the system has reached.
Last but not least are the facts of a specific problem situation, which are obtained from the end-user as needed by the system in operation. These are held in the working memory of the system, where they, and the contents of the knowledge-base, are processed to produce the inferred conclusions which the system is capable of deriving.