Intelligent Decision Support Systems for Laws Drafting

Law is a set of rules or norms of conduct which mandate, proscribe or permit specified relationships among people and organizations, provide methods for ensuring the impartial treatment of such people, and provide punishments for those who do not follow the established rules of conduct. Drafting regulations and policies are a complicated process requiring a careful coordination and understanding of available norms and ethics. Knowledge-based methods are used to model various aspect of rules, norms, and drafting procedures. They also have capabilities of representing laws and judgment under uncertainty. Querying mechanisms are also supported by the knowledge base. This research is aimed to identify and explain the useful resources for legal authorities in drafting documents and regulations. Mechanisms of Drafting Laws Drafting laws are intelligent and complicated process exhibited by any legal system. The drafting process uses a knowledge-based mechanism in order to facilitate such a process in a more effective way. Drafting is one of the most intellectually demanding of all lawyering skills. It requires a knowledge of the law, the ability to deal with abstract concepts, investigative instincts, an extraordinary degree of prescience, and organizational skills [1]. The knowledge-based system is often applied to general purpose computers and also in the field of scientific investigation into the theory and practical application of AI. Using knowledge-based method will help clients intaking initial and subsequent instructions, drafting pleading and correspondence, breparing draft for counsel, preparing final reports to clients, drafting appropraite statutory declarations and submissions, preparing proofs of evidence of witnesses, prepartion of pleas and submissinons, preparing wills, preparing applications for probate and letters of administration. Modern knowledge-based research is concerned with producing useful machines to automate human tasks requiring tedius and intelligent behavior. Examples include: scheduling resources such as military units, answering questions about regulations and policies for lawyers, understanding and transcribing speech, and recognizing meaning of sentenses. As such, it has become an engineering discipline, focused on providing solutions to practical problems. Knowledge-based and AI methods were used to schedule units in the first Gulf War, and the costs saved by this efficiency have repaid the US government's entire investment in KB research since the 1960s. KB and AI systems are now in routine use in many businesses, hospitals and military units around the world, as well as built into common home computer software such as Microsoft Office and video games. Knowledge-based methods are often employed in cognitive sceince research, which explicitly tries to model subsystems of human cognition. Expectations of AI research is far beyond its current capabilities. For this reason, many AI researchers say they work in cognitive science, informatics, statistical inference or information engineering in an attempt to distance themselves from such charlatanism. AI has seen many research paradigms, including symbolic, connictionist and Bayesian approaches. There is still no consensus as to the best way to proceed. Recent fashionable research areas include Bayesian Networks and Artificial life. A nother mechanism that is incorporated into KB is Bayesian network or Bayesian belief network. Bayesian Network is a directed acyclic graph of nodes representing variables and arcs representing dependency relationship among the variables. If there is an arc from node A to another node B, then we say that A is a parent of B. If a node has a known value, it is said to be an evidence node. A node can represent any kind of variable, be it an observed measurement, a parameter, a latent variable, or a hypothesis. Nodes are not restricted to representing random variables; this is what is "Bayesian" about a Bayesian network. A Bayesian network is a representation of the joint distribution over all the variables, for example, policy or regulation, represented by nodes in the graph. Let the policies be X1, ..., Xn. Let parents(A) be the parents of the node A. Then the joint distribution for X1 through Xn is represented as the product of the probability distributions p(Xi | parents(Xi)) for i from 1 to n. If X has no parents, its probability distribution is said to be unconditional, otherwise it is conditional. In order to carry out numerical calculations, it is necessary to further specify for each node X the probability distribution for X conditional on its parents. The distribution of X given its parents may have any form. However, it is common to work with discrete or Gaussian distributions to simplify calculations. The goal of inference is typically to find the conditional distribution of a subset of the variables, conditional on known values for some other subset (the evidence), and integrating over any other variables. Thus a Bayesian network can be considered a mechanism for automatically constructing extensions of Bayes’ theorem to more complex problems. Drafting Regulations We need to establish a good cooperation among decision makers in the Arab world. Modeling and finding a common background on law drafting is an essential requirement. We need a system to help finding conflicting rules and laws within the Arab world law drafting system. More importantly, there are no cooperative efforts among these nations despite their common membership in the Association of Arab League. Instead, a majority of the countries are actively participating in the World Intellectual Property Organization’s initiatives in this space. It is therefore likely that the outcome of the activities will set the pace of policy development as well as its content with whatever model law acting as the template for national legislation on traditional knowledge. Knowledge-based System A knowledge-based system consists of the following components as shown in figure (1). These include [2]: 1. User interface: the tool by which the user and the knowledge-based system communicate. 2. Explanation facility: explains the reasoning of the system to the user. 3. Working memory: a global database of facts used by the rules. 4. Inference engine: makes inferences by deciding which rules are satisfied by facts or objects, prioritizes the satisfied rules, and executes the rule with the highest priority. 5. Agenda: a prioritized list of rules created by the inference engine, whose patterns are satisfied by facts or objects in working memory. 6. Knowledge acquisition facility: an automatic way for the user to enter knowledge in the system instead of having the knowledge engineer explicitly codes the knowledge. Knowledge is a collection of specialized facts, procedures, and judgment rules. Figure (1) shows a number of types of knowledge, which may come from one source or from several sources. It can be collected from human experts, pictures, maps, flow diagrams, stories, sensors, and/or observed behavior. Knowledge can also be identified and collected by using human senses or can be collected by machines using sensors, scanners, pattern matchers, and/ or intelligent agents . Figure (1): component of a knowledge-based system INFERENCE ENGINE