692 research outputs found

    How much of commonsense and legal reasoning is formalizable? A review of conceptual obstacles

    Get PDF
    Fifty years of effort in artificial intelligence (AI) and the formalization of legal reasoning have produced both successes and failures. Considerable success in organizing and displaying evidence and its interrelationships has been accompanied by failure to achieve the original ambition of AI as applied to law: fully automated legal decision-making. The obstacles to formalizing legal reasoning have proved to be the same ones that make the formalization of commonsense reasoning so difficult, and are most evident where legal reasoning has to meld with the vast web of ordinary human knowledge of the world. Underlying many of the problems is the mismatch between the discreteness of symbol manipulation and the continuous nature of imprecise natural language, of degrees of similarity and analogy, and of probabilities

    Large-Scale Legal Reasoning with Rules and Databases

    Get PDF
    Traditionally, computational knowledge representation and reasoning focused its attention on rich domains such as the law. The main underlying assumption of traditional legal knowledge representation and reasoning is that knowledge and data are both available in main memory. However, in the era of big data, where large amounts of data are generated daily, an increasing rangeof scientific disciplines, as well as business and human activities, are becoming data-driven. This chapter summarises existing research on legal representation and reasoning in order to uncover technical challenges associated both with the integration of rules and databases and with the main concepts of the big data landscape. We expect these challenges lead naturally to future research directions towards achieving large scale legal reasoning with rules and databases

    An inquiry into the development of expert systems in legal reasoning

    Get PDF
    The first goal of this paper is to review some of the steps necessary in developing a system that reasons effectively in some domain of law. The paper will begin by addressing the issues of domain selection, domain analysis and knowledge acquisition, knowledge representation, and selection of an inference method. After presenting a brief argument against using rule-based reasoning as the primary mode of inference, the paper will go on to expound the virtues of Kevin Ashley\u27s HYPO, a software model of case based legal argument. It will conclude with a short description of my experience implementing part of a software model of legal reasoning

    Task-adaptable, Pervasive Perception for Robots Performing Everyday Manipulation

    Get PDF
    Intelligent robotic agents that help us in our day-to-day chores have been an aspiration of robotics researchers for decades. More than fifty years since the creation of the first intelligent mobile robotic agent, robots are still struggling to perform seemingly simple tasks, such as setting or cleaning a table. One of the reasons for this is that the unstructured environments these robots are expected to work in impose demanding requirements on a robota s perception system. Depending on the manipulation task the robot is required to execute, different parts of the environment need to be examined, the objects in it found and functional parts of these identified. This is a challenging task, since the visual appearance of the objects and the variety of scenes they are found in are large. This thesis proposes to treat robotic visual perception for everyday manipulation tasks as an open question-asnswering problem. To this end RoboSherlock, a framework for creating task-adaptable, pervasive perception systems is presented. Using the framework, robot perception is addressed from a systema s perspective and contributions to the state-of-the-art are proposed that introduce several enhancements which scale robot perception toward the needs of human-level manipulation. The contributions of the thesis center around task-adaptability and pervasiveness of perception systems. A perception task-language and a language interpreter that generates task-relevant perception plans is proposed. The task-language and task-interpreter leverage the power of knowledge representation and knowledge-based reasoning in order to enhance the question-answering capabilities of the system. Pervasiveness, a seamless integration of past, present and future percepts, is achieved through three main contributions: a novel way for recording, replaying and inspecting perceptual episodic memories, a new perception component that enables pervasive operation and maintains an object belief state and a novel prospection component that enables robots to relive their past experiences and anticipate possible future scenarios. The contributions are validated through several real world robotic experiments that demonstrate how the proposed system enhances robot perception

    From meaning to morality in Kovesi and Harrison

    Get PDF
    The chapter shows that Bernard Harrison and Julius Kovesi are complementary thinkers, interested in similar questions, and arriving at closely comparable answers. It summarizes the theory of concepts and meaning that they shared and the way they have used this theory to make sense of morality

    A Relatedness Analysis Tool for Comparing Drafted Regulations and Associated Public Comments

    Get PDF
    corecore