A Logic for Statutes

Rule-based reasoning (RBR) and case-based reasoning (CBR) are two complementary alternatives for building knowledge-based “intelligent” decision-support systems. RBR and CBR can be combined in three main ways: RBR first, CBR first, or some interleaving of the two. The NEST system, described in this paper, allows us to invoke both components separately and in arbitrary order. In addition to the traditional network of propositions and compositional rules, NEST also supports binary, nominal, and numeric attributes used for derivation of proposition weights, logical (no uncertainty) and default (no antecedent) rules, context expressions, integrity constraints, and cases. The inference mechanism allows use of both rule-based and case-based reasoning. Uncertainty processing (based on Hájek's algebraic theory) allows interval weights to be interpreted as a union of hypothetical cases, and a novel set of combination functions inspired by neural networks has been added. The system is implemented in two versions: stand-alone and web-based client server. A user-friendly editor covering all mentioned features is included.

Case-based reasoning and rule-based reasoning are two techniques using the experience to resolve problems in fixture design. Each method has some shortcoming when it is employed separately. A hybrid method of case-based and rule-based reasoning can eliminate the drawbacks of each method and provide a better way for fixture design. In this paper, a retrieval mechanism of case-based reasoning in fixture design is analyzed. An improved algorithm taking the influence of fixture structure into account is proposed for calculating the similarity of fixture cases. A reuse mechanism of fixture cases is discussed from different levels and different groups. The expanded conception of fixture case is proposed. Moreover, variation level and variation method based-on the expanded fixture cases are studied. Finally, a real case is presented to demonstrate that the hybrid of case-based reasoning and rule-based intelligent variation approaches can improve the re-usability of the fixture cases.

The relative contribution of two traditional, widely used preadmission measures, undergraduate grade point average (UGPA) and the Law School Admission Test (LSAT), to predicting academic performance in each of the three years of law school was investigated in a heterogeneous sample of 23 law schools. The contribution of each measure was uneven across years, with the LSAT making its greatest contribution in early years and UGPA in later years. Differences between law school years with respect to the nature of instructional techniques and curricular emphases were considered in order to gain a better understanding of the construct validity of each measure as a predictor of performance in law school. The results were consistent with the test sponsor's interpretation of the LSAT as a measure of the ability to confront new situations and new problems such as those encountered in the first year of law school. They were also consistent with an interpretation of UGPA as a measure not only of academic competence but also of other qualities (such as persistence and motivation) that are needed over the long haul for the study of law. The author wishes to acknowledge the Law School Admission Council for its support of the project on which this paper is based. Thanks go to Franklin R. Evans for helpful suggestions in conducting the research; to Thomas F. Donlon, Kenneth M. Wilson, and three anonymous reviewers for thoughtful reviews of this paper; to Miriam Godshalk for helpful editorial suggestions; and to the admissions staff of the participating schools for providing the data. Special thanks go to Frieda Hardy and Marie Ormond for managing the data collection activities; to Christopher Constantini, Katherine Kornhauser, and Norma Norris for processing and analyzing the data; to Henry Braun for statistical consultation; and to Lorraine Simon for her careful attention to numerous administrative tasks and to the final preparation of this manuscript.

We consider an approach to automatic knowledge acquisition through machine learning based on integration of two basic paradigms of reasoning – case-based and rule-based reasoning. Case-based reasoning allows to use high-performance database technology for storing and accumulating cases, while rule-based reasoning is the most developed technology for creating declarative knowledge on the basis of strong logical inference. We also propose an improvement of classification algorithm through extraction of fuzzy rules from cases. We have obtained higher classification accuracy for various membership functions and for sequentially reducing amount of training sample by application of special strategies for expanding the scope of fuzzy rules in the control sample classification.

Bridging the Gap: How Introducing Ethical Skills Exercises Will Enrich Learning in First Year Courses

Due to difficult to building the complete rule base and low efficiency of rule-base reasoning (RBR), an approach combines the case-based reasoning (CBR) and fuzzy Petri net (FPN) model for conceptual design is proposed. Firstly, the similar cases are retrieved by CBR method from case base. The revision rules are established and represented by FPN. Secondly, mathematical principle of FPN is used to reason, which is beneficial to overcoming the disadvantage of low efficiency in RBR and helpful to case revision. The presented method could help designers, by referencing previous design cases, to make case revise quickly and finish the design requirement during product design. Finally, the implementation of proposed system is outlined and case study has been used to demonstrate the applicability and efficiency of the proposed approach.

Say What? Teaching the Oral Research Paper

Haley Moss was diagnosed with autism at age three. At the time, her parents were told that achievements such as obtaining a driver's license, graduating from high school, or even making friends were unlikely. Even after she proved the experts wrong and gained acceptance to law school, Moss saw continued challenges for students with disabilities. "I remember in my first year of law school, there was a blind student in my section, but she did not return for the second semester-and I wondered why," she told us.1 "Law school is not always as accessible as it could be for people with disabilities."2 But those people are essential to the functioning of our legal system, she added: "We need all types of minds to get all kinds of jobs done."3.

The first year of law school is chock-full of new experiences. Students can become overwhelmed in the face of so much change that they forget why they chose law school as a career path. They often lose sight of their end goal and need a touchstone to ground them back to their initial interests. Legal Research is the ideal first-year class to connect the acquisition of legal skills with interesting topics in a way that encourages students’ engagement in their present and future learning. By adding a student-selected subject context to their class, Legal Research instructors can incorporate contextualized learning and active learning techniques that improve student engagement and student learning. This article discusses the transformation of a generic, required, first-year legal research course into one that divides into four small (18–20 students) classes, each with one of four topics: patent, trademark and copyright, traditional practice, and social justice. In addition, it envisions future innovations to improve student engagement and student learning.

After studying the common defects and faults of transformers, we design and propose a fast fault diagnosis system for transformer equipment based on CBR(Case-Based Reasoning) and RBR(Rule-Based Reasoning). In the system, test cases are firstly matched with the cases. If there are similar cases, CBR is used to obtain the diagnosis conclusion. If there are no similar cases, the rule reasoning module based on FTA (Fault Tree Analysis) is launched. This module subdivides transformer fault modes into winding, core, bushing, on-load tap changer, cooling system, non-electric protection and other fault modes, and forms rules. Based on this situation, a transformer equipment fault tree is established to serve the rapid diagnosis system. The structural characteristics and fault modes of transformer windings, iron cores, bushings, on-load tap changers, cooling systems, non-electrical protection and other equipment are subdivided. And then, a fast fault diagnosis model and system for transformer equipment are established. Finally, the case study proved that the system can diagnose and analyse the equipment faults based on the comprehensive multi-source data of transformers, and the diagnosis conclusion is reliable and practical, which has provided support for realizing the state maintenance of transformers.

This paper contains an informal introduction to a theory about legal reasoning (reason-based logic) that takes the notion of a reason to be central. Arguing for a conclusion comes down to first collecting the reasons that plead for and against the conclusion, and second weighing them. The paper describes how we can establish the presence of a reason and how we can argue whether the reasons for or the reasons against the conclusion prevail. It also addresses the topic of meta-level reasoning about the use of rules in concrete cases. It is shown how both rule-based reasoning and case-based reasoning are naturally incorporated in the theory of reason-based logic.

Land cover interpretation using multisensor remote sensing images is an important task that allows the extraction of information that is useful for several applications. However, satellite images are usually characterized by several types of imperfection, such as uncertainty, imprecision, and ignorance. Using additional sensors can help improve the image interpretation process and decrease the associated imperfections. Fusion methods such as the probability, possibility, and evidence methods can be used to combine information coming from these sensors. An extensive literature has accumulated during the last decade to resolve the issue of choosing the best fusion method, particularly for satellite images. In this paper, we present a semiautomatic approach based on case-based reasoning (CBR) and rule-based reasoning, allowing intelligent fusion method retrieval. This approach takes into account the advantage of data stored in the case base, allowing a more efficient processing and a decrease in image imperfections. The proposed approach incorporates three modules. The first is a learning module based on evaluating three fusion methods (probability, possibility, and evidence) applied to the given satellite images. The second looks for the best fusion method using CBR. The last is devoted to the fusion of multisensor images using the method retrieved by CBR. We validate our approach on a set of optical images coming from the Satellite Pour l'Observation de la Terre 4 and radar images coming from European Remote Sensing Satellite 2 (ERS-2) representing a central Tunisian region.

We present a new method that integrates rule induction and case-based reasoning. The method is new in two aspects. First, it applies a novel feature weighting function for assessing similarities between cases. By using this weighting function, optimal case retrieval is achieved in that the most relevant cases can be retrieved from the case base. Second, the method handles both classification and numeric prediction tasks under a mixed paradigm of rule-based and case-based reasoning. Before problem solving, rule induction is performed to induce a set of decision rules from a set of training data. The rules are then employed to determine some parameters in the new weighting function. The induced rules are also used to detect possible noise in the training set so that noisy cases are not used in case-based reasoning. For classification tasks, rules are applied to make decisions; if there is a conflict between matched rules, case-based reasoning is performed. The method was implemented in ELEM2-CBR, a learning and problem solving system. We demonstrate the performance of ELEM2-CBR by comparing it with other methods on a number of designed and real-world problems.

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.

A case-based reasoning approach to real estate property appraisal