A principled approach to developing legal knowledge systems

In this article we present a principled, four-phased approach to the development of legal knowledge systems. We set out from the well-studied CommonKADS method for the development of knowledge systems and tailor this method to the legal domain. In particular, we propose a generic legal ontology, and describe the creation of statute-specific ontologies to adopt the method for building legal systems. In the construction of these ontologies, we start from a theoretical analysis of the legal domain. The well-known example of the Imperial College Library Regulations (ICLR) is used to illustrate the method.     

A practical approach to programming with assertions

Embedded assertions have been recognized as a potentially powerful tool for automatic runtime detection of software faults during debugging, testing, maintenance and even production versions of software systems. Yet despite the richness of the notations and the maturity of the techniques and tools that have been developed for programming with assertions, assertions are a development tool that has seen little widespread use in practice. The main reasons seem to be that (1) previous assertion processing tools did not integrate easily with existing programming environments, and (2) it is not well understood what kinds of assertions are most effective at detecting software faults. This paper describes experience using an assertion processing tool that was built to address the concerns of ease-of-use and effectiveness. The tool is called APP, an Annotation PreProcessor for C programs developed in UNIX-based development environments, APP has been used in the development of a variety of software systems over the past five years. Based-on this experience, the paper presents a classification of the assertions that were most effective at detecting faults. While the assertions that are described guard against many common kinds of faults and errors, the very commonness of such faults demonstrates the need for an explicit, high-level, automatically checkable specification of required behavior. It is hoped that the classification presented in this paper will prove to be a useful first step in developing a method of programming with assertions     

A programming approach to mathematics

This article presents an attempt to employ a programming approach to mathematical formalization. Six self-instructional units (120 instructional hours) have been developed for the Open University in Israel. This material was tried on three different types of populations: (a) bright high school students (11th–12th grade, A-level mathematics), (b) mathematics teachers with a programming background, and (c) university students studying mathematics, natural sciences and engineering. The results indicated that the programming approach is complementary to high school and university mathematics. The opportunity to explore, the algorithmic approach to mathematical operations and the visualization of mathematical objects and processes facilitates mathematical understanding and insights. It was most beneficial to the majority of the students who perform on a moderate level of abstraction. The course was less advantageous to those who have a very abstract way of perceiving mathematics or to those who are interested in mathematics only on a technical level. However, the vast majority of students gained new ways of understanding mathematical topics such as functions, curves, probability, game theory, recursive functions, envelopes, tiling, curve fitting, etc. They gained, as well, new strategies of using microcomputers to solve and further explore many mathematical problems which they could not solve before.     

A holistic and principled approach for the empty-answer problem

We propose a principled optimization-based interactive query relaxation framework for queries that return no answers. Given an initial query that returns an empty-answer set, our framework dynamically computes and suggests alternative queries with fewer conditions than those the user has initially requested, in order to help the user arrive at a query with a non-empty-answer, or at a query for which no matter how many additional conditions are ignored, the answer will still be empty. Our proposed approach for suggesting query relaxations is driven by a novel probabilistic framework based on optimizing a wide variety of application-dependent objective functions. We describe optimal and approximate solutions of different optimization problems using the framework. Moreover, we discuss two important extensions to the base framework: the specification of a minimum size on the number of results returned by a relaxed query and the possibility of proposing multiple conditions at the same time. We analyze the proposed solutions, experimentally verify their efficiency and effectiveness, and illustrate their advantages over the existing approaches.     

Easily solving dynamic programming problems in Haskell by memoization of hylomorphisms

Dynamic programming is a well-known algorithmic technique that solves problems by a combination of dividing a problem into subproblems and using memoization to avoid an exponential growth of the costs. We show how to implement dynamic programming in Haskell using a variation of hylomorphisms that includes memoization. Our implementation uses polymorphism so the same function can return the best score or the solution to the problem based on the type of the returned value.     

A compositional approach to multiparadigm programming

The author points out that although different aspects of a system require different approaches, programmers are confined to their language's one paradigm. Multiparadigm programming makes it possible to match the paradigm to the problem. The potential of paradigm composition as an approach to multiparadigm programming is explored. In paradigm composition, a multiparadigm program is written as a collection of single-paradigm programs. The single-paradigm programs are composed, which means that they execute concurrently and cooperatively to form the whole of a functioning system. Interactions between paradigms are defined at the conceptual levels of the participating paradigms. The case of a small prototype telephone network is considered as an example of paradigm composition.<>     

A configuration approach to parallel programming

This paper advocates a configuration approach to parallel programming for distributed memory multicomputers, in particular, arrays of transputers. The configuration approach prescribes the rigorous separation of the logical structure of a program from its component parts. In the context of parallel programs, components are processes which communicate by exchanging messages. The configuration defines the instances of these processes which exist in the program and the paths by which they are interconnected.

The approach is demonstrated by a toolset (Tonic) which embodies the configuration paradigm. A separate configuration language is used to describe both the logical structure of the parallel program and the physical structure of the target multicomputer. Different logical to physical mappings can be obtained by applying different physical configurations to the same logical configuration. The toolset has been developed from the Conic system for distributed programming. The use of the toolset is illustrated through its application to the development of a parallel program to compute Mandelbrot sets.     

A convex programming with nested constraints on the amount of resources

This paper investigates the resource allocation problem of maximizing a strictly concave objective function under nested constraints on a weighted amount of resources. The resources are distributed in a multi-dimensional space and the distribution gives a planner some reward. The constraints on the resources are set in such a way that a weighted amount of resources is limited in each level of subspace as well as in the whole space. We call such constraints nested. The purpose of this paper is to devise efficient methods for an optimal distribution of resources under the constraints. For the concave maximization problem, first we derive necessary and sufficient conditions for an optimal solution and, secondly, propose two methods to solve the problem. Both methods manipulate the so-called Lagrangian multipliers and make a sequence of feasible solutions so as to satisfy all the necessary and sufficient conditions. Numerical examination confirms that the proposed methods perform better than some well-known methods of nonlinear programming in terms of computational time.     

A mathematical programming approach to three-dimensional contact problems with friction

The present study is concerned with the numerical treatment of linear elastic contact problems with friction. Making the restriction that the contact surface is constant during the loading history, we obtain a variational inequality formulation, which justifies a finite element approximation. The finitedimensional problem so obtained is shown to be equivalent to a problem of mathematical programming—namely, a parametric linear complementarity problem (LCP) involving derivatives. A solution procedure for such problems has previously been suggested. In this paper, the conditions under which this procedure defines a unique solution map, which describes the evolution of contact stresses and displacements for a prescribed load history, are given. As an application of the theory presented, the indentation of a halfspace by a flat-ended square punch is considered. Both the loading cases of a vertical displacement of the punch that increases monotonically and the gradual removal of the same displacement are considered.     

A maximum likelihood approach to density estimation with semidefinite programming

Density estimation plays an important and fundamental role in pattern recognition, machine learning, and statistics. In this article, we develop a parametric approach to univariate (or low-dimensional) density estimation based on semidefinite programming (SDP). Our density model is expressed as the product of a nonnegative polynomial and a base density such as normal distribution, exponential distribution, and uniform distribution. When the base density is specified, the maximum likelihood estimation of the polynomial is formulated as a variant of SDP that is solved in polynomial time with the interior point methods. Since the base density typically contains just one or two parameters, computation of the maximum likelihood estimate reduces to a one- or two-dimensional easy optimization problem with this use of SDP. Thus, the rigorous maximum likelihood estimate can be computed in our approach. Furthermore, such conditions as symmetry and unimodality of the density function can be easily handled within this framework. AIC is used to choose the best model. Through applications to several instances, we demonstrate flexibility of the model and performance of the proposed procedure. Combination with a mixture approach is also presented. The proposed approach has possible other applications beyond density estimation. This point is clarified through an application to the maximum likelihood estimation of the intensity function of a nonstationary Poisson process.     

Pardis: a process calculus for parallel and distributed programming in Haskell

Parallel programming and distributed programming involve substantial amounts of boilerplate code for process management and data synchronisation. This leads to increased bug potential and often results in unintended non-deterministic program behaviour. Moreover, algorithmic details are mixed with technical details concerning parallelisation and distribution. Process calculi are formal models for parallel and distributed programming but often leave details open, causing a gap between formal model and implementation. We propose a fully deterministic process calculus for parallel and distributed programming and implement it as a domain-specific language in Haskell to address these problems. We eliminate boilerplate code by abstracting from the exact notion of parallelisation and encapsulating it in the implementation of our process combinators. Furthermore, we achieve correctness guarantees regarding process composition at compile time through Haskell’s type system. Our result can be used as a high-level tool to implement parallel and distributed programs.     

A constraint programming approach to cutset problems

We consider the problem of finding a cutset in a directed graph G=(V,E)$G=(V,E)$, i.e., a set of vertices that cuts all cycles in G  . Finding a cutset of minimum cardinality is NP-hard. There exist several approximate and exact algorithms, most of them using graph reduction techniques. In this paper, we propose a constraint programming approach to cutset problems and design a global constraint for computing cutsets. This cutset constraint is a global constraint over boolean variables associated to the vertices of a given graph and states that the subgraph restricted to the vertices having their boolean variable set to true is acyclic. We propose a filtering algorithm based on graph contraction operations and inference of simple boolean constraints, that has a linear time complexity in O(|E|+|V|)$O(|E|+|V|)$. We discuss search heuristics based on graph properties provided by the cutset constraint, and show the efficiency of the cutset constraint on benchmarks of the literature for pure minimum cutset problems, and on an application to log-based reconciliation problems where the global cutset constraint is mixed with other boolean constraints.     

A message passing approach to robot programming

We are developing a communication oriented interactive environment for robot programming. A set of independent software modules for robot motoring control, low level sensoring processing, kinematics simulation and world model construction has been implemented as separate tools. They have been merged in a software environment whose implementation is based on the operating system Accent and can be accessed through an advanced graphics workstation. Each module interacts with the others through message exchange; each module may be organized into new submodules as new operational behaviours belonging to its functional area become necessary.     

A low-level approach to improve programming learning

Universal Access in the Information Society - Learning to program is becoming a universally desired ability. Discovering better ways to teach programming and improving existing ones is essential to...     

A creative arts approach to computer programming

This paper describes how the creative arts can provide an alternative approach to computer programming that may benefit students in the humanities in general. It focuses on creative arts projects using text, graphics and music that allow students to encounter the same programming concepts from a number of different perspectives. It also discusses the importance of symbolic programming for the arts and how the creative arts can provide a particularly rich environment for developing approaches to object-oriented programming.Gary Greenberg received his B. A. and M. A. in Music from Stanford University and his Ph.D. in Music from Harvard University. His activities include creating compositional programming environments (Composing with Performer Objects, The Proceedings of the 1988 International Computer Music Conference) and developing ways of using the computer to learn about music through compositional activities (Music Learning-Compositional Thinking). He is currently manager of the Northwestern University Advanced Technology Group where he coordinates the Computer Media Studio and is exploring ways to use computer multimedia in instruction and research.     

A dynamic programming approach to trajectory estimation

An iterative equation based on dynamic programming for finding the most likely trajectory of a dynamic system observed through a noisy measurement system is presented; the procedure can be applied to nonlinear systems with non-Gaussian noise. It differs from the recently developed Bayesian estimation procedure in that the most likely estimate of the entire trajectory up to the present time, rather than of the present state only, is generated. It is shown that the two procedures in general yield different estimates of the present state; however, in the case of linear systems with Gaussian noise, both procedures reduce to the Kalman-Bucy filter. Illustrative examples are presented, and the present procedure is compared with the Bayesian procedure and with other estimation techniques in terms of computational requirements and applicability.     

A novel approach to multiparametric quadratic programming

Multiparametric (mp) programming pre-computes optimal solutions offline which are functions of parameters whose values become apparent online. This makes it particularly well suited for applications that need a rapid solution of online optimization problems. In this work, we propose a novel approach to multiparametric programming problems based on an enumeration of active sets and use it to obtain a parametric solution for a convex quadratic program (QP). To avoid the combinatorial explosion of the enumeration procedure, an active set pruning criterion is presented that makes the enumeration implicit. The method guarantees that all regions of the partition are critical regions without any artificial cuts, and further that no region of the parameter space is left unexplored.     

A mathematical programming approach to salary administration

This paper discusses a mathematical programming approach to designing an executive salary administration guide based on the importance of the positions and the quality of job performance. The two fundamental concepts of the salary system that is considered in this study are the compensation plan and the classification plan. The purpose of the compensation plan, and the objective of the paper, is to develop a salary guide for given results from the classification plan. The paper assumes an understanding of basic principles of job evaluation and some exposure to formulation of mathematical models. Emphasis is put on the formulation and linearization of the models, rather than on development of solution techniques that would exploit the particular structure of the models.     

A goal programming approach to job evaluation

Job evaluatio refers to a systematic determination of the relative values of jobs in an organization. Often, jobs are evaluated based upon subjective judgement. By considering each job to consist of certain levels of different job factors, this paper develops a goal programming model to evaluate various levels of job factors. The main constraints in such a formation are obtained by using some existing benchmark jobs. The model development and application in evaluating new jobs are illustrated by solving an example problem consisting of four factors and six levels of each factor.     

A sequential linear programming approach to solve mixed integer programming problems

The optimal operation of pumps in a large water supply system under time-of-use electricity rates is formulated as a mixed integer programming (MIP) problem. The problem is solved using an iterative linear programming (LP) scheme. The scheme is applied to an actual world problem, the City of Inglewood Water Supply System. Computational results are presented and termination criteria for the solution scheme are discussed.