Abstract We explore the use of qualitative reasoning to predict the behaviour of a dynamical system given a change in one of its parameters based upon a qualitative representation of its bifurcation diagram. We present three algorithms to perform this task. The first algorithm generates a qualitative representation from a quantitative representation of the bifurcation diagram of the system. The second uses the qualitative representation to simulate the behaviour of the system given a sequence of parameter adjustments or perturbations. The third algorithm solves the opposite, control problem: it determines what parameters to change to take the system from an initial to a given goal state. The first algorithm segments a quantitative bifurcation diagram into MSs having the same qualitative behaviour. These MSs are then interconnected into a relational network. The network is used by the other two algorithms to simulate and plan the behaviour of the system from an initial situation. We present examples illustrating the qualitative representations and the behaviours of the simulation and planning algorithms for dynamical systems of one parameter.

Computer science graduates face unprecedented opportunities and unforeseen challenges in today's highly global economy. These students will have to work and to think with international perspectives and cultural awareness. In this paper, we report on our experiences organizing and teaching the Pacific Rim Summer Schools in Global Distributed Software Development. We describe the motivation for our focus, our summer school curricula and programs, provide information on the costs of organizing and running the summer schools, and examine the sustainability of our program. We conclude with a discussion of the role of such experiences in computer science curricula and in the education of American and international computer science professionals.

Internationalization of computer science education involves incorporating awareness, knowledge and skills of professional life in a global environment. Through an NSF CPATH1 grant we have established a Pacific Rim community of computer science departments, high tech industry and international programs exploring a new model of computer science education that focuses on the knowledge, skills and competencies necessary for professional success and leadership in a global context. This paper describes our progress in building an international community of computer science educators, as well as our efforts in curricular innovation and establishment of international summer schools. Internationalization of computer science education will help attract the best and brightest students and broaden the appeal of computer science to a much more diverse population. Computer science will be seen as a pathway to a career not in an isolated cubicle but in the wide-open world.

Abstract Broadcasting is the communication process whereby a message that is initially known by one site becomes known to all sites of a network. Messages are transmitted by calls from senders to sets of receivers. In path broadcasting, a site can call nonneighboring sites over paths in the network. A call occupies all sites and edges on the path followed by the call; thus, any calls made concurrently must be vertex‐disjoint. We discuss the design of sparse networks that allow path broadcasting to be completed in minimum time under three different path broadcast models. © 2004 Wiley Periodicals, Inc.

Article Burden of proof in legal argumentation Share on Authors: Arthur M. Farley Computer and Information Science, University of Oregon, Eugene, OR Computer and Information Science, University of Oregon, Eugene, ORView Profile , Kathleen Freeman Computer and Information Science, University of Oregon, Eugene, OR Computer and Information Science, University of Oregon, Eugene, ORView Profile Authors Info & Claims ICAIL '95: Proceedings of the 5th international conference on Artificial intelligence and lawMay 1995 Pages 156–164https://doi.org/10.1145/222092.222227Online:24 May 1995Publication History 32citation543DownloadsMetricsTotal Citations32Total Downloads543Last 12 Months18Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Diagnostic models of complex mechanisms are designed specifically to support diagnostic reasoning. Diagnostic reasoning is that aspect of troubleshooting which determines a set of component faults that can account for observed abnormalities in system performance. A system’s performance is evaluated by comparing observations of the system’s behavior with its functional specifications. As such, diagnostic models must incorporate elements of both function as well as behavior. We define two necessary properties of diagnostic models reflecting this outlook: architectural fidelity and functional adequacy. We then discuss two general principles of diagnostic modeling, based upon observations from expert protocols: model variables assume values relative to normal levels (the normality principle) and these values are propagated through the model to account for the production of single outputs (the single output principle). These two principles allow significant simplification in the definition of diagnostic models of complex mechanisms. We illustrate our approach to diagnostic modeling and reasoning with examples from xerography.

The focus of this paper is on the underlying knowledge base for an intelligent tutorial system for high‐school algebra problems. We present a model of problem solving flexible enough to account for a variety of problem‐solving behaviors and general enough to allow new problem domains to be defined easily. The model is based upon the analysis of protocols between students and expert tutors. We show how student errors can be monitored and remediated using the model, and we provide an approach to understanding problem difficulty that can be used to generate challenging problems and also provides a mechanism for planning their solution.

Until recently, artificial intelligence (AI) research on problem solving ignored issues of uncertainty. With a growing desire to apply research results in real-world contexts, such issues have begun to receive attention. Real-world contexts are inherently uncertain due to several factors, including incomplete and imprecise interpretation of environmental information, unreliable execution of plan actions, and unforeseen interactions among multiple agents. A theoretical framework is offered for addressing issues of problem solving under conditions of uncertainty. The model is a probabilistic generalization of the usual notion of problem space. An admissible forward-directed search algorithm is presented. The need for information-gathering operators to control state disunity and provide pragmatic focusing is established; a representation for such operators is proposed. Aspects of the model are compared to Markov processes and utility-based techniques of decision analysis. A discussion of the limitations of the model is given as well as suggestions for its application.

Abstract A network is immune to a set of failures if all message transfers between operative sites can be completed in the presence of such failures. A set of line failures is isolated if no two failing lines are incident to the same site. Several classes of isolated line failure immune (ILFI) networks are defined, including a class with fewest lines for a given number of sites. An algorithm is presented which turns an arbitrary tree into one of these minimum ILFI networks and computes routing tables for the new network.

Abstract The notion of isolated failure immune (IFI) networks is introduced. A network is an IFI network if and only if all message transfers between operating sites can be completed as long as all site and line failures are isolated. Failures occurring at any time are isolated if they are pairwise isolated. A pair of site failures is isolated if the down sites are not neighbors. A pair of line failures is isolated if the lines are not incident to a common site. A site and a line failure are isolated if the down line is not incident to a neighbor of the down site. We show that 2‐trees are minimum IFI networks. An algorithm is described which adds lines to an arbitrary tree network to produce an IFI network (a 2‐tree). The algorithm also determines routing tables which allow a simple calling protocol to complete all message transfers between operative sites under isolated failures. Specializations and generalizations of our notion of IFI networks are discussed and issues for future research are proposed.

Abstract Broadcasting refers to the process of message dissemination in a communication network whereby a message, originated by one member, becomes known to all members. Line broadcasting assumes that members may “switch‐through” any number of calls during a time unit. An algorithm is presented which produces a calling schedule completing line broadcasting in minimum time from any member of any tree. A discussion of three types of broadcasting and their associated minimum time networks is included.

In knowledge-based problem-solving systems, many problems will be solved by application of general solution plans, each capable of satisfying a class of goal states from a variety of initial states. A range of important issues which must be addressed in the design of such problem-solving systems is discussed. One area of concern deals with the representation, storage, and activation of general solution plans. Another is the efficient coordination of plan executions in circumstances which demand concurrent satisfaction of several goal states. A third area deals with issues related to time: the maintenance over time of the environment in states which allow or facilitate application of general solution plans and the solution of problems under time constraints. A general model within which these issues can be addressed is proposed.