Giorgio P. Ingargiola

An Algorithm for the Solution of 0-1 Loading Problems

An enumerative algorithm is presented for the solution of 0-1 many-knapsack or loading problems. It is based on the principle that before a search is attempted as many decisions as possible should be made about inclusion or exclusion of objects from the knapsacks. This is accomplished by the introduction of a new ordering relation among the objects. This ordering relation, coupled with other relations we define, allows a drastic reduction in the extent of the search required to determine a solution.

A repository that supports teaching and cooperation in the introductory AI course

This paper describes the development of FLAIR (Flexible Learning with an Artificial Intelligence Repository), a repository of educational material and of a highly visual computing environment for use in laboratories associated with the introductory undergraduate Artificial Intelligence (AI) course. This repository supports sharing of pedagogic material and of development tools, and cooperation in their use, while allowing diversity in content and in use at different institutions. Thus the development of the repository has stressed the production of system tools, extensible object-oriented libraries, and strong programming frameworks. Some modules currently available are on Search and Automated Reasoning. Examples of the presentation techniques used are conceptual maps, hypertext, and graphic animations of algorithms. Initial experience in the use of the repository in teaching the introductory AI course is taking place in the 1993/94 academic year.

A unified approach to online assistance

Many interactive computer systems have some form of HELP or assistance commands. Effective online assistance requires a well-defined framework that addresses the needs of both the end-user and the assistance provider. This paper presents such a framework, whose generality and usefulness come from an application-independent assistance processor and a highly structured database of assistance information. Major considerations are (1) the types of assistance interactive users need, (2) the data structures and relationships required to provide comprehensive assistance, (3) software architectures that encourage and support effective forms of assistance, and (4) the programming effort required to include and maintain online assistance. To make online assistance effective and economically feasible, the paper proposes a way to integrate assistance into other phases of the software life cycle.

Finding Optimal Demand Paging Algorithms

A cost is defined for demand paging algorithms with respect to a formal stochastic model of program behavior. This cost is shown to exist under rather general assumptions, and a computational procedure is given which makes it possible to determine the optimal cost and optimal policy for moderate size programs, when the formal model is known and not time dependent. In this latter case it is shown that these computational procedures may be extended to larger programs to obtain arbitrarily close approximations to their optimal policies. In previous models either unwarranted information is assumed beyond the formal model, or the complete stochastic nature of the model is not taken into account.

Recent advances in user assistance

As interactive users find conventional methods of training and documentation inadequate, designers are providing systems with online reference information, descriptions of valid input, elaboration of error messages, and explanations of a systems behavior. This paper describes some existing commercial systems that offer online assistance and more experimental approaches by the research community. The following material was originally presented at the SIGSOC conference on Easier and More Productive Use of Computing Systems. An extended version will appear in a special issue of the IEEE Transactions on Systems, Man, and Cybernetics (Volume SMC-12, March/April, 1982), and is reprinted here with the permission of the IEEE.Online user assistance is now offered on commercial systems and is the subject of investigation in experimental settings. It is difficult to compare the advantages and limitations of different approaches because they vary along many dimensions and because there is no commonly accepted terminology. A grouping of these dimensions into major categories is a necessary first step towards more empirical evaluations. The major software-related features of online assistance appear to fall into four categories:b access method -- the way users can construct or enter requests for assistance;b data structure -- the manner in which different portions of assistance information are related to each other;b software architecture -- how assistance requests and their responses are communicated among a user, an operating system, application programs, and the assistance database; andb contextual knowledge -- how much information is retained about the assistance environment, including the user, the application, and the tasks being performed.

Students' use of animations for algorithm understanding

Our goal in this pilot study is to explore students’ behavior as they learn about two search algorithms, observing the role of algorithm animations. We find that alternative animations of the same algorithm may provide different information and facilitate different types of reasoning.

The introductory undergraduate AI course as observed on WWW

Most people who have taught the introductory undergraduate course in Artificial Intelligence agree that it is a difficult course to teach well. Recently attention has been directed to achieving a better understanding of the objectives, prerequisites, themes, pedagogic methodologies, topics, and support resources for this course [1]. Also, recently a number of professors who teach the course, acting independently have created and made available on the World Wide Web (WWW) simple Course Support Environments (CSEs) which include both pedagogic and administrative information.

Nonprogramming laboratory assignments for the introductory AI course using the FLAIR system

Undergraduate computer science students benefit from nonprogramming laboratory assignments which have them explore and experiment with algorithms and abstractions in a concrete learning environment. FLAIR, a repository of learning materials which supports the laboratory component of the introductory undergraduate AI course affords interesting nonprogramming assignments of this kind. In the following discussion the FLAIR system is described and three laboratory assignments which have been used with FLAIR, are presented.

Hierarchies and relations among data types

The aim of this report is to show that, within the limits of what we know how to do efficiently with computers, it is possible to design a system of types that, to a good degree, models the conceptual hierarchies that we use in our everyday discourse.