Kirstie L. Bellman

Generic programming, partial evaluation, and a new programming paradigm

We describe in this paper a new approach to Generic Programming that combines our integration results with Partial Evaluation methods for adaptation. Our approach supports Partial Evaluation by providing much more information than is usually available, including explicit meta-knowledge about the program fragments and their intended execution environments. We make some ambitious claims here, so we provide some detail about our methods, to justify our interest and expectations. We are not claiming to have solved the problem; only that we think our methods circumvent some of the know difficulties that were previously identified or encountered in approaches to Generic Programming.

The modeling issues inherent in testing and evaluating knowledge-based systems

Abstract Computer science has always been concerned with the problem of organizing and representing knowledge to make it effectively computable. Expert systems or knowledge-based systems (KBSs)1 are not dramatic departures from other computer programs. Rather, they bring together and extend a number of the innovations and concerns that characterize modern computer science. However, like the classes of special-application programs before them or like programs written in new languages, they avoid some of the errors with which we are familiar and create new ones that we need to discover. Part of our ability to specify and test such programs adequately depends upon our ability to discover as quickly as possible (a) the kinds of errors and problems that their new constructs, goals, and processing qualities introduce and (b) the methods, analyses, and tests that will allow one to identify these errors and to fix them.

Self-modeling systems

This paper is about systems with complete models of themselves (down to some very low level of detail). We explain how to build such a system (using careful system engineering, and our Wrapping approach to flexible integration infrastructures for Constructed Complex Systems), and why we want to do so (it is at least interesting, and we believe it is essential for effective autonomy). The long-term goal is the use of these models to understand modeling processes, so that computing systems can be built that can do their own modeling and construct their own abstractions, which we believe is important for computational intelligence.

An approach to integrating and creating flexible software environments supporting the design of complex systems

In research on VEHICLES, a conceptual design environment for space systems, an approach is being developed for flexibility and integration based on the collection and then processing of explicit qualitative descriptions of all the software resources in the environment. The detailed descriptions (or metaknowledge) of the resources are used by the system to help partially automate the combination, selection, and adaptation of tools and models to the particular requirements of the user and the type of problem being solved. The author examines the types of flexibilities and integrative processes necessary to a design environment, especially the services available in an environment to help the user select, integrate, adapt, and explain the software resources in that environment (intelligent user support functions); the authors approach to providing these services by the processing of explicit descriptions of the software resources (wrapping); and VSIM, a simulation built to study the nature of the wrappings, wrapping processors, and different software architectures.<<ETX>>

Wrappings for software development

Constructed complex systems are heterogeneous software and hardware systems that have to function in complex environments. Building and managing such a system requires explicit infrastructure that includes models of the system, its architecture, and its environment. We describe wrapping, our knowledge-based integration infrastructure, and show by example how the meta-knowledge that wrappings contain, and the expressive uniformities that result from stepping up to a meta-level, lead to much cleaner descriptions of many software processes. We describe our problem posing interpretation of programming languages, and the corresponding wrapping expression notation wrex, and show its use both for programming the internal details of a system and for describing a system lifecycle process. We apply our methods to two examples: migration of disparate database systems into a common standard, and the process of software disintegration, which identifies models of components of software and should be part of any software or system re-engineering process.

Problem posing interpretation of programming languages

In this paper, we describe a programming paradigm that changes the focus of programming from solution methods for certain application problems to the specification of the problems themselves, leaving the mapping from the problem specification to the computational resources that will provide or coordinate the solution to one or more separate (and possibly external) information files, knowledge bases, or other processes. The Problem Posing Interpretation is a declarative programming paradigm that uses Knowledge-Based Polymorphism to unify the interpretation of all programming languages. We describe examples from all major programming paradigms, to justify this claim.

Theoretical Biology: Organisms and Mechanisms

The Theoretical Biology Program initiated by Robert Rosen is intended to identify the key theoretical characteristics of organisms, especially those that distinguish organisms from mechanisms, by looking for the proper abstractions and defining the appropriate relationships. There are strong claims about the distinctions in Rosen’s book “Life Itself”, along with some purported proofs of these assertions. Unfortunately, the Mathematics is incorrect, and the assertions remain unproven (and some of them are simply false). In this paper, we present the ideas of Rosen’s approach, demonstrate that his Mathematical formulations and proofs are wrong, and then show how they might be made more successful.

Designing testable, heterogeneous software environments

Abstract Over the last 8 years, our group has focused on developing techniques for designing, testing, and evaluating several new computer technologies, including knowledge-based systems (KBSs). However, even as we speak, the technologies that we need to contend with are changing; rarely to these new technologies come alone. Instead, we are in an era where the problems we are working on demand large software environments with tool sets and libraries composed of often very different types of components. We see fuzzy controllers combined with knowledge bases and neural nets, and all of these combined with standard graphic programs, user interfaces, computer algorithms, spreadsheet programs, editors, data base management systems, etc. In this article we introduce a methodology for constructing large heterogeneous software environments in such a way as to make them “testable” and maintainable. The article is divided into two parts. First, we introduce our “wrapping” approach to engineering software environments. In wrapping, we create machine-processable descriptions for all the software resources in a system. These descriptions include not only the usual protocol and input requirements for applying a software resource, but also metaknowledge about the appropriate content for applying a resource and adapting a resource to different problems. In the second part of the article, we briefly review the verification and validation methods we have developed for testing KBSs, discuss how these methods can be directly applied to the data bases that hold the “wrappings,” and use the verification and validation methods to analyze a simple example.

Interwoven Systems: Self-Improving Systems Integration

Current trends in information and communication technology show that systems are increasingly influencing each other -- which is seldom completely anticipated at design-time. As a result, mastering system integration with traditional methods becomes infeasible due to the resulting complexity. In this paper we argue that self-improving system integration is the most promising solution to counter the resulting challenges. Thereby, we highlight the different aspects of such a process with special attention to the optimisation question and discuss how approaches from the domain of self-organising systems -- in particular Organic and Autonomic Computing -- will be beneficial when researching possible solutions.

Playing in the mud: Virtual worlds are real places

This paper is about how developers will know whether intelligent virtual environments (IVEs) are appropriate for the tasks set to them. There are several important research questions that need to be answered before they can even begin to build IVEs for some of the more promising applications, such as entertainment, education, collaboration on research and development, military, and other training. The main technical point is that every aspect of any IVE involves model, and the modeling needs to be addressed very directly and explicitly. This paper is more for the developers of such systems and the potential customers, than for the users. More is written about what is important to consider as one contemplates, for example, a difficult collaborative task, than about what it will be like to live in these IVEs. The authors believe that IVEs must be expanded to become virtual worlds (VWs), and that the modeling sophistication required for any of the serious applications described above is much more than is currently available in the VWs seen, even the ones with good graphics. Finally, the connection between Virtual Reality and CyberSpace is made, and the authors explain their expectations will be explained for the future of CyberSpace.