Generic programming, partial evaluation, and a new programming paradigm

Landauer, Christopher; Bellman, Kirstie L.

doi:10.1109/hicss.1999.772896

Cited by 37 publications

(16 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We are investigating several fundamental issues in intelligent systems, using some earlier theoretical work on the organization of language and movement processes [7] [9], and on the structure of constructed complex systems mediated or integrated by software [17] [20] [23]. We are not nearly as interested in whether or not these (or any other) autonomous systems "really" have a symbolic model of their environment as we are interested in what our explanatory models of their behavior have to contain and describe.…”

Section: Introductionmentioning

confidence: 99%

Symbol systems in constructed complex systems

Landauer¹,

Bellman²

1999

Proceedings of the 1999 IEEE International Symposium on Intelligent Control Intelligent Systems and Semiotics (Cat. No.99CH3701

Self Cite

View full text Add to dashboard Cite

This paper is about a particular technical problem in the study of autonomy in constructed complex systems: how can a computing system decide that it needs a new symbol system to re-express all of its own processes?We have shown earlier that a constructed complex system will need to change its symbol system, by proving two theorems that limit what it can do with a fixed symbol system. To change that phenomenon, we need construction mechanisms that can themselves be changed and elaborated.In this paper, we show how a constructed complex system can decide that it needs to change its symbol system, and how it can do it.We think that if we can change the construction mechanisms (which include the alphabet, as the mechanisms for constructing expressions from nothing), that is, the basic units that combine or are combined to make the structures, then we have another way to escape the trap of creeping rigidity: we can elaborate the units out from underneath the existing structures. This brings us directly to Computational Semiotics, which for us is the study of the treatment and use of symbols in computing systems. This paper is primarily intended to stimulate discussion and further research in these areas, since the questions are very hard and have no easy answers.

show abstract

Section: Introductionmentioning

confidence: 99%

Symbol systems in constructed complex systems

Landauer¹,

Bellman²

1999

Proceedings of the 1999 IEEE International Symposium on Intelligent Control Intelligent Systems and Semiotics (Cat. No.99CH3701

Self Cite

View full text Add to dashboard Cite

show abstract

“…It seems clear that flexibility in plans is partly the result of their incompleteness: if the detailed goals remain partly unspecified, then there are more possible steps to take. This phenomenon shows up in programming as "late binding", in which a resource used to address a problem is often selected just before use (as in our Wrapping approach to heterogeneous system integration in Constructed Complex Systems [ 131). The delaying of these decisions does, of course, conflict with rapid execution, and the resulting tradeoff is important and depends essentially on rapid elaboration and evaluations of the choices.…”

Section: Adaptibilitymentioning

confidence: 93%

Some measurable characteristics of intelligent computing systems

Landauer¹,

Bellman²

SMC 2000 Conference Proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics. 'Cybernetics Evolving to S

Self Cite

View full text Add to dashboard Cite

We discuss the following measurable characteristics of intelligent behavior in computing systems: (1) speed and scope of adaptibility to unforeseen situations, including recognition, assessment, proposals, selection, and execution; (2) rate of effective learning of observations, behavior patterns, facts, tools, methods, etc., which requires identification, encapsulation, and recall; (3) accurate modeling and prediction of the relevant external environment, which includes the ability to make more effective abstractions; (4) speed and clarity of problem identification and formulation; (5) effective association and evaluation of disparate information; (6) identification of more important assumptions and prerequisites; (7) use of symbolic language, including the range and use of analogies and metaphors (this is about identification of similarities), and the invention of symbolic language, which includes creating effective notations. We make no claim that these are all the important characteristics; discovering others is the point of our research program.

show abstract

“…We have also defined a "Wrapping expression notation" wrex, which can be used to describe our models in a way that is convenient for interpretation, and to define the interpreters as well [29]. It alleviates many of the problems of rigid language definition [41], because it is semantically neutral, relying on the collection of provided resources to define the semantics.…”

Section: Wrapping Expression Notationmentioning

confidence: 99%

“…In this subsection, we describe the Problem Posing Interpretation [29] [30], which is a uniform style of interpreting all programming and modeling paradigms and languages in a declarative way; it separates posed problems from the resources required to study them. This change of attitude greatly simplifies the remaining discussion, and leads to much flexibility in our Constructed Complex Systems.…”

Section: Problem Posingmentioning

confidence: 99%

See 1 more Smart Citation

Wrappings for one-of-a-kind software development

Landauer

Bellman

Proceedings of the 35th Annual Hawaii International Conference on System Sciences

Self Cite

View full text Add to dashboard Cite

System engineering practices have reduced the rate and effect of errors in complex, heterogeneous computing systems, but there are still areas in which they are insufficient. Space systems and other research systems have unique problems that preclude the use of conventional techniques, since the requirements are not all available until after the system is nearly finished.For these systems, the requirements are discovered along the way. That requires a more explicit account of the design decisions and rationales than is usually written down, and a much more explicit connection between the requirements, the design decisions, and the appearance of the code. These conditions also need to be flexible enough to change as new requirements are discovered.In this paper, we show how our Problem Posing Programming Paradigm supports this flexibility of connection, and how some related verification techniques can be used to address the problem, and point towards further development.

show abstract

Generic programming, partial evaluation, and a new programming paradigm

Cited by 37 publications

References 10 publications

Symbol systems in constructed complex systems

Symbol systems in constructed complex systems

Some measurable characteristics of intelligent computing systems

Wrappings for one-of-a-kind software development

Contact Info

Product

Resources

About