Reasoning with worlds and truth maintenance in a knowledge-based programming environment

Filman, Robert E.

doi:10.1145/42404.42405

Cited by 64 publications

(15 citation statements)

References 5 publications

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“…The system has been extended [16] to include a context mechanism, referred to as KEEworlds TM. A world represents a set of related facts--typically a situation such as a belief set, a simulation checkpoint, or a hypothetical state of the problem solver.…”

Section: Worlds In the Knowledge Engineering Environment Keementioning

confidence: 99%

An integrating view on the viewing abstraction: Contexts and perspectives in software development, AI, and databases

Motschnig-Pitrik

1995

Journal of Systems Integration

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Abstract.Viewing entities from different situations and representing and processing them in different contexts constitutes a fundamental concern in various disciplines of computer science. Not surprisingly, the viewing abstraction is supported by many languages and techniques employed either for programming or "world modelling". This paper presents an overview on various manifestations of viewing mechanisms in formal notations including software development techniques, knowledge representation languages, and data models. The concepts of context and perspective are introduced in form of a language-independent framework in order to capture and systematically discuss features that characterize viewing mechanisms, such as the relationship between the two, the relation between different perspectives on the same conceptual entity, or operations supporting effective construction of contexts. In addition, it is argued that the full power of viewing can be exploited by supporting both notions: contexts as well as perspectives. In order to achieve this support, any formal notation has to fulfill a number of general requirements which are stated as a result of the investigation and the survey.

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Section: Worlds In the Knowledge Engineering Environment Keementioning

confidence: 99%

An integrating view on the viewing abstraction: Contexts and perspectives in software development, AI, and databases

Motschnig-Pitrik

1995

Journal of Systems Integration

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“…SIGMA provides a mechanism for scientists to implement one model at a time, but does not allow them to construct multiple models simultaneously or to compare results across models. We have begun to implement a kind of "multiple worlds" mechanism (Filman, 1988;Guha, 1991) that will permit users to have this type of capability.…”

Section: Multiple Modelsmentioning

confidence: 99%

A knowledge-based prototyping environment for construction of scientific modeling software

1994

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Over the past 30 years, scientific software models have played an increasingly prominent role in the conduct of science. Unfortunately, scientific models can be difficult and time-consuming to implement, and there is little software engineering support specifically available for constructing scientific models. Because these models are not easily specified to scientifically-naive programmers, and because the scientist requires intimate knowledge of the code to conduct experiments, many scientists implement their own models. This coding activity takes valuable time away from their primary scientific mission.We have developed a knowledge-based software development tool that assists scientists in prototyping scientific models. With a specialized graphical user interface, the scientist constructs a high-level visual specification that captures the essential computational dependencies in the desired model. The system uses its scientific domain knowledge to ensure that the model being built is consistent and coherent. The final product is an executable prototype of a scientific model. Our tool accelerates the model-building process and eliminates the scientist's need to program in a formal language. Furthermore, the models developed with this tool are easier to understand and reuse than typical low-level scientific modeling code. At present, models developed with our system are restricted to those involving non-coupled algebraic and first order ordinary differential equations. Research is ongoing to lessen this restriction and support models with simultaneous equations.

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“…Some tools providing nonmonotonic reasoning features are presently available for practical development of knowledge-based systems, such as for example, negation in logic programming, defaults in semantic networks, and frame-based systems. In this respect, it is worth mentioning the so-called truth-maintenance systems (de Kleer, 1986) as a vehicle for the implementation of nonmonotonic reasoning, especially as they are becoming available in commercial environments for the development of knowledge-based systems (Filman, 1988). Nevertheless, it is clear that a major aspect of the research in nonmonotonic reasoning is now concerned with the impiementation of the proposed methods.…”

Section: Vxbird(x) a ~Ostrich(x) D Flies(x)mentioning

confidence: 99%

Nonmonotonic reasoning

et al. 1990

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The research on nonmonotonic reasoning includes several attempts to formalize reasoning that refuse to acknowledge one of the fundamental properties of classical logic: monotonicity. Nonmonotonic formalizations of reasoning deal with the problem of drawing conclusions when the description of either a situation or a problem is incomplete. Such incompleteness can be justified not only by epistemological reasons, since the knowledge about a situation can often be partial, but also by engineering reasons, as the need arises to avoid the explicit recording of a huge amount of information. In this paper we describe the main formalizations of nonmonotonic reasoning with the aim to present a coherent overview of the research in this field, and therefore we pay special attention to the problem of establishing relationships among different proposals.

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Reasoning with worlds and truth maintenance in a knowledge-based programming environment

Abstract: In traditional knowledge-based system development environments, the fundamental representational building blocks are mechanisms such as frames, rules, and attached procedures. The KEE system has been extended to include both a context (worlds) system and a truth maintenance system.

Cited by 64 publications

References 5 publications

An integrating view on the viewing abstraction: Contexts and perspectives in software development, AI, and databases

An integrating view on the viewing abstraction: Contexts and perspectives in software development, AI, and databases

A knowledge-based prototyping environment for construction of scientific modeling software

Nonmonotonic reasoning

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