Visual explanations of probabilistic reasoning

Erwig, Martin; Walkingshaw, Eric

doi:10.1109/vlhcc.2009.5295309

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…This work is an extension and consolidation of two earlier papers on explaining probabilistic reasoning [12,13]. Prior to these papers, we presented a domain-specific embedded language (DSEL) in Haskell for creating and manipulating (but not explaining) probabilistic values [10].…”

Section: Consider Only Cases Where Two Heads Have Been Flippedmentioning

confidence: 96%

“…That paper also provides the first examples of Probula's visual notation along with an extended discussion of philosophical research on explanations and a lengthier motivation for the story-telling metaphor than is provided here. We formalized a subset of the visual notation of Probula in [13] and also provided a formal semantics for a subset of the operations used for creating Probula explanations. That work also contained the first set of laws for transforming a Probula explanation into equivalent alternatives.…”

Section: Consider Only Cases Where Two Heads Have Been Flippedmentioning

confidence: 99%

“…In this paper, we improve and complete the formalization of the visual notation begun in [13] and provide a formal semantics for all of the supported operations. In particular, we formalize for the first time the branching operation and the selection of representative examples, and improve the treatment of grouping operations.…”

Section: Consider Only Cases Where Two Heads Have Been Flippedmentioning

confidence: 99%

“…In Section 2 we discussed our previous work on computing with probability distributions [10] and supporting and explaining probabilistic reasoning [12,13]. Although there are other languages that support computation with probabilistic values, such as IBAL [29] and the OCaml DSEL designed by Kiselyov and Shan [23], there does not seem to be any other work on explaining these computations.…”

Section: Related Workmentioning

confidence: 99%

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A visual language for explaining probabilistic reasoning

Erwig

Walkingshaw

2013

Journal of Visual Languages & Computing

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We present an explanation-oriented, domain-specific, visual language for explaining probabilistic reasoning. Explanation-oriented programming is a new paradigm that shifts the focus of programming from the computation of results to explanations of how those results were computed. Programs in this language therefore describe explanations of probabilistic reasoning problems. The language relies on a storytelling metaphor of explanation, where the reader is guided through a series of wellunderstood steps from some initial state to the final result. Programs can also be manipulated according to a set of laws to automatically generate equivalent explanations from one explanation instance. This increases the explanatory value of the language by allowing readers to cheaply derive alternative explanations if they do not understand the first. The language is comprised of two parts: a formal textual notation for specifying explanation-producing programs and the more elaborate visual notation for presenting those explanations. We formally define the abstract syntax of explanations and define the semantics of the textual notation in terms of the explanations that are produced.

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Section: Consider Only Cases Where Two Heads Have Been Flippedmentioning

confidence: 96%

Section: Consider Only Cases Where Two Heads Have Been Flippedmentioning

confidence: 99%

Section: Consider Only Cases Where Two Heads Have Been Flippedmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A visual language for explaining probabilistic reasoning

Erwig

Walkingshaw

2013

Journal of Visual Languages & Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…A major guiding principle for the design of the visual notation was the traceabilty of results. A different, but related form of tracing was also used in the explanation language for probabilistic reasoning problems [10].…”

Section: Related Workmentioning

confidence: 99%

Explanations for Regular Expressions

Erwig

Gopinath

2012

Fundamental Approaches to Software Engineering

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Abstract. Regular expressions are widely used, but they are inherently hard to understand and (re)use, which is primarily due to the lack of abstraction mechanisms that causes regular expressions to grow large very quickly. The problems with understandability and usability are further compounded by the viscosity, redundancy, and terseness of the notation. As a consequence, many different regular expressions for the same problem are floating around, many of them erroneous, making it quite difficult to find and use the right regular expression for a particular problem. Due to the ubiquitous use of regular expressions, the lack of understandability and usability becomes a serious software engineering problem. In this paper we present a range of independent, complementary representations that can serve as explanations of regular expressions. We provide methods to compute those representations, and we describe how these methods and the constructed explanations can be employed in a variety of usage scenarios. In addition to aiding understanding, some of the representations can also help identify faults in regular expressions. Our evaluation shows that our methods are widely applicable and can thus have a significant impact in improving the practice of software engineering.

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Semantics First!

Erwig

Walkingshaw

2012

Software Language Engineering

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Visual explanations of probabilistic reasoning

Cited by 8 publications

References 10 publications

A visual language for explaining probabilistic reasoning

A visual language for explaining probabilistic reasoning

Explanations for Regular Expressions

Semantics First!

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