Causal Reasoning with Neuron Diagrams

Erwig, Martin; Walkingshaw, Eric

doi:10.1109/vlhcc.2010.23

Cited by 10 publications

(14 citation statements)

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“…In these models, explanations are represented by directed graphs with variables as nodes (typically representing events or states), where an edge leads from X to Y if X has a direct effect on Y . In our previous work we have worked extensively with one such representation called neuron diagrams [27], formalizing and extending the language in [14] and designing a Haskell DSEL for creating and analyzing neuron diagrams in [36].…”

Section: The Story-telling Model Of Explanationsmentioning

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: The Story-telling Model Of Explanationsmentioning

confidence: 99%

“…The philosophical study of causation is in turn a large area of research, which we have discussed briefly in Section 3, and at greater length in our previous work [12,14]. Of particular interest are the various visual notations that have been used in this research for representing graphs of causally related events [16,27,37].…”

Section: Related Workmentioning

confidence: 99%

A visual language for explaining probabilistic reasoning

Erwig

Walkingshaw

2013

Journal of Visual Languages & Computing

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“…Walkingshaw and Erwig [24,7] have developed an EDSL for neuron diagrams [17], a formalism in philosophy that can model complex causal relationships between events, similar to how premises and exceptions determine a conclusion in an argument. Walkingshaw and Erwig extend this model to work on non-boolean values, while at the same time providing an implementation, thereby unifying formal description and actual implementation.…”

Section: Related Workmentioning

confidence: 99%

Haskell Gets Argumentative

Gijzel

Nilsson

2013

Lecture Notes in Computer Science

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Abstract. Argumentation theory is an interdisciplinary field studying how conclusions can be reached through logical reasoning. The notion of argument is completely general, including for example legal arguments, scientific arguments, and political arguments. Computational argumentation theory is studied in the context of artificial intelligence, and a number of computational argumentation frameworks have been put forward to date. However, there is a lack of concrete realisations of these frameworks, which hampers research and applications at a number of levels. We hypothesise that the lack of suitable domain-specific languages in which to formalise argumentation frameworks is a contributing factor. In this paper, we present a formalisation of a particular computational argumentation framework, Carneades, as a case study with a view to investigate the extent to which functional languages are useful as a means to realising computational argumentation frameworks and reason about them.

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“…That is, given a graph g and list of input values as, the outputs of this function are the values of the terminal neurons in the diagram D g as. We call this the firing semantics of the graph [2], but it is important to stress (and we do so repeatedly throughout this paper) that the firing semantics of a graph does not uniquely determine the causal relationships encoded in that graph. That is, the internal structure of a neuron graph is significant; graphs do not simply reduce to multifunctions.…”

Section: Graph Majororders == Graph Bothordermentioning

confidence: 99%

“…We also introduce our first extension to the language of neuron diagrams, used to distinguish neurons that are potential causes from those that are not. The formal definition of our cause inference algorithm can be found in our previous work [2].…”

Section: Effects (Graph Trump) == Effects (Graph Bothorder)mentioning

confidence: 99%

A DSEL for Studying and Explaining Causation

Walkingshaw

Erwig

2011

Electron. Proc. Theor. Comput. Sci.

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We present a domain-specific embedded language (DSEL) in Haskell that supports the philosophical study and practical explanation of causation. The language provides constructs for modeling situations comprised of events and functions for reliably determining the complex causal relationships that emerge between these events. It enables the creation of visual explanations of these causal relationships and a means to systematically generate alternative, related scenarios, along with corresponding outcomes and causes. The DSEL is based on neuron diagrams, a visual notation that is well established in practice and has been successfully employed for causation explanation and research. In addition to its immediate applicability by users of neuron diagrams, the DSEL is extensible, allowing causation experts to extend the notation to introduce special-purpose causation constructs. The DSEL also extends the notation of neuron diagrams to operate over non-boolean values, improving its expressiveness and offering new possibilities for causation research and its applications.

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Causal Reasoning with Neuron Diagrams

Cited by 10 publications

References 14 publications

A visual language for explaining probabilistic reasoning

A visual language for explaining probabilistic reasoning

Haskell Gets Argumentative

A DSEL for Studying and Explaining Causation

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