A visual language for explaining probabilistic reasoning

Abstract

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.

Towards a visual guide for communicating uncertainty in Visual Analytics

“…Using such a visual language and combining building blocks of graphical and textual elements, users can intuitively define behavioural requirements for algorithms [40] and have the process of probabilistic reasoning explained to them in an easy-to-understand manner [41]. This way, non-experts may gain insight and understanding into an otherwise "black box" system [42].…”

Domain-Specific Probabilistic Programming with Multiverse Explorer

“…Previous work in our group has demonstrated that visualisation of causal graphs can assist with construction and interpretation of programs in a PPL [17], and others have shown that live visualisation of distributions can be integrated into a probabilistic database for data science applications [26]. Following the pioneering work of Erwig and Walkingshaw [15], our group has explored whether interactive visualisation of causal graphs, probability distributions, and Monte Carlo simulations offer educational benefits in the teaching of probability [1], [4].…”

Improving error notification comprehension in IDEs by supporting developer self-explanations

“…One possible avenue is to adapt techniques by Erwig and Walkingshaw, who describe a textual notation for specifying the semantics of an explanation-producing program, coupled with a visual notation for presenting the explanation [8].…”