#pyVivid

pyVivid is an implementation of the theoretical framework, Vivid, described in the paper here (built entirely in python 2.7). Vivid provides a framework for mechanized natural deduction capable of combining two different forms of reasoning; the first of which being traditional symbolic logic and the second being so called "diagrammatic" logic. More specifically, Vivid allows one to combine the reasoning processes used by people when reasoning about diagrams (e.g. a driver knows both the meaning of and how to identify a stop sign without any formal deduction) with traditional logic (e.g. first-order logic, propositional logic etc.) to produce succinct yet powerfully explanatory proofs. Vivid can also deal with reasoning over incomplete information (e.g. in the case of a partially filled in diagram).

• Sen, A., Bringsjord, S., Marton, N., Licato, J. (2015) “Toward Diagrammatic Automated Discovery in Axiomatic Physics” Logic, Relativity and Beyond 2nd International Conference
• Atriya Sen, Matthew Peveler, Nick Marton, Rikhiya Ghosh, John Licato, Richard J. Radke, Tianna-Kaye A. E. Woodstock, Selmer Bringsjord "Towards the Cognitive Classroom: Mathematical Physics" Rensselaer-IBM Cognitive Colloquium