Visualising Reasoning: What ATP Can Learn From CP | Zendy

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Visualising Reasoning: What ATP Can Learn From CP

Author(s) -

John Slaney

Publication year - 2012

Publication title -

electronic notes in theoretical computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.242

H-Index - 60

ISSN - 1571-0661

DOI - 10.1016/j.entcs.2012.06.006

Subject(s) - computer science , constraint programming , constraint (computer aided design) , representation (politics) , programming language , contrast (vision) , visualization , theoretical computer science , automated theorem proving , artificial intelligence , mathematics , mathematical optimization , geometry , politics , political science , stochastic programming , law

Tools for graphical representation of problems in automated deduction or of proof searches are rare and mostly primitive. By contrast, there is a more substantial history of work in the constraint programming community on information visualisation techniques for helping programmers and end users to understand problems, searches and solutions. Here we consider the extent to which concepts and tools from a constraint programming platform can be adapted for use with automatic theorem provers

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