Possible worlds explorer: Datalog and answer set programming for the rest of us

Sahil Gupta, Yi Yun Cheng, Bertram Ludäscher

Research output: Contribution to journalConference article

Abstract

Datalog and Answer Set Programming (ASP) are powerful languages for rule-based querying and constraint solving, respectively. We have developed Possible Worlds Explorer (PWE), an open source Python-based toolkit that employs Jupyter notebooks to make working with Datalog and ASP systems easier and more productive. PWE can parse output from different reasoners (Clingo and DLV) and then run analytical queries over all answer sets or “possible worlds” (PWs), e.g., to calculate relative frequencies of atoms across PWs or to hierarchically cluster PWs based on user-defined complexity and similarity measures. PWE also has support for well-founded Datalog models (from DLV) and temporal models that use a special state argument. Using simple Python functions, generic as well as user-definable presentation and visualization formats can be easily created, e.g., to display all PWs (world views), the unique three-valued well-founded model (partial views), and temporal models (timelines and time series). We provide containerized versions of PWE that can be run in the cloud or locally. We hope that in this way Datalog and ASP can be made more accessible for a wider audience.

Original languageEnglish (US)
Pages (from-to)44-55
Number of pages12
JournalCEUR Workshop Proceedings
Volume2368
StatePublished - Jan 1 2019
Event3rd International Workshop on the Resurgence of Datalog in Academia and Industry, Datalog 2.0 2019 - Philadelphia, United States
Duration: Jun 4 2019Jun 5 2019

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ASJC Scopus subject areas

  • Computer Science(all)

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