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plingo: A system for probabilistic reasoning in clingo based on lpmln

Susana Hahn, Tomi Janhunen, Roland Kaminski, Javier Romero, Nicolas Rühling, Torsten Schaub

TL;DR

The core of plingo amounts to a re-implementation of LP^MLN in terms of modern ASP technology, extended by an approximation technique based on a new method for answer set enumeration in the order of optimality.

Abstract

We present plingo, an extension of the ASP system clingo with various probabilistic reasoning modes. Plingo is centered upon LP^MLN, a probabilistic extension of ASP based on a weight scheme from Markov Logic. This choice is motivated by the fact that the core probabilistic reasoning modes can be mapped onto optimization problems and that LP^MLN may serve as a middle-ground formalism connecting to other probabilistic approaches. As a result, plingo offers three alternative frontends, for LP^MLN, P-log, and ProbLog. The corresponding input languages and reasoning modes are implemented by means of clingo's multi-shot and theory solving capabilities. The core of plingo amounts to a re-implementation of LP^MLN in terms of modern ASP technology, extended by an approximation technique based on a new method for answer set enumeration in the order of optimality. We evaluate plingo's performance empirically by comparing it to other probabilistic systems.

plingo: A system for probabilistic reasoning in clingo based on lpmln

TL;DR

The core of plingo amounts to a re-implementation of LP^MLN in terms of modern ASP technology, extended by an approximation technique based on a new method for answer set enumeration in the order of optimality.

Abstract

We present plingo, an extension of the ASP system clingo with various probabilistic reasoning modes. Plingo is centered upon LP^MLN, a probabilistic extension of ASP based on a weight scheme from Markov Logic. This choice is motivated by the fact that the core probabilistic reasoning modes can be mapped onto optimization problems and that LP^MLN may serve as a middle-ground formalism connecting to other probabilistic approaches. As a result, plingo offers three alternative frontends, for LP^MLN, P-log, and ProbLog. The corresponding input languages and reasoning modes are implemented by means of clingo's multi-shot and theory solving capabilities. The core of plingo amounts to a re-implementation of LP^MLN in terms of modern ASP technology, extended by an approximation technique based on a new method for answer set enumeration in the order of optimality. We evaluate plingo's performance empirically by comparing it to other probabilistic systems.
Paper Structure (14 sections, 7 theorems, 63 equations, 4 figures, 1 table)

This paper contains 14 sections, 7 theorems, 63 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. The language Lpmln$^{\mathit{\pm}}$
  4. From Lpmln to Lpmln$^{\mathit{\pm}}$
  5. From ProbLog to Lpmln$^{\mathit{\pm}}$ and back
  6. The language of plingo and its frontends
  7. The frontend of Lpmln
  8. The frontend of ProbLog
  9. The frontend of P-log
  10. The system plingo
  11. Experiments
  12. Conclusion
  13. Acknowledgments
  14. Proofs

Key Result

Proposition 1

If $\Pi$ is an Lpmln program such that $\Pi^{\mathit{soft}}$ contains only soft integrity constraints, then $\mathit{SSM}^{\mathit{alt}}(\Pi)=\mathit{SM}({\overline{\Pi^{\mathit{hard}}}})$.

Figures (4)

  • Figure 1: System architecture of plingo. The frontends are colored in yellow. Modules of the system are gray boxes. The green flow corresponds to MPE inference, the blue one to exact marginal inference, and the red one to approximate inference, all of them using plingo's internal solving algorithms. The purple flow corresponds to MPE and marginal inference using problog.
  • Figure 2: Runtimes of all systems and quality of the approximation method on the Grid domain.
  • Figure 3: Runtimes of plingo, plog-naive and plog-dco on the P-log domains.
  • Figure 4: Runtimes of plingo and lpmln2asp on the Lpmln domains.

Theorems & Definitions (15)

  • Example 1
  • Example 2
  • Example 3
  • Example 4
  • Proposition 1
  • Example 5
  • Proposition 2
  • Example 6
  • Proposition 3
  • Example 7
  • ...and 5 more