Potassco - the Potsdam Answer Set Solving Collection

LoIDE

Tue, 27 Sep 2022 00:00:00 +0000

LoIDE is an open-source web-based IDE for Logic Programming. It is modular and extensible, and it has been developed using modern technologies and languages to be available from any device.

Link to the webpage

Link to a live demo

Link to the GitHub repository

EmbASP

Tue, 27 Sep 2022 00:00:00 +0000

EmbASP is an open-source framework for embedding Logic Programming into external systems. It has been conceived to help developers design and implement, on different platforms, complex reasoning tasks by means of logic-based solvers.

Link to the website

Link to the GitHub repository

telingo

Mon, 03 Jun 2019 00:00:00 +0000

Telingo is a solver for temporal programs. It leaverages clingo’s input language and scripting cababilities to parse and solve programs with temporal formulas. As such, the input of telingo is valid clingo input supporting all of clingo’s language features, like for example aggregates; only the way programs are grounded and solved is adjusted.

Download and Resources

asprilo

Tue, 20 Feb 2018 00:00:00 +0000

What is asprilo?

asprilo is an benchmarking framework to study typical scenarios in intra-logistics and warehouse automation with multiple mobile robots. It offers a concise specification of this problem domain accompanied by a set of tools to generate benchmark instances, verify plans, as well as visualize both instances and plans. Due to the diverse and complex nature of this domain, asprilo offers an ideal test bed not only for modern industrial scenarios but complex dynamic problems in general. Although, the implementation of asprilo relies on answer set programming (ASP) and Python, it also supports any other approach that complies with its fact-based I/O interface.

“Full Warehouse Domain” (A-Domain)	“Movement-Only Domain” (M-Domain)

(Click images for video playback)

Documentation

A detailed documentation of asprilo’s problem domain and key components can be found at https://asprilo.github.io/

Development

The source code of the project is available at https://github.com/potassco/asprilo

Publications

Gebser, M., Obermeier, P., Otto, T., Schaub, T., Sabuncu, O., Nguyen, V., & Son, T. C. (2018). Experimenting with robotic intra-logistics domains. CoRR, abs/1804.10247. [Experiments]
Gebser, M., Obermeier, P., Otto, T., Schaub, T., Sabuncu, O., Nguyen, V., & Son, T. C. (2018). Experimenting with robotic intra-logistics domains. TPLP, 18(3-4), 502–519. [Experiments]
Nguyen, V., Obermeier, P., Son, T. C., Schaub, T., & Yeoh, W. (2017). Generalized Target Assignment and Path Finding Using Answer Set Programming. In IJCAI (pp. 1216–1223). ijcai.org.

clingo[LP]

Mon, 19 Jun 2017 00:00:00 +0000

clingo[LP] extends the ASP solver clingo with linear constraints as dealt with in Linear Programming (LP).

Install

Install via conda:

conda install -c potassco -c conda-forge clingo-lp

CPLEX

The clingolp propagator requires an LP solver. The default is lpsolve but cplex can be used optionally.

IBM also provides a promotional version of cplex that is sufficient to solve small problems limited to 1000 variables and 1000 constraints.

conda install -c ibmdecisionoptimization cplex

To solve larger problems, you need to use the full version of CPLEX Studio.

Syntax

LP constraints can be expressed as follows:

LP constraints	ClingoLP Syntax
w₁x₁+…+w_nx_n >= k	`&sum{`w₁``x₁`;`…`;`w_n``x_n`} >=` k
domain(x)={l,…,u}	`&dom{`l`..`u`} =` x
maximize: w₁x₁+…+w_nx_n	`&maximize{`w₁``x₁`;`…`;`w_n``x₁`}` (minimize analogous)

To avoid syntax clashes, you must quote " real numbers. Instead of 1.5 write "1.5".

General usage of clingo[LP]

Basic call: clingoLP <encoding> <instance> <options>

Example: clingoLP example_encoding.lp example_instance.lp --show-lp-solution 0

Options
- –show-lp-solution
  - Show LP solution and value of objective function
- –lp-solver=cplx
  - Selects a LP solver (default lp-solver=lps)
- –accuracy=n
  - Prints n decimal positions (default accuracy=1)
- –epsilon=n,m
  - Set epsilon to convert lhs > k into lhs >= k+n*10^-m (default epsilon=1,3)
- –strict
  - Enables strict semantics
- –trace
  - Enables detailed output of theory propagation
- –core-confl=n
  - Searches for core conflicts if at least n% of the theory atoms are decided (default core-confl=20)
- –prop-heur=n
  - Starts a solve call of the LP solver if at least n% of the theory atoms are decided (default prop-heur=0)
- –ilp
  - Sets the LP solver to solve an Integer Linear Programming (ILP) problem
- All clingo options
- Number of solutions controlled via clingo

Publication

Tomi Janhunen, Roland Kaminski, Max Ostrowski, Torsten Schaub, Sebastian Schellhorn and Philipp Wanko, Clingo goes Linear Constraints over Reals and Integers, TPLP, 2017: [Experiments]

clingo[DL]

Mon, 19 Jun 2017 00:00:00 +0000

The system clingo[DL] provides a seamless way to integrate a subset of the theory of linear constraints, namely difference logic, into ASP. It deals with constraints of the form x-y<=k, where x and y are integer variables and k is an integer constant. Despite its restriction, difference logic can be used to naturally encode timing related problems, e.g., scheduling or timetabling, and provides the additional advantage of being solvable in polynomial time. Syntactically, a difference constraint x-y<=k is represented by a difference constraint atom of the form &diff {x-y} <= k.

Download

Recent clingo[DL] releases are on github: https://github.com/potassco/clingoDL/releases.
The latest source is on github: github.com/potassco/clingoDL.

Resources

Publications

Tomi Janhunen, Roland Kaminski, Max Ostrowski, Torsten Schaub, Sebastian Schellhorn and Philipp Wanko, Clingo goes Linear Constraints over Reals and Integers, TPLP, 2017: [Experiments]

gtapf

Wed, 24 May 2017 00:00:00 +0000

gtapf is a generalization of Target Assignment and Path Finding (TAPF) planning problems for which we propose solutions using ASP.

Demo

A demo video in addition to our IJCAI’17 paper.

Publications

Nguyen, V., Obermeier, P., Son, T., Schaub, T., Yeoh, W.: Generalized Target Assignment and Path Finding Using Answer Set Programming. In Proceedings of the Twenty-sixth International Joint Conference on Artificial Intelligence (IJCAI’17), 2017, to be published. [PDF]

How do aggregates work?

Thu, 20 Apr 2017 00:00:00 +0000

A sum aggregate consists of a set of elements consisting of a tuple together with a condition and a guard. Whenever the condition of an element is true wrt an answer set, its associated tuple contributes a value to a set. Hence, if two equal tuples have true conditions, then only one value is provided.

Consider for example the program

{a; b; c}.

:- #sum { 2,p : a; 1,q : a
        ; 1,q : b; 2,r : b
        ; 2,r : c; 1,s : c
    } < 6.

If a and b are true, the values of tuples (2,p), (1,q), and (2,r) are summed up. Hence, the sum aggregate evaluates to 5. Note that the value of tuple (1,q) provided by the second and third element of the sum aggregate is counted only once.

If a and c are true, the values of tuples (2,p), (1,q), (2,r), and (1,s) are summed up. Hence, the sum aggregate evaluates to 6 and {a, c} is an answer set of the program. In fact, there are only two answer sets: {a, c} and {a, b, c}.

Accumulation in #sum+, #min, and #max aggregates as well as #minimize and #maximize constraints works the same.

fz2aspif

Fri, 02 Dec 2016 00:00:00 +0000

fz2aspif translates flatzinc files into ground aspif files to be used with clingcon. As clingcon currently only supports linear and distinct constraints, fz2aspif provides the translation descriptions for mzn2fzn here: share/clingcon. Use mzn2fzn as described in the README to convert minizinc csp files into flatzinc files compatible with fz2aspif.

Download

Wasp

Tue, 18 Oct 2016 00:00:00 +0000

Wasp is an ASP solver handling disjunctive logic programs under the stable model semantics. It combines techniques originally introduced for SAT solving with methods specifically designed for ASP computation.

Link to Website