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    "info": {
        "author": "Javier Romero",
        "author_email": "javier@cs.uni-potsdam.de",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Software Development :: Build Tools"
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        "description": "# asprin\n> A general framework for qualitative and quantitative optimization in answer set programming.\n\n## Description\n`asprin` is a general framework for optimization in ASP that allows:\n* computing optimal stable models of logic programs with preferences, and\n* defining new preference types in a very easy way.\nSome preference types (`subset`, `pareto`...) are already defined in `asprin`'s library,\nbut many more can be defined simply writing a logic program.\n\nFor a formal description of `asprin`, please read our [paper](http://www.cs.uni-potsdam.de/wv/pdfformat/brderosc15a.pdf) ([bibtex](http://www.cs.uni-potsdam.de/wv/bibtex/brderosc15a.bib)).\n\nStarting with version 3, `asprin` is documented in the [Potassco guide](https://github.com/potassco/guide/releases/).\nOlder versions are documented in the [Potassco guide on Sourceforge](https://sourceforge.net/projects/potassco/files/guide/).\n\n\n## Usage\n```bash\n$ asprin [number_of_models] [options] [files]\n```\nBy default, `asprin` loads its library `asprin_lib.lp`. This may be disabled with option `--no-asprin-lib`.\n\nOption `--help` prints help.\n\nOptions `--approximation=weak` and `--approximation=heuristic` activate solving modes different than the basic ones, \nand are often faster than it.\n\nOption `--meta=query` can be used to compute optimal models that contain the atom `query`. \n\nOptions `--meta=simple` or `--meta=combine` should be used to compute many optimal models using\nnon stratified preference programs (in `asprin`'s library this can only happen with CP nets, see below).\n\nOption `--on-opt-heur` can be used to enumerate diverse (or similar) optimal stable models. \nFor example, try with `--on-opt-heur=+,p,1,false --on-opt-heur=-,p,1,true`.\n\nOption `--improve-limit` can be used to enumerate close to optimal stable models.\nFor example, try with `--improve-limit 2,1000`.\n\n## Building\n\n<!--- TO BE CHANGED -->\nThe easiest way to obtain `asprin` is using Anaconda. \nPackages are available in the Potassco channel.\nFirst install either Anaconda or Miniconda and then run: \n`conda install -c potassco asprin`.\n<!---               -->\n\n`asprin` can also be installed with [pip](https://pip.pypa.io) via\n```pip install asprin```. \nFor a local installation, add option ```--user```.\nIn this case, setting environment variable `PYTHONUSERBASE` to `dir` before running `pip`, \n`asprin` will be installed in `dir/bin/asprin`.\n\n<!--- TO BE CHANGED -->\nIf that does not work, \nyou can always download the sources from \n[here](https://github.com/potassco/asprin/releases/download/v3.1.0/asprin-3.1.0.tar.gz) in some directory `dir`,\nand run `asprin` with `python dir/asprin/asprin/asprin.py`.\n<!---               -->\n\nSystem tests may be run with ```asprin --test``` and ```asprin --test --all```.\n\n`asprin` has been tested with `Python 2.7.13` and `3.5.3`, using `clingo 5.3.0`.\n\n```asprin``` uses the `ply` library, version `3.11`,\nwhich is bundled in [asprin/src/spec_parser/ply](https://github.com/potassco/asprin/tree/master/asprin/src/spec_parser/ply),\nand was retrieved from http://www.dabeaz.com/ply/.\n\n## Examples\n```\n$ cat examples/example1.lp\ndom(1..3).\n1 { a(X) : dom(X) }.\n#show a/1.\n\n#preference(p,subset) { \n  a(X)\n}.\n#optimize(p).\n\n\n$ asprin examples/example1.lp 0\nasprin version 3.0.0\nReading from examples/example1.lp\nSolving...\nAnswer: 1\na(3)\nOPTIMUM FOUND\nAnswer: 2\na(2)\nOPTIMUM FOUND\nAnswer: 3\na(1)\nOPTIMUM FOUND\n\nModels       : 3\n  Optimum    : yes\n  Optimal    : 3\n\n$ cat examples/example2.lp\n%\n% base program\n%\n\ndom(1..3).\n1 { a(X) : dom(X) } 2.\n1 { b(X) : dom(X) } 2.\n#show a/1.\n#show b/1.\n\n%\n% basic preference statements\n%\n\n#preference(p(1),subset){\n  a(X)\n}.\n\n#preference(p(2),less(weight)){\n  X :: b(X)\n}.\n\n#preference(p(3),aso){\n  a(X) >> not a(X) || b(X)\n}.\n\n#preference(p(4),poset){\n  a(X);\n  b(X);\n  a(X) >> b(X)\n}.\n\n%\n% composite preference statements\n%\n\n#preference(q,pareto){\n  **p(X)\n}.\n\n#preference(r,neg){\n  **q\n}.\n\n%\n% optimize statement\n%\n\n#optimize(r).\n\n$ asprin examples/example2.lp \nasprin version 3.0.0\nReading from examples/example2.lp\nSolving...\nAnswer: 1\na(3) b(1)\nOPTIMUM FOUND\n\nModels       : 1+\n  Optimum    : yes\n```\n\n## CP nets\n\n`asprin` preference library implements the preference type `cp`,\nthat stands for *CP nets*.\n\nCP nets where introduced in the following paper:\n*  Craig Boutilier, Ronen I. Brafman, Carmel Domshlak, Holger H. Hoos, David Poole:\nCP-nets: A Tool for Representing and Reasoning with Conditional Ceteris Paribus Preference Statements. \nJ. Artif. Intell. Res. 21: 135-191 (2004)\n\nPropositional preference elements of type `cp` have one of the following forms:\n1. `a >> not a || { l1; ...; ln }`, or\n2. `not a >> a || { l1; ...; ln }`\n\nwhere `a` is an atom and `l1`, ..., `ln` are literals.\n\nThe semantics is defined using the notion of improving flips.\nLet `X` and `Y` be two interpretations of a logic program.\nThere is an improving flip from `X` to `Y` if \nthere is some preference element such that `X` and `Y` satisfy all `li`'s, and either\nthe element has the form (1) and `Y` is the union of `X` and `{a}`, \nor \nthe element has the form (2) and `Y` is `X` minus `{a}`.\nThen, `W` is better than `Z` if there is a sequence of improving flips from `W` to `Z`.\nA CP net is consistent if there is no interpretation `X` such that `X` is better than `X`.\n\n\nWe provide various encoding and solving techniques for CP nets, \nthat can be applied depending on the structure of the CP net.\nFor tree-like CP nets, see example [cp_tree.lp](https://github.com/potassco/asprin/blob/master/asprin/examples/cp_tree.lp).\nFor acyclic CP nets, see example [cp_acyclic.lp](https://github.com/potassco/asprin/blob/master/asprin/examples/cp_acyclic.lp).\nFor general CP nets, see example [cp_general.lp](https://github.com/potassco/asprin/blob/master/asprin/examples/cp_general.lp).\n\n`asprin` implementation of CP nets is correct only for consistent CP nets.\nNote that tree-like and acyclic CP nets are always consistent, but this does not hold in general.\n\n\n## Contributors\n\n* Javier Romero\n\n\n",
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