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    "info": {
        "author": "Thiago P. Bueno",
        "author_email": "thiago.pbueno@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
        ],
        "description": "MDP-ProbLog\n===========\n\n    MDP-ProbLog is a Python3 framework to represent and solve (infinite-horizon)\n    MDPs using Probabilistic Logic Programming.\n\nInstall\n-------\n\nIt is required to have Python3 installed.\n\n::\n\n    $ pip3 install mdpproblog\n\nUsage\n-----\n\n::\n\n    $ mdp-problog --help\n    usage: mdp-problog {list, show, simulate, solve} [-m DOMAIN INSTANCE] [OPTIONS]\n\n    MDP-ProbLog is a Python3 framework to represent and solve Markovian Decision\n    Processes by Probabilistic Logic Programming. This project is free software.\n    Please check the documentation at http://pythonhosted.org/mdpproblog/.\n\n    positional arguments:\n      {list,show,solve,simulate}\n                            available commands: list examples, show and solve\n                            models or simulate optimal policy\n\n    optional arguments:\n      -h, --help            show this help message and exit\n      -m MODEL MODEL, --model MODEL MODEL\n                            list of domain and instance files\n      -x EXAMPLE, --example EXAMPLE\n                            select model from examples\n      -g GAMMA, --gamma GAMMA\n                            discount factor (default=0.9)\n      -e EPSILON, --epsilon EPSILON\n                            maximum error (default=0.1)\n      -t TRIALS, --trials TRIALS\n                            number of trials (default=100)\n      -z HORIZON, --horizon HORIZON\n                            simulation horizon (default=30)\n\nInput\n-----\n\nDomain specification for the sysadmin planning problem\n(models/sysadmin/domain.pl).\n\n.. code:: prolog\n\n\n    % Network topology properties\n    accTotal([],A,A).\n    accTotal([_|T],A,X) :- B is A+1, accTotal(T,B,X).\n    total(L,T) :- accTotal(L,0,T).\n    total_connected(C,T) :- connected(C,L),\n                            total(L,T).\n\n    accAlive([],A,A).\n    accAlive([H|T],A,X) :- running(H,0), B is A+1, accAlive(T,B,X).\n    accAlive([H|T],A,X) :- not(running(H,0)), B is A, accAlive(T,B,X).\n    alive(L,A) :- accAlive(L,0,A).\n    total_running(C,R) :- connected(C,L),\n                          alive(L,R).\n\n    % State fluents\n    state_fluent(running(C)) :- computer(C).\n\n    % Actions\n    action(reboot(C)) :- computer(C).\n    action(reboot(none)).\n\n    % Transition model\n    1.00::running(C,1) :- reboot(C).\n    0.05::running(C,1) :- not(reboot(C)), not(running(C,0)).\n    P::running(C,1)    :- not(reboot(C)), running(C,0),\n                          total_connected(C,T), total_running(C,R), P is 0.45+0.50*R/T.\n\n    % Utility attributes\n\n    % costs\n    utility(reboot(C), -0.75) :- computer(C).\n    utility(reboot(none), 0.00).\n\n    % rewards\n    utility(running(C,0),  1.00) :- computer(C).\n\nExample\n-------\n\n::\n\n    $ mdp-problog simulate -x sysadmin1\n\n    Value(running(c1,0)=0, running(c2,0)=0, running(c3,0)=0) = 16.829\n    Value(running(c1,0)=1, running(c2,0)=0, running(c3,0)=0) = 19.171\n    Value(running(c1,0)=0, running(c2,0)=1, running(c3,0)=0) = 19.205\n    Value(running(c1,0)=1, running(c2,0)=1, running(c3,0)=0) = 23.028\n    Value(running(c1,0)=0, running(c2,0)=0, running(c3,0)=1) = 19.206\n    Value(running(c1,0)=1, running(c2,0)=0, running(c3,0)=1) = 23.029\n    Value(running(c1,0)=0, running(c2,0)=1, running(c3,0)=1) = 21.392\n    Value(running(c1,0)=1, running(c2,0)=1, running(c3,0)=1) = 25.607\n\n    Policy(running(c1,0)=0, running(c2,0)=0, running(c3,0)=0) = reboot(c1)\n    Policy(running(c1,0)=1, running(c2,0)=0, running(c3,0)=0) = reboot(c3)\n    Policy(running(c1,0)=0, running(c2,0)=1, running(c3,0)=0) = reboot(c1)\n    Policy(running(c1,0)=1, running(c2,0)=1, running(c3,0)=0) = reboot(c3)\n    Policy(running(c1,0)=0, running(c2,0)=0, running(c3,0)=1) = reboot(c1)\n    Policy(running(c1,0)=1, running(c2,0)=0, running(c3,0)=1) = reboot(c2)\n    Policy(running(c1,0)=0, running(c2,0)=1, running(c3,0)=1) = reboot(c1)\n    Policy(running(c1,0)=1, running(c2,0)=1, running(c3,0)=1) = reboot(none)\n\n    >> Value iteration converged in 0.196sec after 40 iterations.\n    >> Average time per iteration = 0.005sec.\n\n    Expectation(running(c1,0)=0, running(c2,0)=0, running(c3,0)=0) = 16.733\n    Expectation(running(c1,0)=1, running(c2,0)=0, running(c3,0)=0) = 19.433\n    Expectation(running(c1,0)=0, running(c2,0)=1, running(c3,0)=0) = 19.108\n    Expectation(running(c1,0)=1, running(c2,0)=1, running(c3,0)=0) = 23.377\n    Expectation(running(c1,0)=0, running(c2,0)=0, running(c3,0)=1) = 19.546\n    Expectation(running(c1,0)=1, running(c2,0)=0, running(c3,0)=1) = 23.287\n    Expectation(running(c1,0)=0, running(c2,0)=1, running(c3,0)=1) = 21.785\n    Expectation(running(c1,0)=1, running(c2,0)=1, running(c3,0)=1) = 25.849\n\nLicense\n-------\n\nCopyright (c) 2016-2017 Thiago Pereira Bueno All Rights Reserved.\n\nMDPProbLog is free software: you can redistribute it and/or modify it\nunder the terms of the GNU Lesser General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nMDPProbLog is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU Lesser General Public License\nalong with MDPProbLog. If not, see http://www.gnu.org/licenses/.",
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