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Probabilistic logic language for inference, planning and learning in static and dynamic domains
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README.md

Distributional Clauses (beta)

Probabilistic logic language for inference, planning and learning in static and dynamic domains
DC: Distributional Clauses for inference in static models. (example and tutorial at examples/tutorial.pl)
DCPF: Distributional Clauses Particle Filter for filtering in dynamic models
HYPE: planner for hybrid MDPs based on DCPF (the code will be soon available)

The code is in beta, if you need help or find a bug please write an issue or contact me at davide (DOT) nitti (AT) cs (DOT) kuleuven (DOT) be

Prerequisites

Install Yap prolog (tested on YAP 6.2.2), follow these instructions:

cd yap-6.2.2
mkdir arch
cd arch
../configure --enable-tabling=yes --enable-dynamic-loading
make
sudo make install
sudo make install_library

Install GSL library, e.g., for Ubuntu 14.04:

sudo apt-get install libgsl0-dev libgsl0ldbl

For more recent Ubuntu distributions (e.g., 16.04) use this instead:

sudo apt-get install libgsl2 libgsl2:i386

Install boost library, e.g., for Ubuntu:

sudo apt-get install libboost-all-dev

Compilation

Execute

sh make.sh

Test

Execute

sh test.sh

or equivalently

yap -L examples/example1.pl

Expected output (the error values may differ):

Testing example1.pl...
Absolute error drawn(1) ~= 1: 4.91269841260689e-05
Absolute error drawn(1) ~= 2: 0.000294206349202047
Absolute error drawn(1) ~= 3: 0.00178948412698662
Absolute error average g ~ Gaussian(0,0.1): 0.000900261603206224
% 0.290 CPU in 0.291 seconds ( 99% CPU)

Tutorials

DC tutorial

A quick DC tutorial is available at examples/tutorial.pl
To run the tutorial, execute inside examples:

yap -l tutorial.pl

then write test_coin(N). followed by enter or other queries as explained in the file

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