dpasp
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kamel-usp
Differentiable probabilistic answer set programming
Hi. I'm writing a simple program but the following line: ``` fb(FB):- #count{ X : f(X), b(X)} = FB. ``` raises a syntax error (at `FB` after `=`). However, by...
Create packages for common distributions (debian/ubuntu, homebrew, what else?) and containerization (docker)
Allow for a #include "filename.py" or #include "filename.plp" directive that is equivalent to the Python snippet #python #end, but can be used to split a dpasp program into several files....
Implement algorithms for maximum a posteriori inference, that is, finding the probable interpretation of a selected set of facts. Implement at least a brute-force algorithm and an algorithm that calls...
Create a repository of pre-trained neural networks for common tasks such as object detection, object classification, entity extraction etc. Those networks should be easily accessible from a dpasp program by...
Implement MCMC inference a la PASTA for probabilistic logic programs, then extend it to approximate learning of neural logic programs.
Build dependency graph to help with grounding of probabilistic facts and accelerate inference
Implement efficient techniques for fully observed data (including at least observations of all probabilistic facts). Consider the case of probabilistic logic programs (solved in closed form) and neuro-probabilistic logic programs...
For a very simple (and stratified) program like: ```mango_buyer(1..3). mango_seller(1..3). 0.5::deal(X, Y) :- mango_buyer(X), mango_seller(Y). was_deal :- deal(X, Y). #query(was_deal). ``` we get **non-converging inference errors** with ASEO as opposed...
Approximate inference through Answer Set Enumeration by Optimality (ASEO) is currently only implemented for probabilistic facts in stratified programs under the maxent semantics. Adding support for annotated disjunctions (ADs), non-stratified...
