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Found 4 papers, 1 papers with code
Answer-Set Programming for Lexicographical Makespan Optimisation in Parallel Machine Scheduling
We study the application of Answer-Set Programming (ASP) to solve this problem.
A Neuro-Symbolic ASP Pipeline for Visual Question Answering
Our pipeline covers (i) training neural networks for object classification and bounding-box prediction of the CLEVR scenes, (ii) statistical analysis on the distribution of prediction values of the neural networks to determine a threshold for high-confidence predictions, and (iii) a translation of CLEVR questions and network predictions that pass confidence thresholds into logic programs so that we can compute the answers using an ASP solver.
A Confidence-Based Interface for Neuro-Symbolic Visual Question Answering
We present a neuro-symbolic visual question answering (VQA) approach for the CLEVR dataset that is based on the combination of deep neural networks and answer-set programming (ASP), a logic-based paradigm for declarative problem solving.
Stepwise Debugging of Answer-Set Programs
Similar to debugging in imperative languages, where the behaviour of a program is observed during a step-by-step execution, stepping for ASP allows for observing the effects that rule applications have in the computation of an answer set.
