Search papers, labs, and topics across Lattice.
This paper introduces a neurosymbolic reasoning and learning framework that integrates answer set programming (ASP) with energy-based models, enabling joint optimization in continuous latent spaces while leveraging declarative semantics. The methodology incorporates background knowledge, constraints, and non-monotonic inference, enhancing the robustness of training in dynamic domains. Experimental results on benchmarks like MNIST, Clevr, and MOT illustrate the framework's effectiveness in real-world applications involving perception and interaction.
Joint optimization of continuous latent spaces with ASP semantics leads to a robust neurosymbolic framework that excels in dynamic reasoning tasks.
We present a general neurosymbolic reasoning and learning methodology based on a modular integration of answer set programming with an energy based model substrate. Key contributions are: (1) supporting joint optimisation in the continuous latent space through explicit ASP-based declarative semantics fully incorporating background knowledge, constraints, non-monotonic inference; and (2) advancing recent works at the interface of answer sets, probabilistic logic, and answer set modulo theories by providing a generalised model and practical platform for ASP-centric robust, end-to-end training for applications in dynamic domains (e.g., involving perception and interaction). We provide a practical implementation, and demonstrate basic use and application (with MNIST), and evaluate with the visual question-answering benchmark Clevr and the multi-object tracking benchmark MOT.
