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CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments
In CLEVR-POC, knowledge in the form of logical constraints needs to be leveraged to generate plausible answers to questions about a hidden object in a given partial scene.
Neuro-Symbolic Spatio-Temporal Reasoning
In this chapter, we view this integration problem from the perspective of Neuro-Symbolic AI.
Compositional Generalization and Neuro-Symbolic Architectures
Compositional generalization is the ability to understand novel combinations of known concepts.
Semantic Referee: A Neural-Symbolic Framework for Enhancing Geospatial Semantic Segmentation
Understanding why machine learning algorithms may fail is usually the task of the human expert that uses domain knowledge and contextual information to discover systematic shortcomings in either the data or the algorithm.
Reasoning for Improved Sensor Data Interpretation in a Smart Home
The ontology of this paradigm is inspired form the SSN ontology (Semantic Sensor Network) and used for representation of both the sensor data and the contextual information.
