Morpho-logic from a Topos Perspective: Application to symbolic AI
This work addresses symbolic AI researchers by providing a novel theoretical framework for reasoning tasks, but it appears incremental as it builds on existing links between mathematical morphology and modal logic.
The paper tackles the problem of linking mathematical morphology and modal logic from a topos perspective to enhance symbolic AI, resulting in a constructive modal logic called morpho-logic with a sound and complete proof system applicable to tasks like revision and spatial reasoning.
Modal logics have proved useful for many reasoning tasks in symbolic artificial intelligence (AI), such as belief revision, spatial reasoning, among others. On the other hand, mathematical morphology (MM) is a theory for non-linear analysis of structures, that was widely developed and applied in image analysis. Its mathematical bases rely on algebra, complete lattices, topology. Strong links have been established between MM and mathematical logics, mostly modal logics. In this paper, we propose to further develop and generalize this link between mathematical morphology and modal logic from a topos perspective, i.e. categorial structures generalizing space, and connecting logics, sets and topology. Furthermore, we rely on the internal language and logic of topos. We define structuring elements, dilations and erosions as morphisms. Then we introduce the notion of structuring neighborhoods, and show that the dilations and erosions based on them lead to a constructive modal logic, for which a sound and complete proof system is proposed. We then show that the modal logic thus defined (called morpho-logic here), is well adapted to define concrete and efficient operators for revision, merging, and abduction of new knowledge, or even spatial reasoning.