On a plausible concept-wise multipreference semantics and its relations with self-organising maps
This work addresses knowledge representation challenges in AI, particularly for defeasible reasoning, but it appears incremental as it builds on existing preferential approaches.
The paper tackles the problem of modeling defeasible reasoning in knowledge representation by proposing a concept-wise multi-preference semantics for description logic, which satisfies properties like KLM postulates and avoids the drowning problem, and it relates this semantics to self-organising maps used in psychology for category generalisation.
Inthispaperwedescribeaconcept-wisemulti-preferencesemantics for description logic which has its root in the preferential approach for modeling defeasible reasoning in knowledge representation. We argue that this proposal, beside satisfying some desired properties, such as KLM postulates, and avoiding the drowning problem, also defines a plausible notion of semantics. We motivate the plausibility of the concept-wise multi-preference semantics by developing a logical semantics of self-organising maps, which have been proposed as possible candidates to explain the psychological mechanisms underlying category generalisation, in terms of multi-preference interpretations.