I am interested in new approaches to knowledge representation and reasoning for AI systems which get over the rigidity and brittleness of classical approaches. Human knowledge of a concept such as "container" is very flexible to be applied to a wide range of objects (pots, cups, bags, boxes, rooms, buildings, ...) and applied in more abstract domains (political parties, controls on disease spread, damage from a scandal). The actions associated with container (insert, remove, escape, seal, breach, etc.) can also be adapted appropriately. These are not special or unusual or effortful applications of a concept for humans. Every human concept is effortlessly applied to a wide range of situations, and examples are everywhere in everyday cognition. It suggests that the human representation and reasoning machinery has a design which facilitates this.
I am looking for (non-classical) knowledge representation and reasoning which could allow AI systems to transfer knowledge of basic concepts in a human-like way. Vision example: give a system some knowledge of the types of tool (e.g. spatulas) that can lift pancakes or eggs from a pan, and enable it to transfer the concept to other objects which afford the same action. Manipulation example: give a system some knowledge of containers and container actions and enable it to apply this across a variety of scenarios. Language processing example: in understanding, given knowledge of concepts such as container and associated actions, to be able to recognise it in varied instantiations, e.g. where not literally used.