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.
Abelha, P., Guerin, F. "Learning How a Tool Affords by Simulating 3D Models from the Web". In Proceedings of IEEE International Conference on Intelligent Robots and Systems (IROS 2017). . Pre-version-PDF.
Abelha, P., Guerin, F., Schoeler, M. "A Model-Based Approach to Finding Substitute Tools in 3D Vision Data". In Proceedings of IEEE International Conference on Robotics and Automation 2016. Acceptance rate 34.7%. Pre-version-PDF.
H. Celikkanat, G. Orhan, N. Pugeault, F. Guerin, E. Sahin, S. Kalkan, "Learning Context on a Humanoid Robot using Incremental Latent Dirichlet Allocation", IEEE Trans. AMD, (early access) 2015. METU-CENG-TR-2015-01.
Guerin, F., Krueger, N. and Kraft, D. "A Survey of the Ontogeny of Tool Use: from Sensorimotor Experience to Planning" for IEEE Trans. AMD, 5(1):18–45, 2013. pre-publication PDF.