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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.
 

Selected
Publications

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):1845, 2013.  pre-publication PDF.