A logic-based calculus of events
New Generation Computing
New Generation Computing
Maintaining knowledge about temporal intervals
Communications of the ACM
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
Protocols from perceptual observations
Artificial Intelligence - Special volume on connecting language to the world
Using theory completion to learn a robot navigation control program
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Event Model Learning from Complex Videos using ILP
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
HYPROLOG: a new logic programming language with assumptions and abduction
ICLP'05 Proceedings of the 21st international conference on Logic Programming
Temporal logic for process specification and recognition
Intelligent Service Robotics
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We propose an interleaved inductive-abductive model for reasoning about complex spatio-temporal narratives. Typed Inductive Logic Programming (Typed-ILP) is used as a basis for learning the domain theory by generalising from observation data, whereas abductive reasoning is used for noisy data correction by scenario and narrative completion thereby improving the inductive learning to get semantically meaningful event models. We apply the model to an airport domain consisting of video data for 15 turn-arounds from six cameras simultaneously monitoring logistical processes concerned with aircraft arrival, docking, departure etc and a verbs data set with 20 verbs enacted out in around 2500 vignettes. Our evaluation and demonstration focusses on the synergy afforded by the inductive-abductive cycle, whereas our proposed model provides a blue-print for interfacing common-sense reasoning about space, events and dynamic spatio-temporal phenomena with quantitative techniques in activity recognition.