| Ql-bt: Enhancing behaviour tree design and implementation with q-learning R Dey, C Child 2013 IEEE Conference on Computational Inteligence in Games (CIG), 1-8, 2013 | 70 | 2013 |
| NPCs as people, too: the extreme AI personality engine J Georgeson, C Child arXiv preprint arXiv:1609.04879, 2016 | 11 | 2016 |
| Rendering non-euclidean space in real-time using spherical and hyperbolic trigonometry D Osudin, C Child, YH He Computational Science–ICCS 2019: 19th International Conference, Faro …, 2019 | 10 | 2019 |
| Hand pose estimation using deep stereovision and markov-chain monte carlo R Remilekun Basaru, G Slabaugh, E Alonso, C Child Proceedings of the IEEE International Conference on Computer Vision …, 2017 | 8 | 2017 |
| Agents and Environments K Stathis, C Child, W Lu, GK Lekeas Technical report, SOCS Consortium, 2002. IST32530/CITY/005/DN/I/a1, 2002 | 8 | 2002 |
| Quantized census for stereoscopic image matching RR Basaru, C Child, E Alonso, G Slabaugh 2014 2nd International Conference on 3D Vision 2, 22-29, 2014 | 7 | 2014 |
| The apriori stochastic dependency detection (ASDD) algorithm for learning stochastic logic rules C Child, K Stathis International Workshop on Computational Logic in Multi-Agent Systems, 234-249, 2004 | 7 | 2004 |
| HandyDepth: Example-based stereoscopic hand depth estimation using Eigen Leaf Node Features RR Basaru, GG Slabaugh, C Child, E Alonso 2016 International Conference on Systems, Signals and Image Processing …, 2016 | 6 | 2016 |
| Rule value reinforcement learning for cognitive agents C Child, K Stathis Proceedings of the fifth international joint conference on autonomous agents …, 2006 | 4 | 2006 |
| Data‐driven recovery of hand depth using CRRF on stereo images RR Basaru, C Child, E Alonso, G Slabaugh IET Computer Vision 12 (5), 666-678, 2018 | 3 | 2018 |
| Performance Enhancement of Deep Reinforcement Learning Networks using Feature Extraction J Ollero, C Child Advances in Neural Networks–ISNN 2018: 15th International Symposium on …, 2018 | 3 | 2018 |
| SMART (Stochastic Model Acquisition with ReinforcemenT) learning agents: A preliminary report C Child, K Stathis Symposium on Adaptive Agents and Multi-agent Systems, 73-87, 2003 | 3 | 2003 |
| Modelling Emotion Based Reward Valuation with Computational Reinforcement Learning CHT Child, C Koluman, T Weyde Proceedings of the 41st Annual Conference of the Cognitive Science Society …, 2019 | 2 | 2019 |
| Implementing racing AI using q-learning and steering behaviours BP Trusler, C Child Conference on Simulation and AI in Computer Games 11, 09-2014, 2014 | 2 | 2014 |
| Be The controller: a kinect tool kit for video game control N Hadjiminas, C Child Computer Games, Multimedia and Allied Technology (CGAT 2012), 44, 2012 | 2 | 2012 |
| Be the controller: A kinect tool kit for video game control-recognition of human motion using skeletal relational angles N Hadjiminas, CHT Child | 2 | 2012 |
| Learning to Act with RVRL agents CHT Child, K Stathis, A Garcez | 2 | 2007 |
| International Classification of Diseases Prediction from MIMIIC-III Clinical Text Using Pre-Trained ClinicalBERT and NLP Deep Learning Models Achieving State of the Art I Aden, CHT Child, CC Reyes-Aldasoro Big Data and Cognitive Computing 8 (5), 47, 2024 | 1 | 2024 |
| Non-Euclidean Video Games: Exploring Player Perceptions and Experiences inside Impossible Spaces D Osudin, A Denisova, C Child IEEE Transactions on Games, 2024 | | 2024 |
| ORCID: 0000-0001-5425-2308, Koluman, C. and Weyde, T. ORCID: 0000-0001-8028-9905 (2019). Modelling Emotion Based Reward Valuation with Computational Reinforcement Learning CHT Child Cogsci, 2019 | | 2019 |
