Chris Child
Chris Child
Senior Lecturer, City, University of London
Verified email at - Homepage
Cited by
Cited by
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
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
NPCs as people, too: the extreme AI personality engine
J Georgeson, C Child
arXiv preprint arXiv:1609.04879, 2016
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
Agents and Environments
K Stathis, C Child, W Lu, GK Lekeas
Technical report, SOCS Consortium, 2002. IST32530/CITY/005/DN/I/a1, 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
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
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
Rule value reinforcement learning for cognitive agents
C Child, K Stathis
Proceedings of the fifth international joint conference on autonomous agents …, 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
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
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
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
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
Be the controller: A kinect tool kit for video game control-recognition of human motion using skeletal relational angles
N Hadjiminas, CHT Child
Learning to Act with RVRL agents
CHT Child, K Stathis, A Garcez
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
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
Non-Euclidean Video Games: Exploring Player Perceptions and Experiences inside Impossible Spaces
D Osudin, A Denisova, C Child
IEEE Transactions on Games, 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
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