By Dr Francisco Cruz Naranjo

Conference Papers

Cruz F; Wuppen P; Magg S; Fazrie A; Wermter S, 2017, 'Agent-advising approaches in an interactive reinforcement learning scenario', in 7th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics ICDL Epirob 2017, pp. 209 - 214, http://dx.doi.org/10.1109/DEVLRN.2017.8329809

Cruz F; Parisi GI; Twiefel J; Wermter S, 2016, 'Multi-modal integration of dynamic audiovisual patterns for an interactive reinforcement learning scenario', in IEEE International Conference on Intelligent Robots and Systems, pp. 759 - 766, http://dx.doi.org/10.1109/IROS.2016.7759137

Cruz F; Parisi GI; Wermter S, 2016, 'Learning contextual affordances with an associative neural architecture', in Esann 2016 24th European Symposium on Artificial Neural Networks, pp. 665 - 670

Cruz F; Twiefel J; Magg S; Weber C; Wermter S, 2015, 'Interactive reinforcement learning through speech guidance in a domestic scenario', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN.2015.7280477

Cruz F; Magg S; Weber C; Wermter S, 2014, 'Improving reinforcement learning with interactive feedback and affordances', in IEEE ICDL Epirob 2014 4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, pp. 165 - 170, http://dx.doi.org/10.1109/DEVLRN.2014.6982975

Naranjo FC; Leiva GA, 2010, 'Indirect training with error backpropagation in gray-box neural model: Application to a chemical process', in Proceedings International Conference of the Chilean Computer Science Society Sccc, pp. 265 - 269, http://dx.doi.org/10.1109/SCCC.2010.41

Cruz F; Acuña G; Cubillos F; Moreno V; Bassi D, 2007, 'Indirect training of grey-box models: application to a bioprocess', in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 391 - 397, http://dx.doi.org/10.1007/978-3-540-72393-6_47

Acuña G; Cruz F; Moreno V, 2006, 'Identifiability of time varying parameters in a Grey-Box Neural Model: Application to a biotechnological process', in 4th International Conference on Simulation and Modelling in the Food and Bio Industry 2006 Foodsim 2006, pp. 26 - 31

Preprints

Harland H; Dazeley R; Senaratne H; Vamplew P; Cruz F; Nakisa B, 2024, AI Apology: A Critical Review of Apology in AI Systems, http://arxiv.org/abs/2412.15787v1

Althubyani M; Meng Z; Xie S; Seung C; Razzak I; Sandoval EB; Kocaballi B; Cruz F, 2024, MERCI: Multimodal Emotional and peRsonal Conversational Interactions Dataset, http://arxiv.org/abs/2412.04908v2

Harland H; Dazeley R; Vamplew P; Senaratne H; Nakisa B; Cruz F, 2024, Adaptive Alignment: Dynamic Preference Adjustments via Multi-Objective Reinforcement Learning for Pluralistic AI, http://arxiv.org/abs/2410.23630v1

Ye WZ; Sandoval EB; Carreno-Medrano P; Cru F, 2024, Contextual Affordances for Safe Exploration in Robotic Scenarios, http://arxiv.org/abs/2405.06422v1

Lin Z; Cruz F; Sandoval EB, 2024, Self context-aware emotion perception on human-robot interaction, http://arxiv.org/abs/2401.10946v1

Bernotat J; Jirak D; Sandoval EB; Cruz F; Sciutti A, 2023, Asch Meets HRI: Human Conformity to Robot Groups, http://arxiv.org/abs/2308.13307v1

Hashemi M; Darejeh A; Cruz F, 2023, Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposal, http://arxiv.org/abs/2302.03180v2

Muñoz H; Portugal E; Ayala A; Fernandes B; Cruz F, 2022, Explaining Agent's Decision-making in a Hierarchical Reinforcement Learning Scenario, http://arxiv.org/abs/2212.06967v1

Millán-Arias C; Contreras R; Cruz F; Fernandes B, 2022, Reinforcement Learning for UAV control with Policy and Reward Shaping, http://arxiv.org/abs/2212.03828v1

Schroeter N; Cruz F; Wermter S, 2022, Introspection-based Explainable Reinforcement Learning in Episodic and Non-episodic Scenarios, http://arxiv.org/abs/2211.12930v1

Cruz F; Bignold A; Nguyen HS; Dazeley R; Vamplew P, 2022, Broad-persistent Advice for Interactive Reinforcement Learning Scenarios, http://arxiv.org/abs/2210.05187v1

Ly A; Dazeley R; Vamplew P; Cruz F; Aryal S, 2022, Elastic Step DQN: A novel multi-step algorithm to alleviate overestimation in Deep QNetworks, http://arxiv.org/abs/2210.03325v1

Cruz F; Young C; Dazeley R; Vamplew P, 2022, Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios, http://arxiv.org/abs/2207.03214v1

Nguyen HS; Cruz F; Dazeley R, 2021, A Broad-persistent Advising Approach for Deep Interactive Reinforcement Learning in Robotic Environments, http://arxiv.org/abs/2110.08003v2

Dazeley R; Vamplew P; Cruz F, 2021, Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey, http://arxiv.org/abs/2108.09003v1

Ayala A; Cruz F; Fernandes B; Dazeley R, 2021, Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Task, http://arxiv.org/abs/2108.08911v1

Millán-Arias C; Fernandes B; Cruz F, 2021, Learning Proxemic Behavior Using Reinforcement Learning with Cognitive Agents, http://arxiv.org/abs/2108.03730v1

Dazeley R; Vamplew P; Foale C; Young C; Aryal S; Cruz F, 2021, Levels of explainable artificial intelligence for human-aligned conversational explanations, http://dx.doi.org/10.1016/j.artint.2021.103525

Bignold A; Cruz F; Dazeley R; Vamplew P; Foale C, 2021, Persistent Rule-based Interactive Reinforcement Learning, http://arxiv.org/abs/2102.02441v2

Cuevas J; Henriquez C; Cruz F, 2020, Towards Assistive Diagnoses in m-Health: A Gray-box Neural Model for Cerebral Autoregulation Index, http://arxiv.org/abs/2011.12115v1

Bignold A; Cruz F; Dazeley R; Vamplew P; Foale C, 2020, Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning, http://dx.doi.org/10.1007/s00521-021-06850-6

Contreras R; Ayala A; Cruz F, 2020, Unmanned Aerial Vehicle Control Through Domain-based Automatic Speech Recognition, http://dx.doi.org/10.3390/computers9030075

Ayala A; Fernandes B; Cruz F; Macêdo D; Oliveira ALI; Zanchettin C, 2020, KutralNet: A Portable Deep Learning Model for Fire Recognition, http://dx.doi.org/10.1109/IJCNN48605.2020.9207202

Ayala A; Cruz F; Campos D; Rubio R; Fernandes B; Dazeley R, 2020, A Comparison of Humanoid Robot Simulators: A Quantitative Approach, http://arxiv.org/abs/2008.04627v1

Barros P; Tanevska A; Cruz F; Sciutti A, 2020, Moody Learners -- Explaining Competitive Behaviour of Reinforcement Learning Agents, http://arxiv.org/abs/2007.16045v1

Moreira I; Rivas J; Cruz F; Dazeley R; Ayala A; Fernandes B, 2020, Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environment, http://dx.doi.org/10.3390/app10165574

Bignold A; Cruz F; Taylor ME; Brys T; Dazeley R; Vamplew P; Foale C, 2020, A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review, http://dx.doi.org/10.1007/s12652-021-03489-y

Cruz F; Dazeley R; Vamplew P; Moreira I, 2020, Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario, http://dx.doi.org/10.1007/s00521-021-06425-5

Cruz F; Magg S; Nagai Y; Wermter S, 2019, Improving interactive reinforcement learning: What makes a good teacher?, http://dx.doi.org/10.1080/09540091.2018.1443318

Back to profile page