By Dr Francisco Cruz Naranjo

Conference Papers

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; 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

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