Select Publications
Preprints
2024, Adaptive Alignment: Dynamic Preference Adjustments via Multi-Objective Reinforcement Learning for Pluralistic AI, http://arxiv.org/abs/2410.23630v1
,2024, Contextual Affordances for Safe Exploration in Robotic Scenarios, http://arxiv.org/abs/2405.06422v1
,2024, Self context-aware emotion perception on human-robot interaction, http://arxiv.org/abs/2401.10946v1
,2023, Asch Meets HRI: Human Conformity to Robot Groups, http://arxiv.org/abs/2308.13307v1
,2023, Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposal, http://arxiv.org/abs/2302.03180v2
,2022, Explaining Agent's Decision-making in a Hierarchical Reinforcement Learning Scenario, http://arxiv.org/abs/2212.06967v1
,2022, Reinforcement Learning for UAV control with Policy and Reward Shaping, http://arxiv.org/abs/2212.03828v1
,2022, Introspection-based Explainable Reinforcement Learning in Episodic and Non-episodic Scenarios, http://arxiv.org/abs/2211.12930v1
,2022, Broad-persistent Advice for Interactive Reinforcement Learning Scenarios, http://arxiv.org/abs/2210.05187v1
,2022, Elastic Step DQN: A novel multi-step algorithm to alleviate overestimation in Deep QNetworks, http://arxiv.org/abs/2210.03325v1
,2022, Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios, http://arxiv.org/abs/2207.03214v1
,2021, A Broad-persistent Advising Approach for Deep Interactive Reinforcement Learning in Robotic Environments, http://arxiv.org/abs/2110.08003v2
,2021, Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey, http://arxiv.org/abs/2108.09003v1
,2021, Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Task, http://arxiv.org/abs/2108.08911v1
,2021, Learning Proxemic Behavior Using Reinforcement Learning with Cognitive Agents, http://arxiv.org/abs/2108.03730v1
,2021, Levels of explainable artificial intelligence for human-aligned conversational explanations, http://dx.doi.org/10.1016/j.artint.2021.103525
,2021, Persistent Rule-based Interactive Reinforcement Learning, http://arxiv.org/abs/2102.02441v2
,2020, Towards Assistive Diagnoses in m-Health: A Gray-box Neural Model for Cerebral Autoregulation Index, http://arxiv.org/abs/2011.12115v1
,2020, Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning, http://dx.doi.org/10.1007/s00521-021-06850-6
,2020, Unmanned Aerial Vehicle Control Through Domain-based Automatic Speech Recognition, http://dx.doi.org/10.3390/computers9030075
,2020, KutralNet: A Portable Deep Learning Model for Fire Recognition, http://dx.doi.org/10.1109/IJCNN48605.2020.9207202
,2020, A Comparison of Humanoid Robot Simulators: A Quantitative Approach, http://arxiv.org/abs/2008.04627v1
,2020, Moody Learners -- Explaining Competitive Behaviour of Reinforcement Learning Agents, http://arxiv.org/abs/2007.16045v1
,2020, Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environment, http://dx.doi.org/10.3390/app10165574
,2020, A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review, http://dx.doi.org/10.1007/s12652-021-03489-y
,2020, Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario, http://dx.doi.org/10.1007/s00521-021-06425-5
,2019, Improving interactive reinforcement learning: What makes a good teacher?, http://dx.doi.org/10.1080/09540091.2018.1443318
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