no code implementations • 18 Jan 2024 • Zihan Lin, Francisco Cruz, Eduardo Benitez Sandoval
In the multimodal, accuracy has experienced an elevation from 77. 48% to 78. 93%.
no code implementations • 25 Aug 2023 • Jasmina Bernotat, Doreen Jirak, Eduardo Benitez Sandoval, Francisco Cruz
We present a research outline that aims at investigating group dynamics and peer pressure in the context of industrial robots.
no code implementations • 14 Dec 2022 • Hugo Muñoz, Ernesto Portugal, Angel Ayala, Bruno Fernandes, Francisco Cruz
The results obtained showed that it is possible to use the memory-based method in hierarchical environments with high-level tasks and compute the probabilities of success to be used as a basis for explaining the agent's behavior.
no code implementations • 6 Dec 2022 • Cristian Millán-Arias, Ruben Contreras, Francisco Cruz, Bruno Fernandes
RL is a machine learning paradigm wherein an agent interacts with an environment to solve a given task.
no code implementations • 23 Nov 2022 • Niclas Schroeter, Francisco Cruz, Stefan Wermter
Results obtained show the viability of introspection for episodic robotics tasks and, additionally, that the introspection-based approach can be used to generate explanations for the actions taken in a non-episodic robotics environment as well.
no code implementations • 11 Oct 2022 • Francisco Cruz, Adam Bignold, Hung Son Nguyen, Richard Dazeley, Peter Vamplew
The use of interactive advice in reinforcement learning scenarios allows for speeding up the learning process for autonomous agents.
no code implementations • 7 Oct 2022 • Adrian Ly, Richard Dazeley, Peter Vamplew, Francisco Cruz, Sunil Aryal
However, divergent and unstable behaviour have been long standing issues in DQNs.
no code implementations • 7 Jul 2022 • Francisco Cruz, Charlotte Young, Richard Dazeley, Peter Vamplew
In this work, we make use of human-like explanations built from the probability of success to complete the goal that an autonomous robot shows after performing an action.
no code implementations • 15 Oct 2021 • Hung Son Nguyen, Francisco Cruz, Richard Dazeley
However, current research has been limited to interactions that offer actionable advice to only the current state of the agent.
no code implementations • 20 Aug 2021 • Richard Dazeley, Peter Vamplew, Francisco Cruz
EXplainable RL (XRL) is relatively recent field of research that aims to develop techniques to extract concepts from the agent's: perception of the environment; intrinsic/extrinsic motivations/beliefs; Q-values, goals and objectives.
no code implementations • 18 Aug 2021 • Angel Ayala, Francisco Cruz, Bruno Fernandes, Richard Dazeley
Explainable reinforcement learning allows artificial agents to explain their behavior in a human-like manner aiming at non-expert end-users.
no code implementations • 8 Aug 2021 • Cristian Millán-Arias, Bruno Fernandes, Francisco Cruz
This behavior is an essential part of the communication process due to delimit the acceptable distance to interact with another being.
no code implementations • 7 Jul 2021 • Richard Dazeley, Peter Vamplew, Cameron Foale, Charlotte Young, Sunil Aryal, Francisco Cruz
Over the last few years there has been rapid research growth into eXplainable Artificial Intelligence (XAI) and the closely aligned Interpretable Machine Learning (IML).
no code implementations • 4 Feb 2021 • Adam Bignold, Francisco Cruz, Richard Dazeley, Peter Vamplew, Cameron Foale
Interactive reinforcement learning has allowed speeding up the learning process in autonomous agents by including a human trainer providing extra information to the agent in real-time.
no code implementations • 17 Nov 2020 • Jorge Cuevas, Claudio Henriquez, Francisco Cruz
The cerebral autoregulation system (CAS), is a mechanism which aims to regulate pressure variations occurring in the cerebral circulatory system.
no code implementations • 21 Sep 2020 • Adam Bignold, Francisco Cruz, Richard Dazeley, Peter Vamplew, Cameron Foale
When interacting with a learner agent, humans may provide either evaluative or informative advice.
no code implementations • 9 Sep 2020 • Ruben Contreras, Angel Ayala, Francisco Cruz
The obtained results show that the unmanned aerial vehicle is capable of interpreting user voice instructions achieving an improvement in speech-to-action recognition for both languages when using phoneme matching in comparison to only using the cloud-based algorithm without domain-based instructions.
1 code implementation • 16 Aug 2020 • Angel Ayala, Bruno Fernandes, Francisco Cruz, David Macêdo, Adriano L. I. Oliveira, Cleber Zanchettin
The experiments show that our model keeps high accuracy while substantially reducing the number of parameters and flops.
no code implementations • 30 Jul 2020 • Pablo Barros, Ana Tanevska, Francisco Cruz, Alessandra Sciutti
Designing the decision-making processes of artificial agents that are involved in competitive interactions is a challenging task.
no code implementations • 7 Jul 2020 • Ithan Moreira, Javier Rivas, Francisco Cruz, Richard Dazeley, Angel Ayala, Bruno Fernandes
We compare three different learning methods using a simulated robotic arm for the task of organizing different objects; the proposed methods are (i) deep reinforcement learning (DeepRL); (ii) interactive deep reinforcement learning using a previously trained artificial agent as an advisor (agent-IDeepRL); and (iii) interactive deep reinforcement learning using a human advisor (human-IDeepRL).
no code implementations • 3 Jul 2020 • Adam Bignold, Francisco Cruz, Matthew E. Taylor, Tim Brys, Richard Dazeley, Peter Vamplew, Cameron Foale
In this work, while reviewing externally-influenced methods, we propose a conceptual framework and taxonomy for assisted reinforcement learning, aimed at fostering collaboration by classifying and comparing various methods that use external information in the learning process.
no code implementations • 24 Jun 2020 • Francisco Cruz, Richard Dazeley, Peter Vamplew, Ithan Moreira
As a way to explain the goal-driven robot's actions, we use the probability of success computed by three different proposed approaches: memory-based, learning-based, and introspection-based.
no code implementations • 15 Apr 2019 • Francisco Cruz, Sven Magg, Yukie Nagai, Stefan Wermter
Interactive reinforcement learning has become an important apprenticeship approach to speed up convergence in classic reinforcement learning problems.
no code implementations • 26 Jul 2018 • Francisco Cruz, German I. Parisi, Stefan Wermter
Additionally, we modulate the influence of sensory-driven feedback in the IRL task using goal-oriented knowledge in terms of contextual affordances.
no code implementations • 7 May 2018 • Francisco Cruz, Oriol Ramos Terrades
We successfully combine Expectation-Maximization algorithm and variational approaches for parameter learning and computing inference on Markov random felds.