Search Results for author: Francisco Cruz

Found 25 papers, 1 papers with code

Asch Meets HRI: Human Conformity to Robot Groups

no code implementations25 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.

Industrial Robots

Explaining Agent's Decision-making in a Hierarchical Reinforcement Learning Scenario

no code implementations14 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.

Decision Making Hierarchical Reinforcement Learning +2

Introspection-based Explainable Reinforcement Learning in Episodic and Non-episodic Scenarios

no code implementations23 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.

reinforcement-learning Reinforcement Learning (RL)

Broad-persistent Advice for Interactive Reinforcement Learning Scenarios

no code implementations11 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.

reinforcement-learning Reinforcement Learning (RL)

Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios

no code implementations7 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.

counterfactual Decision Making +3

Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey

no code implementations20 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.

Decision Making Explainable artificial intelligence +3

Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Task

no code implementations18 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.

Decision Making reinforcement-learning +1

Learning Proxemic Behavior Using Reinforcement Learning with Cognitive Agents

no code implementations8 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.

reinforcement-learning Reinforcement Learning (RL)

Levels of explainable artificial intelligence for human-aligned conversational explanations

no code implementations7 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).

Decision Making Explainable artificial intelligence +2

Persistent Rule-based Interactive Reinforcement Learning

no code implementations4 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.

reinforcement-learning Reinforcement Learning (RL)

Towards Assistive Diagnoses in m-Health: A Gray-box Neural Model for Cerebral Autoregulation Index

no code implementations17 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.

Unmanned Aerial Vehicle Control Through Domain-based Automatic Speech Recognition

no code implementations9 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.

Action Recognition Automatic Speech Recognition +2

KutralNet: A Portable Deep Learning Model for Fire Recognition

1 code implementation16 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.

Moody Learners -- Explaining Competitive Behaviour of Reinforcement Learning Agents

no code implementations30 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.

Decision Making reinforcement-learning +1

Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environment

no code implementations7 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).

reinforcement-learning Reinforcement Learning (RL)

A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review

no code implementations3 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.

Decision Making reinforcement-learning +2

Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario

no code implementations24 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.

Action Understanding Decision Making +2

Improving interactive reinforcement learning: What makes a good teacher?

no code implementations15 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.

reinforcement-learning Reinforcement Learning (RL)

Multi-modal Feedback for Affordance-driven Interactive Reinforcement Learning

no code implementations26 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.

reinforcement-learning Reinforcement Learning (RL)

A probabilistic framework for handwritten text line segmentation

no code implementations7 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.

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