Search Results for author: Ana Paiva

Found 14 papers, 6 papers with code

Learning from Explanations and Demonstrations: A Pilot Study

no code implementations ACL (NL4XAI, INLG) 2020 Silvia Tulli, Sebastian Wallkötter, Ana Paiva, Francisco S. Melo, Mohamed Chetouani

AI has become prominent in a growing number of systems, and, as a direct consequence, the desire for explainability in such systems has become prominent as well.

Transfer Learning

From Modelling to Understanding Children's Behaviour in the Context of Robotics and Social Artificial Intelligence

no code implementations20 Oct 2022 Serge Thill, Vicky Charisi, Tony Belpaeme, Ana Paiva

Understanding and modelling children's cognitive processes and their behaviour in the context of their interaction with robots and social artificial intelligence systems is a fundamental prerequisite for meaningful and effective robot interventions.

Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning

1 code implementation12 Oct 2022 Pedro P. Santos, Diogo S. Carvalho, Miguel Vasco, Alberto Sardinha, Pedro A. Santos, Ana Paiva, Francisco S. Melo

We introduce hybrid execution in multi-agent reinforcement learning (MARL), a new paradigm in which agents aim to successfully complete cooperative tasks with arbitrary communication levels at execution time by taking advantage of information-sharing among the agents.

Multi-agent Reinforcement Learning reinforcement-learning +1

Avant-Satie! Using ERIK to encode task-relevant expressivity into the animation of autonomous social robots

no code implementations2 Mar 2022 Tiago Ribeiro, Ana Paiva

ERIK is an expressive inverse kinematics technique that has been previously presented and evaluated both algorithmically and in a limited user-interaction scenario.

Geometric Multimodal Contrastive Representation Learning

1 code implementation7 Feb 2022 Petra Poklukar, Miguel Vasco, Hang Yin, Francisco S. Melo, Ana Paiva, Danica Kragic

Learning representations of multimodal data that are both informative and robust to missing modalities at test time remains a challenging problem due to the inherent heterogeneity of data obtained from different channels.

Reinforcement Learning (RL) Representation Learning

How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning Agents

1 code implementation7 Oct 2021 Miguel Vasco, Hang Yin, Francisco S. Melo, Ana Paiva

This work addresses the problem of sensing the world: how to learn a multimodal representation of a reinforcement learning agent's environment that allows the execution of tasks under incomplete perceptual conditions.

Atari Games reinforcement-learning +1

FAtiMA Toolkit -- Toward an effective and accessible tool for the development of intelligent virtual agents and social robots

no code implementations4 Mar 2021 Samuel Mascarenhas, Manuel Guimarães, Pedro A. Santos, João Dias, Rui Prada, Ana Paiva

More than a decade has passed since the development of FearNot!, an application designed to help children deal with bullying through role-playing with virtual characters.

Decision Making Multiagent Systems Human-Computer Interaction Robotics

Explainable Agency by Revealing Suboptimality in Child-Robot Learning Scenarios

1 code implementation6 Nov 2020 Silvia Tulli, Marta Couto, Miguel Vasco, Elmira Yadollahi, Francisco Melo, Ana Paiva

In the application scenario, the child and the robot learn together how to play a zero-sum game that requires logical and mathematical thinking.

Explanation Generation

Explainable Agents Through Social Cues: A Review

no code implementations11 Mar 2020 Sebastian Wallkotter, Silvia Tulli, Ginevra Castellano, Ana Paiva, Mohamed Chetouani

One reason for this high variance in terminology is the unique array of social cues that embodied agents can access in contrast to that accessed by non-embodied agents.

Playing Games in the Dark: An approach for cross-modality transfer in reinforcement learning

1 code implementation28 Nov 2019 Rui Silva, Miguel Vasco, Francisco S. Melo, Ana Paiva, Manuela Veloso

In this work we explore the use of latent representations obtained from multiple input sensory modalities (such as images or sounds) in allowing an agent to learn and exploit policies over different subsets of input modalities.

OpenAI Gym reinforcement-learning +1

Software architecture for YOLO, a creativity-stimulating robot

1 code implementation24 Sep 2019 Patrícia Alves-Oliveira, Samuel Gomes, Ankita Chandak, Patrícia Arriaga, Guy Hoffman, Ana Paiva

Additionally, this software allows the creation of Social Behaviors that enable the robot to behave as a believable character.

Learning multimodal representations for sample-efficient recognition of human actions

no code implementations6 Mar 2019 Miguel Vasco, Francisco S. Melo, David Martins de Matos, Ana Paiva, Tetsunari Inamura

In this work we present \textit{motion concepts}, a novel multimodal representation of human actions in a household environment.

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