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.
no code implementations • 20 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.
1 code implementation • 12 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
no code implementations • 19 Sep 2022 • Miguel Faria, Francisco S. Melo, Ana Paiva
In this paper we investigate the notion of legibility in sequential decision tasks under uncertainty.
no code implementations • 2 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.
1 code implementation • 7 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.
1 code implementation • 7 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.
no code implementations • 4 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
1 code implementation • 6 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.
no code implementations • 4 Jun 2020 • Miguel Vasco, Francisco S. Melo, Ana Paiva
Humans are able to create rich representations of their external reality.
no code implementations • 11 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.
1 code implementation • 28 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.
1 code implementation • 24 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.
no code implementations • 6 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.