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.
1 code implementation • 29 Sep 2023 • Clémence Grislain, Hugo Caselles-Dupré, Olivier Sigaud, Mohamed Chetouani
To this end, human teachers seem to build mental models of the learner's internal state, a capacity known as Theory of Mind (ToM).
no code implementations • 18 Aug 2023 • Hugo Caselles-Dupré, Olivier Sigaud, Mohamed Chetouani
We introduce a novel category of GC-agents capable of functioning as both teachers and learners.
no code implementations • 30 Sep 2022 • Hanan Salam, Oya Celiktutan, Hatice Gunes, Mohamed Chetouani
An integral part of seamless human-human communication is engagement, the process by which two or more participants establish, maintain, and end their perceived connection.
no code implementations • 26 Sep 2022 • Hugo Caselles-Dupré, Olivier Sigaud, Mohamed Chetouani
Teaching an agent to perform new tasks using natural language can easily be hindered by ambiguities in interpretation.
1 code implementation • 9 Jun 2022 • Hugo Caselles-Dupré, Olivier Sigaud, Mohamed Chetouani
In this paper, we implement pedagogy and pragmatism mechanisms by leveraging a Bayesian model of Goal Inference from demonstrations (BGI).
no code implementations • 11 May 2022 • Sera Buyukgoz, Jasmin Grosinger, Mohamed Chetouani, Alessandro Saffiotti
One way is to recognize humans' intentions and to act to fulfill them, like opening the door that you are about to cross.
no code implementations • 28 Feb 2022 • Hugo Caselles-Dupré, Mohamed Chetouani, Olivier Sigaud
When demonstrating a task, human tutors pedagogically modify their behavior by either "showing" the task rather than just "doing" it (exaggerating on relevant parts of the demonstration) or by giving demonstrations that best disambiguate the communicated goal.
no code implementations • 30 Jan 2022 • Ramona Merhej, Fernando P. Santos, Francisco S. Melo, Mohamed Chetouani, Francisco C. Santos
In this paper we investigate the consequences of risk diversity in groups of agents learning to play CRDs.
no code implementations • 25 May 2021 • Olivier Sigaud, Ahmed Akakzia, Hugo Caselles-Dupré, Cédric Colas, Pierre-Yves Oudeyer, Mohamed Chetouani
In the field of Artificial Intelligence, these extremes respectively map to autonomous agents learning from their own signals and interactive learning agents fully taught by their teachers.
1 code implementation • ICLR 2021 • Ahmed Akakzia, Cédric Colas, Pierre-Yves Oudeyer, Mohamed Chetouani, Olivier Sigaud
In a second stage (L -> G), it trains a language-conditioned goal generator to generate semantic goals that match the constraints expressed in language-based inputs.
no code implementations • 12 Jun 2020 • Cédric Colas, Ahmed Akakzia, Pierre-Yves Oudeyer, Mohamed Chetouani, Olivier Sigaud
In the real world, linguistic agents are also embodied agents: they perceive and act in the physical world.
no code implementations • ICML Workshop LaReL 2020 • Cédric Colas, Ahmed Akakzia, Pierre-Yves Oudeyer, Mohamed Chetouani, Olivier Sigaud
In the real world, linguistic agents are also embodied agents: they perceive and act in the physical world.
no code implementations • 22 May 2020 • Anis Najar, Mohamed Chetouani
In this paper, we provide an overview of the existing methods for integrating human advice into a Reinforcement Learning process.
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.
no code implementations • 5 Feb 2019 • Anis Najar, Olivier Sigaud, Mohamed Chetouani
In this paper, we propose a framework that enables a human teacher to shape a robot behaviour by interactively providing it with unlabeled instructions.
no code implementations • 28 Jan 2019 • Pierre Fournier, Olivier Sigaud, Cédric Colas, Mohamed Chetouani
In this paper we study a new reinforcement learning setting where the environment is non-rewarding, contains several possibly related objects of various controllability, and where an apt agent Bob acts independently, with non-observable intentions.
1 code implementation • 15 Oct 2018 • Cédric Colas, Pierre Fournier, Olivier Sigaud, Mohamed Chetouani, Pierre-Yves Oudeyer
In open-ended environments, autonomous learning agents must set their own goals and build their own curriculum through an intrinsically motivated exploration.
2 code implementations • 25 Jun 2018 • Pierre Fournier, Olivier Sigaud, Mohamed Chetouani, Pierre-Yves Oudeyer
In this paper, we investigate a new form of automated curriculum learning based on adaptive selection of accuracy requirements, called accuracy-based curriculum learning.