Search Results for author: Mohamed Chetouani

Found 17 papers, 4 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

Automatic Context-Driven Inference of Engagement in HMI: A Survey

no code implementations30 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.

Overcoming Referential Ambiguity in Language-Guided Goal-Conditioned Reinforcement Learning

no code implementations26 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.


Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments

1 code implementation9 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).

Two ways to make your robot proactive: reasoning about human intentions, or reasoning about possible futures

no code implementations11 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.

Pedagogical Demonstrations and Pragmatic Learning in Artificial Tutor-Learner Interactions

no code implementations28 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.

Learning Collective Action under Risk Diversity

no code implementations30 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.

Towards Teachable Autotelic Agents

no code implementations25 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.

Grounding Language to Autonomously-Acquired Skills via Goal Generation

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.

Language Acquisition

Reinforcement learning with human advice: a survey

no code implementations22 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.


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.

Interactively shaping robot behaviour with unlabeled human instructions

no code implementations5 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.


CLIC: Curriculum Learning and Imitation for object Control in non-rewarding environments

no code implementations28 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.

reinforcement-learning Unsupervised Reinforcement Learning

CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning

1 code implementation15 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.

Efficient Exploration Multi-Goal Reinforcement Learning +1

Accuracy-based Curriculum Learning in Deep Reinforcement Learning

2 code implementations25 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.


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