Memory Effect in Expressed Emotions During Long Term Group Interactions

Gorbunov, Roman D.; Barakova, Emilia; Rauterberg, Matthias

doi:10.1007/978-3-319-59740-9_25

Cited by 3 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The limitation on designing, implementing and deploying realistic interaction scenarios, in particular where affect plays a role, is one of the problems that must be solved before the deployment of such robots in real life. In a previous attempt without using a robot, a game specially designed to provoke to favor one and disfavor another player in a multiplayer interaction was shown to provoke emotions [8], which lasted during the repetitive games between the same players [9]. Adding a robot to such a scenario, however, would demand an incredible powerful simulation environment, or several rounds of real-life experiments in order to collect enough real-world data to adapt learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

The Chef's Hat Simulation Environment for Reinforcement-Learning-Based Agents

Barros,

Bloem,

Hootsmans

et al. 2020

Preprint

View full text Add to dashboard Cite

To achieve social interactions within Human-Robot Interaction (HRI) environments is a very challenging task. Most of the current research focuses on Wizard-of-Oz approaches, which neglect the recent development of intelligent robots. On the other hand, real-world scenarios usually do not provide the necessary control and reproducibility which are needed for learning algorithms. In this paper, we propose a virtual simulation environment that implements the Chef's Hat card game, designed to be used in HRI scenarios, to provide a controllable and reproducible scenario for reinforcementlearning algorithms.

show abstract

Section: Introductionmentioning

confidence: 99%

The Chef's Hat Simulation Environment for Reinforcement-Learning-Based Agents

Barros,

Bloem,

Hootsmans

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…To overcome this limitation, several human-robot-and human-agent-based scenarios are developed as games, as they have been proven to capture some relevant aspects of natural group interaction. For instance, a game specially designed to favor one player and disfavor another player in a multiplayer interaction was shown to provoke emotional reactions (Barakova et al, 2015), which lasted during the repetitive games between the same players (Gorbunov et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

You Were Always on My Mind: Introducing Chef’s Hat and COPPER for Personalized Reinforcement Learning

et al. 2021

View full text Add to dashboard Cite

Reinforcement learning simulation environments pose an important experimental test bed and facilitate data collection for developing AI-based robot applications. Most of them, however, focus on single-agent tasks, which limits their application to the development of social agents. This study proposes the Chef’s Hat simulation environment, which implements a multi-agent competitive card game that is a complete reproduction of the homonymous board game, designed to provoke competitive strategies in humans and emotional responses. The game was shown to be ideal for developing personalized reinforcement learning, in an online learning closed-loop scenario, as its state representation is extremely dynamic and directly related to each of the opponent’s actions. To adapt current reinforcement learning agents to this scenario, we also developed the COmPetitive Prioritized Experience Replay (COPPER) algorithm. With the help of COPPER and the Chef’s Hat simulation environment, we evaluated the following: (1) 12 experimental learning agents, trained via four different regimens (self-play, play against a naive baseline, PER, or COPPER) with three algorithms based on different state-of-the-art learning paradigms (PPO, DQN, and ACER), and two “dummy” baseline agents that take random actions, (2) the performance difference between COPPER and PER agents trained using the PPO algorithm and playing against different agents (PPO, DQN, and ACER) or all DQN agents, and (3) human performance when playing against two different collections of agents. Our experiments demonstrate that COPPER helps agents learn to adapt to different types of opponents, improving the performance when compared to off-line learning models. An additional contribution of the study is the formalization of the Chef’s Hat competitive game and the implementation of the Chef’s Hat Player Club, a collection of trained and assessed agents as an enabler for embedding human competitive strategies in social continual and competitive reinforcement learning.

show abstract

“…The limitation on designing, implementing and deploying realistic interaction scenarios, where affective information is more complex to capture and respond to is one of the problems that must be solved before the deployment of robots in real life. In a previous attempt without using a robot, a game especially designed to provoke to favor one and disfavor another player in a multiplayer interaction was shown to provoke emotions [1], which lasted during the repetitive games between the same players [7].…”

Section: Introductionmentioning

confidence: 99%

It's Food Fight! Introducing the Chef's Hat Card Game for Affective-Aware HRI

Barros,

Sciutti,

Bloem

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

Memory Effect in Expressed Emotions During Long Term Group Interactions

Cited by 3 publications

References 12 publications

The Chef's Hat Simulation Environment for Reinforcement-Learning-Based Agents

The Chef's Hat Simulation Environment for Reinforcement-Learning-Based Agents

You Were Always on My Mind: Introducing Chef’s Hat and COPPER for Personalized Reinforcement Learning

It's Food Fight! Introducing the Chef's Hat Card Game for Affective-Aware HRI

Contact Info

Product

Resources

About