Multi-party referential communication in complex strategic games

Verbal communication is an ubiquitous aspect of human interaction occurring in many contexts; however, it is primarily studied in the limited context of two people communicating information. Understanding communication in complex, multi-party interactions is both a scientific challenge for psycholinguistics and an engineering challenge for creating artificial agents who can participate in these richer contexts. We adapted the reference game paradigm to an online 3-player game where players refer to objects in order to coordinate selections based on the available utilities. We ran games with shared or individual payoffs and with or without access to language. Our paradigm can also be used for artificial agents; we trained reinforcement learning-based agents on the same task as a comparison. Our dataset shows the same patterns found in simpler reference games and contains rich language of reference and negotiation.

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The role and relationship of mindreading and social attunement in HRI – position statements of interdisciplinary researchers. Workshop HRI'20

10.31234/osf.io/3p6cd ◽

2020 ◽

Author(s):

Agnieszka Wykowska ◽

Jairo Pérez-Osorio ◽

Stefan Kopp

Keyword(s):

Mental States ◽

Human Robot Interaction ◽

Human Interaction ◽

Artificial Agents ◽

The Novel ◽

Robot Interaction ◽

Novel Coronavirus ◽

Relationship Of ◽

The Relationship

This booklet is a collection of the position statements accepted for the HRI’20 conference workshop “Social Cognition for HRI: Exploring the relationship between mindreading and social attunement in human-robot interaction” (Wykowska, Perez-Osorio & Kopp, 2020). Unfortunately, due to the rapid unfolding of the novel coronavirus at the beginning of the present year, the conference and consequently our workshop, were canceled. On the light of these events, we decided to put together the positions statements accepted for the workshop. The contributions collected in these pages highlight the role of attribution of mental states to artificial agents in human-robot interaction, and precisely the quality and presence of social attunement mechanisms that are known to make human interaction smooth, efficient, and robust. These papers also accentuate the importance of the multidisciplinary approach to advance the understanding of the factors and the consequences of social interactions with artificial agents.

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An Evaluation Methodology for Interactive Reinforcement Learning with Simulated Users

Biomimetics ◽

10.3390/biomimetics6010013 ◽

2021 ◽

Vol 6 (1) ◽

pp. 13

Author(s):

Adam Bignold ◽

Francisco Cruz ◽

Richard Dazeley ◽

Peter Vamplew ◽

Cameron Foale

Keyword(s):

Reinforcement Learning ◽

Information Source ◽

Human Interaction ◽

Evaluation Methodology ◽

External Information ◽

Preliminary Evaluation ◽

Learning Agents ◽

Learning Agent ◽

Knowledge Bias ◽

The Impact

Interactive reinforcement learning methods utilise an external information source to evaluate decisions and accelerate learning. Previous work has shown that human advice could significantly improve learning agents’ performance. When evaluating reinforcement learning algorithms, it is common to repeat experiments as parameters are altered or to gain a sufficient sample size. In this regard, to require human interaction every time an experiment is restarted is undesirable, particularly when the expense in doing so can be considerable. Additionally, reusing the same people for the experiment introduces bias, as they will learn the behaviour of the agent and the dynamics of the environment. This paper presents a methodology for evaluating interactive reinforcement learning agents by employing simulated users. Simulated users allow human knowledge, bias, and interaction to be simulated. The use of simulated users allows the development and testing of reinforcement learning agents, and can provide indicative results of agent performance under defined human constraints. While simulated users are no replacement for actual humans, they do offer an affordable and fast alternative for evaluative assisted agents. We introduce a method for performing a preliminary evaluation utilising simulated users to show how performance changes depending on the type of user assisting the agent. Moreover, we describe how human interaction may be simulated, and present an experiment illustrating the applicability of simulating users in evaluating agent performance when assisted by different types of trainers. Experimental results show that the use of this methodology allows for greater insight into the performance of interactive reinforcement learning agents when advised by different users. The use of simulated users with varying characteristics allows for evaluation of the impact of those characteristics on the behaviour of the learning agent.

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GENDER FEATURES OF NON-VERBAL COMMUNICATION IN TELEVISED POLITICAL DEBATES

Bulletin of Yerevan Brusov State University of Languages and Social Sciences. Pedagogy and Social Sciences ◽

10.51307/18293107/laph/2021.1-152 ◽

2021 ◽

pp. 152-160

Author(s):

LUIZA MARABYAN

Keyword(s):

Social Development ◽

Nonverbal Communication ◽

Facial Expressions ◽

Verbal Communication ◽

Human Interaction ◽

Political Debates ◽

Surrounding Space ◽

Gender Characteristics ◽

Gender Features

LUIZA MARABYAN - GENDER FEATURES OF NON-VERBAL COMMUNICATION IN TELEVISED POLITICAL DEBATES The paper examines gender characteristics in nonverbal communication during televised political debates. Nonverbal communication plays an important role in the process of human interaction. Means of nonverbal communication as a kind of language of feelings are the same product of social development as the language of words. Among such means are facial expressions, views, postures, gestures, touches, behavior in the surrounding space. All these types of nonverbal messages interact, sometimes complementing each other, sometimes contradicting each other.

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Agents and Social Interaction Insights from Social Psychology

Social Computing ◽

10.4018/978-1-60566-984-7.ch007 ◽

2010 ◽

pp. 74-91

Author(s):

Joseph C. Bullington

Keyword(s):

Social Psychology ◽

Social Interaction ◽

Social Perception ◽

Attribution Theory ◽

Interpersonal Relations ◽

Human Interaction ◽

Artificial Agents ◽

Agent Based ◽

Human Agent ◽

Agent Interaction

Social interaction represents a powerful new locus of research in the quest to build more truly humanlike artificial agents. The work in this area, as in the field of human computer interaction, generally, is becoming more interdisciplinary in nature. In this spirit, the present chapter will survey concepts and theory from social psychology, a field many researchers may be unfamiliar with. Dennett’s notion of the intentional system will provide some initial grounding for the notion of social interaction, along with a brief discussion of conversational agents. The body of the chapter will then survey the areas of animal behavior and social psychology most relevant to human-agent interaction, concentrating on the areas of interpersonal relations and social perception. Within the area of social perception, the focus will be on the topics of emotion and attribution theory. Where relevant, research in the area of agent-human interaction will be discussed. The chapter will conclude with a brief survey of the use of agent-based modeling and simulation in social theory. The future looks very promising for researchers in this area; the complex problems involved in developing artificial agents who have mind-like attributes will require an interdisciplinary effort.

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Agents and Social Interaction

Handbook of Research on Agent-Based Societies ◽

10.4018/978-1-60566-236-7.ch003 ◽

2009 ◽

pp. 35-50

Author(s):

Joseph C. Bullington

Keyword(s):

Social Psychology ◽

Social Interaction ◽

Social Perception ◽

Attribution Theory ◽

Interpersonal Relations ◽

Human Interaction ◽

Artificial Agents ◽

Agent Based ◽

Human Agent ◽

Agent Interaction

Social interaction represents a powerful new locus of research in the quest to build more truly human-like artificial agents. The work in this area, as in the field of human computer interaction, generally, is becoming more interdisciplinary in nature. In this spirit, the present chapter will survey concepts and theory from social psychology, a field many researchers may be unfamiliar with. Dennett’s notion of the intentional system will provide some initial grounding for the notion of social interaction, along with a brief discussion of conversational agents. The body of the chapter will then survey the areas of animal behavior and social psychology most relevant to human-agent interaction, concentrating on the areas of interpersonal relations and social perception. Within the area of social perception, the focus will be on the topics of emotion and attribution theory. Where relevant, research in the area of agent-human interaction will be discussed. The chapter will conclude with a brief survey of the use of agent-based modeling and simulation in social theory. The future looks very promising for researchers in this area; the complex problems involved in developing artificial agents who have mind-like attributes will require an interdisciplinary effort.

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A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning

Sensors ◽

10.3390/s20123450 ◽

2020 ◽

Vol 20 (12) ◽

pp. 3450

Author(s):

Muhammad Diyan ◽

Bhagya Nathali Silva ◽

Kijun Han

Keyword(s):

Reinforcement Learning ◽

Energy Consumption ◽

Real Time ◽

Smart Home ◽

Scheduling Algorithm ◽

Smart Homes ◽

Human Interaction ◽

Household Appliances ◽

Slack Time ◽

Home User

Maintaining a fair use of energy consumption in smart homes with many household appliances requires sophisticated algorithms working together in real time. Similarly, choosing a proper schedule for appliances operation can be used to reduce inappropriate energy consumption. However, scheduling appliances always depend on the behavior of a smart home user. Thus, modeling human interaction with appliances is needed to design an efficient scheduling algorithm with real-time support. In this regard, we propose a scheduling algorithm based on human appliances interaction in smart homes using reinforcement learning (RL). The proposed scheduling algorithm divides the entire day into various states. In each state, the agents attached to household appliances perform various actions to obtain the highest reward. To adjust the discomfort which arises due to performing inappropriate action, the household appliances are categorized into three groups i.e., (1) adoptable, (2) un-adoptable, (3) manageable. Finally, the proposed system is tested for the energy consumption and discomfort level of the home user against our previous scheduling algorithm based on least slack time phenomenon. The proposed scheme outperforms the Least Slack Time (LST) based scheduling in context of energy consumption and discomfort level of the home user.

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Selfies as Duplex Non-verbal Communication: Human—Media Interaction, Human—Human Interaction, Case Study, and Research Manifesto

Frontiers in Computer Science ◽

10.3389/fcomp.2020.00012 ◽

2020 ◽

Vol 2 ◽

Author(s):

Nicola Bruno ◽

Stefano Uccelli ◽

Veronica Pisu ◽

Mauro Belluardo ◽

Elisa De Stefani

Keyword(s):

Verbal Communication ◽

Human Interaction ◽

Media Interaction

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Dynamic Resource Allocation with Integrated Reinforcement Learning for a D2D-Enabled LTE-A Network with Access to Unlicensed Band

Mobile Information Systems ◽

10.1155/2016/4565203 ◽

2016 ◽

Vol 2016 ◽

pp. 1-18 ◽

Cited By ~ 2

Author(s):

Alia Asheralieva ◽

Yoshikazu Miyanaga

Keyword(s):

Resource Allocation ◽

Reinforcement Learning ◽

Channel Allocation ◽

Dynamic Resource Allocation ◽

Throughput Maximization ◽

Cellular Radio ◽

Resource Allocation Algorithm ◽

Stochastic Optimization Problem ◽

Player Game ◽

Dynamic Resource

We propose a dynamic resource allocation algorithm for device-to-device (D2D) communication underlying a Long Term Evolution Advanced (LTE-A) network with reinforcement learning (RL) applied for unlicensed channel allocation. In a considered system, the inband and outband resources are assigned by the LTE evolved NodeB (eNB) to different device pairs to maximize the network utility subject to the target signal-to-interference-and-noise ratio (SINR) constraints. Because of the absence of an established control link between the unlicensed and cellular radio interfaces, the eNB cannot acquire any information about the quality and availability of unlicensed channels. As a result, a considered problem becomes a stochastic optimization problem that can be dealt with by deploying a learning theory (to estimate the random unlicensed channel environment). Consequently, we formulate the outband D2D access as a dynamic single-player game in which the player (eNB) estimates its possible strategy and expected utility for all of its actions based only on its own local observations using a joint utility and strategy estimation based reinforcement learning (JUSTE-RL) with regret algorithm. A proposed approach for resource allocation demonstrates near-optimal performance after a small number of RL iterations and surpasses the other comparable methods in terms of energy efficiency and throughput maximization.

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