scholarly journals A Novel Training and Collaboration Integrated Framework for Human–Agent Teleoperation

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8341
Author(s):  
Zebin Huang ◽  
Ziwei Wang ◽  
Weibang Bai ◽  
Yanpei Huang ◽  
Lichao Sun ◽  
...  
Keyword(s):  
Dynamic Environments ◽  
Agent Based ◽  
Integrated Framework ◽  
Human Agent ◽  
Bilateral Training ◽  
Promising Solution ◽  
Human Operators ◽  
Operational Errors ◽  
Operational Time ◽  
Or Task

Human operators have the trend of increasing physical and mental workloads when performing teleoperation tasks in uncertain and dynamic environments. In addition, their performances are influenced by subjective factors, potentially leading to operational errors or task failure. Although agent-based methods offer a promising solution to the above problems, the human experience and intelligence are necessary for teleoperation scenarios. In this paper, a truncated quantile critics reinforcement learning-based integrated framework is proposed for human–agent teleoperation that encompasses training, assessment and agent-based arbitration. The proposed framework allows for an expert training agent, a bilateral training and cooperation process to realize the co-optimization of agent and human. It can provide efficient and quantifiable training feedback. Experiments have been conducted to train subjects with the developed algorithm. The performances of human–human and human–agent cooperation modes are also compared. The results have shown that subjects can complete the tasks of reaching and picking and placing with the assistance of an agent in a shorter operational time, with a higher success rate and less workload than human–human cooperation.

Rule Engines and Agent-Based Systems

2012 ◽  
pp. 211-218 ◽  
Author(s):  
Agostino Poggi ◽  
Michele Tomaiuolo
Keyword(s):  
Dynamic Environments ◽  
Multi Agent Systems ◽  
Rule Engine ◽  
Rule Based ◽  
Agent Based ◽  
Agent Systems ◽  
Loosely Coupled ◽  
Domain Specific ◽  
Multi Agent ◽  
Good Support

Expert systems are successfully applied to a number of domains. Often built on generic rule-based systems, they can also exploit optimized algorithms. On the other side, being based on loosely coupled components and peer to peer infrastructures for asynchronous messaging, multi-agent systems allow code mobility, adaptability, easy of deployment and reconfiguration, thus fitting distributed and dynamic environments. Also, they have good support for domain specific ontologies, an important feature when modelling human experts’ knowledge. The possibility of obtaining the best features of both technologies is concretely demonstrated by the integration of JBoss Rules, a rule engine efficiently implementing the Rete-OO algorithm, into JADE, a FIPA-compliant multi-agent system.

Agents and Social Interaction Insights from Social Psychology

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.

Agents and Social Interaction

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.

scholarly journals Organisational plasticity: can we really model human–agent behaviours?

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dale Richards
Keyword(s):  
Sudden Change ◽  
Computational Modelling ◽  
Human Behaviour ◽  
Complex Nature ◽  
Key Factors ◽  
Content Type ◽  
Agent Based ◽  
Human Agent ◽  
External Pressures ◽  
Behaviour Models

PurposeThe ability for an organisation to adapt and respond to external pressures is a beneficial activity towards optimising efficiency and increasing the likelihood of achieving set goals. It can also be suggested that this very ability to adapt to one's surroundings is one of the key factors of resilience. The nature of dynamically responding to sudden change and then to return to a state that is efficient may be termed as possessing the characteristic of plasticity. Uses of agent-based systems in assisting in organisational processes may have a hand in facilitating an organisations' plasticity, and computational modelling has often been used to try and predict both agent and human behaviour. Such models also promise the ability to examine the dynamics of organisational plasticity through the direct manipulation of key factors. This paper discusses the use of such models in application to organisational plasticity and in particular the relevance to human behaviour and perception of agent-based modelling. The uses of analogies for explaining organisational plasticity is also discussed, with particular discussion around the use of modelling. When the authors consider the means by which the authors can adopt theories to explain this type of behaviour, models tend to focus on aspects of predictability. This in turn loses a degree of realism when we consider the complex nature of human behaviour, and more so that of human–agent behaviour.Design/methodology/approachThe methodology and approach used for this paper is reflected in the review of the literature and research.FindingsThe use of human–agent behaviour models in organisational plasticity is discussed in this paper.Originality/valueThe originality of this paper is based on the importance of considering the human–agent-based models. When compared to agent-based model approaches, analogy is used as a narrative in this paper.

A Design Strategy for Multi-Agent Based Industrial Intelligent Monitor Processing

2012 ◽  
Vol 224 ◽  
pp. 184-188 ◽  
Author(s):  
Hai Rui Wang ◽  
Ya Li ◽  
Jian Ying Wang ◽  
Gui Hong Bi ◽  
Zhi Bin Zhang
Keyword(s):  
Processing System ◽  
Object Oriented ◽  
Good Choice ◽  
Point Of View ◽  
Object Oriented Programming ◽  
Multi Agent System ◽  
Agent Based ◽  
Scada System ◽  
Multi Agent ◽  
Human Operators

In this paper, a design issue related to the multi-agent based industrial intelligent monitor processing system (IIMPS) in distributed substations is proposed. Multiagent-oriented programming might be the new trend of software programming after object-oriented programming. Multagent-oriented approach uses a higher-level point of view to see the problems. We comment on the characteristics of industrial monitor problems and claim that the multi-agent system (MAS) is a good choice to solve problems with these characteristics. The proposed multi-agent based IIMPS is able to analyze industrial all real messages to assist human operators in identifying associated events. A prototype MAS with JAVA-based GUI used to integrate the existing SCADA system and as the user interface for operators to perform will be discussed in the paper.

scholarly journals Group formation on a small-world: experiment and modelling

2019 ◽  
Vol 16 (156) ◽  
pp. 20180814 ◽  
Author(s):  
Kunal Bhattacharya ◽  
Tuomas Takko ◽  
Daniel Monsivais ◽  
Kimmo Kaski
Keyword(s):  
Group Formation ◽  
Small World ◽  
Limited Information ◽  
Online Game ◽  
Probability Matching ◽  
Agent Based ◽  
Human Agent ◽  
Neighbourhood Information ◽  
Human Participants ◽  
Parameter Ranges

As a step towards studying human-agent collectives, we conduct an online game with human participants cooperating on a network. The game is presented in the context of achieving group formation through local coordination. The players set initially to a small-world network with limited information on the location of other players, coordinate their movements to arrange themselves into groups. To understand the decision-making process, we construct a data-driven model of agents based on probability matching. The model allows us to gather insight into the nature and degree of rationality employed by the human players. By varying the parameters in agent-based simulations, we are able to benchmark the human behaviour. We observe that while the players use the neighbourhood information in limited capacity, the perception of risk is optimal. We also find that for certain parameter ranges, the agents are able to act more efficiently when compared to the human players. This approach would allow us to simulate the collective dynamics in games with agents having varying strategies playing alongside human proxies.

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