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This article reports the results from a panel discussion held at the 2019 European Conference on Information Systems (ECIS) on the use of technology-based autonomous agents in collaborative work.

The panelists (Drs Izak Benbasat, Paul Benjamin Lowry, Stefan Morana, and Stefan Seidel) presented ideas related to affective and cognitive implications of using autonomous technology-based agents in terms of (1) emotional connection with these agents, (2) decision-making, and (3) knowledge and learning in settings with autonomous agents. These ideas provided the basis for a moderated panel discussion (the moderators were Drs Isabella Seeber and Lena Waizenegger), during which the initial position statements were elaborated on and additional issues were raised.

Through the discussion, a set of additional issues were identified. These issues related to (1) the design of autonomous technology-based agents in terms of human–machine workplace configurations, as well as transparency and explainability, and (2) the unintended consequences of using autonomous technology-based agents in terms of de-evolution of social interaction, prioritization of machine teammates, psychological health, and biased algorithms.

Key issues related to the affective and cognitive implications of using autonomous technology-based agents, design issues, and unintended consequences highlight key contemporary research challenges that allow researchers in this area to leverage compelling questions that can guide further research in this field.

IEEE Ontologies for Robotics and Automation Working Group were divided into subgroups that were in charge of studying industrial robotics, service robotics and autonomous robotics. This paper aims to present the work in-progress developed by the autonomous robotics (AuR) subgroup. This group aims to extend the core ontology for robotics and automation to represent more specific concepts and axioms that are commonly used in autonomous robots.

For autonomous robots, various concepts for aerial robots, underwater robots and ground robots are described. Components of an autonomous system are defined, such as robotic platforms, actuators, sensors, control, state estimation, path planning, perception and decision-making.

AuR has identified the core concepts and domains needed to create an ontology for autonomous robots.

AuR targets to create a standard ontology to represent the knowledge and reasoning needed to create autonomous systems that comprise robots that can operate in the air, ground and underwater environments. The concepts in the developed ontology will endow a robot with autonomy, that is, endow robots with the ability to perform desired tasks in unstructured environments without continuous explicit human guidance.

Creating a standard for knowledge representation and reasoning in autonomous robotics will have a significant impact on all R&A domains, such as on the knowledge transmission among agents, including autonomous robots and humans. This tends to facilitate the communication among them and also provide reasoning capabilities involving the knowledge of all elements using the ontology. This will result in improved autonomy of autonomous systems. The autonomy will have considerable impact on how robots interact with humans. As a result, the use of robots will further benefit our society. Many tedious tasks that currently can only be performed by humans will be performed by robots, which will further improve the quality of life. To the best of the authors’knowledge, AuR is the first group that adopts a systematic approach to develop ontologies consisting of specific concepts and axioms that are commonly used in autonomous robots.

The purpose of this paper is to propose a layered adjustable autonomy (LAA) as a dynamically adjustable autonomy model for a multi-agent system. It is mainly used to efficiently manage humans’ and agents’ shared control of autonomous systems and maintain humans’ global control over the agents.

The authors apply the LAA model in an agent-based autonomous unmanned aerial vehicle (UAV) system. The UAV system implementation consists of two parts: software and hardware. The software part represents the controller and the cognitive, and the hardware represents the computing machinery and the actuator of the UAV system. The UAV system performs three experimental scenarios of dance, surveillance and search missions. The selected scenarios demonstrate different behaviors in order to create a suitable test plan and ensure significant results.

The results of the UAV system tests prove that segregating the autonomy of a system as multi-dimensional and adjustable layers enables humans and/or agents to perform actions at convenient autonomy levels. Hence, reducing the adjustable autonomy drawbacks of constraining the autonomy of the agents, increasing humans’ workload and exposing the system to disturbances.

The application of the LAA model in a UAV manifests the significance of implementing dynamic adjustable autonomy. Assessing the autonomy within three phases of agents run cycle (task-selection, actions-selection and actions-execution) is an original idea that aims to direct agents’ autonomy toward performance competency. The agents’ abilities are well exploited when an incompetent agent switches with a more competent one.

Previous sales research remains limited to analyzing the influence of sales activities with sales agent tenure. To date, research on this subject has focused on the downstream direct or indirect impact of sales activities to sales performance, failing to consider whether sales activities impact a sales agent’s tenure. This paper aims to assess the effect of sales activities on sales performance and sales agent engagement on sales agent tenure through the lens of autonomous motivation to better understand sales activities as an overall sales process antecedent

Through the utilization of secondary sales operational data, this research demonstrates the influence of sales activities on multiple sales agent outcomes, while depicting the importance of sales managers creating an autonomous motivational climate.

This research demonstrates the direct relationship between sales activities to job engagement and sales performance. However, sales activities have a negative relationship to sales agent tenure, which require a sales manager to create an autonomous motivation to mediate the relationship between sales activities and sales agent tenure.

Organizations are provided with sample methodology and analysis to better determine how a culture grounded in autonomous motivation mediates sales activities and can be a catalyst for improving sales agent tenure. Then, provide a better understanding of the effect of actual sales activities on important sales department work outcomes.

The model is the first to test holistically the influence of sales activities on sales performance, sales agent engagement and tenure jointly by using actual secondary operational data. This study provides a glimpse of the real world balance a sales manager must consider between climate and activities. Plus, this study takes initial steps to study sales agent engagement, an under-researched construct in sales research.

Because of indeterminateness of the environment and delay of the communication, deep space spacecraft is required to be autonomous. Planning technology is studied in order to realize the spacecraft autonomy. First, a multi‐agent planning system (MAPS) based on temporal constraint satisfaction is proposed for concurrency and distribution of spacecraft system. Second, timeline concept is used to describe simultaneous activity, continue time, resource and temporal constraints. Third, for every planning agent in the MAPS, its layered architecture is designed and planning algorithm based on the temporal constraint satisfaction is given in detail. Finally, taking some key subsystems of deep space explorer as an example, the prototype system of MAPS is implemented. The results show that with the communication and cooperation of the planning agents, the MAPS is able to produce complete plan for explorer mission quickly under the complex constraints of time and resource.

Academics and practitioners have long acknowledged the potential of multiagent systems (MAS) to automate and autonomize decision-making in logistics and supply chain networks. Despite the manifold promises of MAS, industry adoption is lagging behind, and the exact benefits of these systems remain unclear. This study aims to fill this knowledge gap by analyzing 11 specific MAS use cases, highlighting their benefits, clarifying how they can help enhance logistics network resilience and identifying existing barriers.

A three-stage Delphi study was conducted with 18 industry experts. In the first round, these experts identified 11 use cases of MAS and their potential benefits, as well as any barriers that could hinder their adoption. In the second round, they assessed the identified use cases with regard to their potential to enhance logistics network resilience and improve organizational productivity. Furthermore, they estimated the complexity of MAS implementation. In the third round, the experts reassessed their evaluations in light of the evaluations of the other study participants.

This study proposes 11 specific MAS use cases and illustrates their potential for increasing logistics network resilience and enhancing organizational performance due to autonomous decision-making in informational processes. Furthermore, this study discusses important barriers for MAS, such as lack of standardization, insufficient technological maturity, soaring costs, complex change management and a lack of existing use cases. From a theoretical perspective, it is shown how MAS can contribute to resilience research in supply chain management.

The identification and assessment of diverse MAS use cases informs managers about the potential of this technology and the barriers that need to be overcome.

This study fills a gap in the literature by providing a thorough and up-to-date assessment of the potential of MAS for logistics and supply chain management. To the best of the authors’ knowledge, this is the first study to investigate the relevance of MAS for logistics network resilience using the Delphi method.

The purpose of this paper is to extend the existing approaches of coalition formation to how to adapt dynamically the size of the coalition according to the complexity of the task to be accomplished.

A considerable amount of attention has been paid to the coalition formation problem to deal efficiently with tasks needing more than one agent (i.e. robot). However, little attention has been paid to the problem of monitoring a coalition during the execution by modifying it according to the progress of the accomplishment of the task. In this paper, the authors consider a coalition of resource‐bounded autonomous agents with anytime behavior solving a common complex task. There is no central control component. Agents can observe the effect of the other agents' actions. They can decide whether they should continue to contribute in solving the common task or to stop their contribution and to leave the coalition. This decision is made in a distributed way. The objective is to avoid the waste of resources and time by using the same coalition along the task accomplishment while some agents become unnecessary to pursue the accomplishment of the task. The authors formalize this decentralized decision‐making problem as a decentralized Markov decision process (DEC‐MDP).

The paper results in a framework leading to Coal‐DEC‐MDP, which allows each agent to decide whether to stay in the coalition or leave it by estimating the progress on the task accomplishment.

The approach could be extended to deal with more than one coalition.

Decentralized control of a fleet of robots accomplishing a mission.

The paper deals with a new problem of adapting dynamically the coalition to the target task and the use of DEC‐MDPs.

The purpose of this paper is to develop a collaborative agent-based web architecture and an agent-based green IS assessment tool to aid information technology (IT) practitioners in data centers assess their current green information systems (IS) practice toward attaining sustainability.

The methodology comprises that the collaborative agent-based web architecture, agents’ algorithm and the green IS assessment tool, which is validated by employing focus group questionnaire targeting IT practitioners in seven Malaysian-based enterprises that have an in-house data centers. With 105 valid samples at hand, descriptive analysis and exploratory factor analysis was utilized to determine the applicability of the implemented agent-based green IS assessment tool.

Findings reveal that the agent-based green IS assessment tool possesses the capability to evaluate benchmark and rate enterprise data centers current green IS practice. Additional findings indicate that the agent-based green IS assessment tool provide suggestions on how green IS practice can be improved in enterprise data centers.

This study only collected data from 105 IT practitioners in enterprise data centers based in Malaysia; as such results from this research cannot be generalized to other countries. Moreover, the developed collaborative agents for green IS practice assessment can only be fully deployed after domain experts has added green IS practice assessment questions and alternative answers.

This study presents an autonomous agent-based green IS assessment tool that supports the assessment of enterprise toward inclusion of sustainability considerations to enhance enterprise environmental performance.

This study provides empirical evidence for data centers efficacy leading toward a greener society for environmental conservation for future generations to come.

This study creates awareness by presenting the green IS practice to be implemented by IT practitioners in data centers. In addition, the agent-based green IS assessment tool provides a web-based platform for promoting environmental sustainability by supporting data centers toward evaluating, benchmarking and rating their current green IS practices.

Automatic, autonomous browsing has an increasingly important task in information discovery and assisted browsing on the Internet. Where users could once keep up to date with information of interest on the Internet, the recursive growth of the network has made this process increasingly time consuming and less rewarding. Presents two possible solutions to this problem: Data Agents and CollaborAgents which were developed with IBM’s Aglet Workbench – a particular implementation of mobile agents. Also surveys the agent technology and discusses the agent building package used to develop both mentioned applications. Concludes that the future of local interaction, reduced network loading, server flexibility and application autonomy which are supported by mobile agent technology, all help to provide a level agility above distributed problem solving.

This two-part paper is concerned with the creation of a generalised cybernetic agency-based ecosystem. The purpose of the first part is to explore the basis for the creation of an agentic ecology theory to provide a generalised multidisciplinary context-free manifold that can be applied to specific domains and contexts. As an element of this, it will explore the relationship between agency and its agents (at various foci) and the nature of agency ecologies and their evolution. It will also explore the relationship between viability and sustainability. In the second part of the paper, the purpose will be to formulate a general basis for agency ecology, followed by an agency model that recognises the analytical and decision-making attributes of the viability–sustainability relationship by centering on the modelling a socioeconomic ecosystem and a social disciplinary species model.

Agency theory will be used to model a generic agency ecology and its environment of subordinate elements – especially those subordinates that can be used as amenities to satisfy the needs to agency development. Part 1 of the paper will take a tour of concepts relevant to the representation of neo-ecosystem structures and their application. Part 2 will centre on delivering a schema capable of embracing agency neo-ecology from which applications may derive.

It is shown that agency theory as a modelling schema can be used as a methodology through which to provide diagnosis to examine the condition of, or for locating problems within, an agency in its ecosystem environment. This is illustrated within a socioeconomic context.

The paper is conceptual in nature, and there has been no intention to diagnose any substantive issues within the socioeconomic context.

A generalised agency ecology approach is proposed over this two-part paper that is novel through the use of third-order cybernetics.