The reference ontology of collective behavior of autonomous agents and its extensions

Gorodetsky, Vladimir; Samoylov, Vladimir; Trotskii, D. V.

doi:10.1134/s1064230715030089

Cited by 9 publications

(13 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Perhaps, one of the ways to overcome this problem is to develop basic ontology for domain-independent concepts used in MAS. One of the examples of ontology for domain-independent behavioral concepts of the BDI-model can be found in [Gorodetsky et al, 2015].…”

Section: State-of-the-artmentioning

confidence: 99%

System engineering view on multi-agent technology for industrial applications: barriers and prospects

Gorodetsky

Skobelev

2020

CAP

View full text Add to dashboard Cite

For over a quarter of a century, multi-agent systems have been considered as one of the most promising technologies for conceptualization, software development and implementation of Artificial Intelligence (AI) solutions. Not so widely but multi-agent technology is also considered as a way of designing complex adaptive systems based on bio-inspired principles of selforganization and evolution. However, in practice, the industry rarely uses multi-agent technology, despite the appearance of new classes of applications for which it is the perfect match, for example, smart cyber-physical systems with digital twins of controlled objects. The paper analyzes the recent anticipations and real achievements in the practical use of multi-agent systems at the industry level. It also identifies the engineering problems that currently impede the extensive industrial implementation of multi-agent systems and technologies as well as the ways to overcome them. Finally, prospects for development of these technologies are evaluated up to the level of industrial implementation.

show abstract

Section: State-of-the-artmentioning

confidence: 99%

System engineering view on multi-agent technology for industrial applications: barriers and prospects

Gorodetsky

Skobelev

2020

CAP

View full text Add to dashboard Cite

show abstract

“…Besides, the RobotML is quite specific to their application; thus, it lacks generality to be reused in the underwater robots field. A reference ontology of collective behavior of autonomous was developed by Gorodetsky et al [18]. This ontology provides model of the system and environment, model of interaction between the system objects and the environment, and model of the behavior of the system objects and the environment.…”

Section: Related Workmentioning

confidence: 99%

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots

Bilbao

Martín-Wanton

et al. 2017

Sensors

View full text Add to dashboard Cite

In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN) theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning.

show abstract

“…It should be noted that in the normative decision-making theory it is considered that a decision-making process is an uncontrolled factor. Therefore, the development of the theory of multiagent systems is now aimed at solving a set of problems that concentrate around a subjective choice phenomenon [5,11,13]. The formal choice theory [12] has been developing through abstracting from subjective factors, which led to the creation of a normative decision-making theory by an "ideal" subject.…”

Section: Follow-up On the Issuementioning

confidence: 99%

“…Typically, all possible options of such systems behavior are designed by a human and included in them at the design stage. When such system gets into conditions that were not taken into account by its developers, it can lead to emergency termination or severe consequences [5]. One of the approaches to solving this problem is agentoriented programming.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, researchers are focused on solving the problem of modeling subjectively rational decision making by a natural or artificial entity that has the ability to form goals endogenously based on their interests. The papers [1][2][3][4][5] give a definition of intellectual and purposeful behavior. The papers [4,11] justify that the prerequisites for such behavior are natural and artificial entities (hereinafter an agent) have processes that allow creating internal images of themselves, an influencing environment and determinants of their subjective understanding of choice situations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling Systems with Selectable Structures of Interests and Ideas of the Environment

Vinogradov¹

2018

Proceedings of the VTH International Workshop "Critical Infrastructures: Contingency Management, Intelligent, Agent-Based, Clou

View full text Add to dashboard Cite

The paper shows that regularities of abandoning an "ideal" rational choice by the subject to transfer to a subjectively rational one are connected with peculiarities of identifying and understanding external conditions and properties of interests. The subject makes choice based on perceptions of a choice situation. Evaluation of satisfaction with it leads to changes in subject's interest structure, and he can choose it. When making decisions, the subject uses three sets of alternatives: control, structural, and identification. The rules for choosing alternatives are formed by a compromise, the problem of choice modeling acquires game content.

show abstract

The reference ontology of collective behavior of autonomous agents and its extensions

Cited by 9 publications

References 11 publications

System engineering view on multi-agent technology for industrial applications: barriers and prospects

System engineering view on multi-agent technology for industrial applications: barriers and prospects

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots

Modeling Systems with Selectable Structures of Interests and Ideas of the Environment

Contact Info

Product

Resources

About