scholarly journals METHODOLOGICAL FOUNDATIONS OF MODELING AND INTELLIGENT MANAGEMENT OF AN INDUSTRIAL COMPLEX AS A COMPLEX DYNAMIC MULTI-AGENT OBJECT

2020 ◽  
Vol 2 (№11 2020) ◽  
pp. 288-293
Author(s):  
B.G. Ilyasov ◽  
E.А. Makarova ◽  
E.Sh. Zakieva ◽  
E.R. Gabdullina
Keyword(s):  
Industrial Complex ◽  
Complex Dynamic ◽  
Multi Agent ◽  
Intelligent Management

scholarly journals Development of information support for the technological production process of drinks based on plant roughage

2020 ◽  
Vol 222 ◽  
pp. 01017
Author(s):  
Sergey Pachkin ◽  
Pavel Ivanov ◽  
Anatoliy Maytakov ◽  
Liliya Beryazeva ◽  
Roman Kotlyarov
Keyword(s):  
Production Process ◽  
Supervisory Control ◽  
Information Support ◽  
Industrial Complex ◽  
Complex Dynamic ◽  
Capital Costs ◽  
Complex Dynamic Systems ◽  
Development Framework ◽  
Technological Production ◽  
Automated Process Control System

Today, SCADA is the main and advanced method for automated management of complex dynamic systems (processes). Supervisory control of technological processes in various branches of the agro-industrial complex is an essential factor in improving their effectiveness and fulfilling the tasks of increasing productivity, competitiveness, and profitability of production. Due to the introduction of SCADA, the company manages to facilitate and improve the production process. The article deals with an example of supervisory control of the production of a drink based on plant roughages. In order to decrease capital costs and further maintenance costs during automation, the functions of the controller level are united with the operator level, and dispatching functions are combined with the level of the administrative automated process control system. SCADA TRACE MODE was selected as the development framework for the technological process information support of the drink. The structure of the SCADA project and the key screen forms allowing dispatching the technological production process of a drink based on plant roughage were designed.

scholarly journals Experimenting with robotic intra-logistics domains

2018 ◽  
Vol 18 (3-4) ◽  
pp. 502-519
Author(s):  
MARTIN GEBSER ◽  
PHILIPP OBERMEIER ◽  
THOMAS OTTO ◽  
TORSTEN SCHAUB ◽  
ORKUNT SABUNCU ◽  
...  
Keyword(s):  
Industrial Revolution ◽  
Experimental Studies ◽  
Answer Set Programming ◽  
Complex Dynamic ◽  
Fourth Industrial Revolution ◽  
Reasoning About Action ◽  
Multi Agent ◽  
Action Change ◽  
Alternative Solutions ◽  
Multi Agent Planning

AbstractWe introduce theasprilo1framework to facilitate experimental studies of approaches addressing complex dynamic applications. For this purpose, we have chosen the domain of robotic intra-logistics. This domain is not only highly relevant in the context of today's fourth industrial revolution but it moreover combines a multitude of challenging issues within a single uniform framework. This includes multi-agent planning, reasoning about action, change, resources, strategies, etc. In return,aspriloallows users to study alternative solutions as regards effectiveness and scalability. Althoughasprilorelies on Answer Set Programming and Python, it is readily usable by any system complying with its fact-oriented interface format. This makes it attractive for benchmarking and teaching well beyond logic programming. More precisely,aspriloconsists of a versatile benchmark generator, solution checker and visualizer as well as a bunch of reference encodings featuring various ASP techniques. Importantly, the visualizer's animation capabilities are indispensable for complex scenarios like intra-logistics in order to inspect valid as well as invalid solution candidates. Also, it allows for graphically editing benchmark layouts that can be used as a basis for generating benchmark suites.

scholarly journals Towards Flexible Teamwork

1997 ◽  
Vol 7 ◽  
pp. 83-124 ◽  
Author(s):  
M. Tambe
Keyword(s):  
Information Integration ◽  
Environmental Conditions ◽  
Building Block ◽  
Domain Specificity ◽  
Basic Building Block ◽  
Complex Dynamic ◽  
Team Members ◽  
Implementation Effort ◽  
Central Hypothesis ◽  
Multi Agent

Many AI researchers are today striving to build agent teams for complex, dynamic multi-agent domains, with intended applications in arenas such as education, training, entertainment, information integration, and collective robotics. Unfortunately, uncertainties in these complex, dynamic domains obstruct coherent teamwork. In particular, team members often encounter differing, incomplete, and possibly inconsistent views of their environment. Furthermore, team members can unexpectedly fail in fulfilling responsibilities or discover unexpected opportunities. Highly flexible coordination and communication is key in addressing such uncertainties. Simply fitting individual agents with precomputed coordination plans will not do, for their inflexibility can cause severe failures in teamwork, and their domain-specificity hinders reusability. Our central hypothesis is that the key to such flexibility and reusability is providing agents with general models of teamwork. Agents exploit such models to autonomously reason about coordination and communication, providing requisite flexibility. Furthermore, the models enable reuse across domains, both saving implementation effort and enforcing consistency. This article presents one general, implemented model of teamwork, called STEAM. The basic building block of teamwork in STEAM is joint intentions (Cohen & Levesque, 1991b); teamwork in STEAM is based on agents' building up a (partial) hierarchy of joint intentions (this hierarchy is seen to parallel Grosz & Kraus's partial SharedPlans, 1996). Furthermore, in STEAM, team members monitor the team's and individual members' performance, reorganizing the team as necessary. Finally, decision-theoretic communication selectivity in STEAM ensures reduction in communication overheads of teamwork, with appropriate sensitivity to the environmental conditions. This article describes STEAM's application in three different complex domains, and presents detailed empirical results.

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