Software experience when using ontologies in a multi‐agent system for automated planning and scheduling

González, Evelio J.; Hamilton, A.; Moreno, L.; Marichal, R. L.; Muñoz, V.

doi:10.1002/spe.711

Cited by 14 publications

(9 citation statements)

References 9 publications

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“…In ABMS, knowledge is usually simplified and modelled as set of properties and values or as a state-machine [122,123]. More complex knowledge representations have been proposed, mainly from a MAS perspective, for example, using the fuzzy cognitive model in Reference [124] or ontologies in References [125][126][127]. The approach for representing agent's knowledge is tied to the decision-making process.…”

Section: Multi-agent Modelsmentioning

confidence: 99%

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

et al. 2020

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Systems engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address systems engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in the industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant.Processes 2020, 8, 312 2 of 29 in a telecommunication system network, nodes (e.g., routers and switches) may suffer over time a degradation in their properties that may lead them to diminish their performance. However, just the daily variation on traffic demand also has an impact on such nodes performance. Unexpected, external intervention such as cyber-attacks or extreme weather conditions may affect the properties, even the topology of a telecommunication system.Intelligent distributed systems are capable of modelling how different parts of the network might work individually and collectively [10]. Ultimately, this is used as support for any decision-making system aiming to achieve better overall system functioning and its consequent performance. Intelligent distributed systems encompass autonomous learning units that can be associated with the nodes of a complex network. These intelligent network nodes are able to act independently and also interact with other nodes to pursue both individual (local) and general system-level targets. The necessary communication between individuals can be represented by links of the complex network connecting their nodes. It is natural to understand such nodes as intelligent agents within a MAS framework. Each agent separately obeying simple rules but working together with other agents makes it possible to approach complex engineering challenges. Intelligent distributed systems have been proposed in a broad range of engineered complex systems [11,12]. Precisely, the review proposed herein focuses only on engineered systems and manufacturing processes, given the wide spectrum of the approached topic. Out of this review fall, then, topics related to social systems analysis such as migration models [13,14] or online social media [15,16]. Similarly, natural systems are out of this review scope. This is the case of computational chemistry [17,18], biology [19,20], and metabolic networks [21,22].Automatic optimisation and control in an engineering system is associated with a near real-time data acquisition and an optimal decision-making. Th...

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Section: Multi-agent Modelsmentioning

confidence: 99%

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

et al. 2020

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“…The basic knowledge structure is constituted by an ontology which is a key feature for achieving flexibility in agent-based control systems [15]. In contrast to multi-agent systems with centralized ontologies [16], each automation agent relies on its own ontology which is derived from a general system ontology. The vocabulary defined by the ontology is then used to express knowledge about the current state of the world in a set of facts.…”

Section: High Level Controlmentioning

confidence: 99%

A Multi-Layer Approach for Failure Detection in a Manufacturing System Based on Automation Agents

Lepuschitz

Jirkovský

Kadera

et al. 2012

2012 Ninth International Conference on Information Technology - New Generations

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Automatically detecting component failures and unexpected behaviors is an essential service for achieving fault-tolerant robust manufacturing systems. The application of a multiagent system is regarded as a promising approach for designing complex systems such as manufacturing systems due to its distributed nature. In this paper we present an agent-based control system with diagnostic capabilities on several layers for a pallet transport system. Local diagnostic tasks for detecting failures are performed by the automation agents each controlling one physical component such as a diverter. In order to observe the correct behavior of the components on a system-wide scale, an approach based on Hidden Markov Models linked to the agents is employed.

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“…The authors have decided to design a coarse-grained ontology [17], due to the relatively simpleness of its use in MASCONTROL. Defined classes are mainly related to control concepts: System, Input, Output, ReferenceInput, Error, ControlAction.…”

Section: Ontology Design Summarymentioning

confidence: 99%

A Mas Implementation for System Identification and Process Control

González

Hamilton

Moreno

et al. 2006

Asian Journal of Control

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In this paper, a MAS for system identification and process control is presented. In particular, this MAS implements a self-tuning regulator (STR) scheme. It has adopted FIPA specifications because they have become a stronger standard in MAS development and they involve not only agent language specifications but also agent management and conversations. In this work, an Ontology Agent (OA) is included, using DAML+OIL as ontology language. The obtained results validate this approach in the implementation of well-known algorithms for control process.

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Software experience when using ontologies in a multi‐agent system for automated planning and scheduling

Cited by 14 publications

References 9 publications

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

A Multi-Layer Approach for Failure Detection in a Manufacturing System Based on Automation Agents

A Mas Implementation for System Identification and Process Control

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