A Distributed Artificial Intelligence

Singh, Pushpa; Singh, Rajnesh; Singh, Narendra; Singh, Murari Kumar

doi:10.1201/9781003038467-16

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…With this in mind, a CPS can be considered an advanced DAI system, where the data, intelligence, and control are distributed, eliminating bottlenecks. Additionally, the horizontal layers of components envisioned by CPS can overcome some of the limitations of traditional distributed systems, where the knowledge about the overall system is dispersed throughout the entities, which individually have only a partial and imprecise perspective [22]. This layer creates a hierarchical coordination/control structure [22], where upper components integrate the data and information for several lower components, with a global view of the system that can support the lower layers, mainly in the long-term tasks, since the short-term tasks usually do not require a complete view of the system.…”

Section: Multi-agent Systemsmentioning

confidence: 99%

Collective Intelligence in Self-Organized Industrial Cyber-Physical Systems

2022

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Cyber-physical systems (CPS) play an important role in the implementation of new Industry 4.0 solutions, acting as the backbone infrastructure to host distributed intelligence capabilities and promote the collective intelligence that emerges from the interactions among individuals. This collective intelligence concept provides an alternative way to design complex systems with several benefits, such as modularity, flexibility, robustness, and reconfigurability to condition changes, but it also presents several challenges to be managed (e.g., non-linearity, self-organization, and myopia). With this in mind, this paper discusses the factors that characterize collective intelligence, particularly that associated with industrial CPS, analyzing the enabling concepts, technologies, and application sectors, and providing an illustrative example of its application in an automotive assembly line. The main contribution of the paper focuses on a comprehensive review and analysis of the main aspects, challenges, and research opportunities to be considered for implementing collective intelligence in industrial CPS. The identified challenges are clustered according to five different categories, namely decentralization, emergency, intelligent machines and products, infrastructures and methods, and human integration and ethics. Although the research indicates some potential benefits of using collective intelligence to achieve the desired levels of autonomy and dynamic adaptation of industrial CPS, such approaches are still in the early stages, with perspectives to increase in the coming years. Based on that, they need to be further developed considering some main aspects, for example, related to balancing the distribution of intelligence by the vertical and horizontal dimensions and controlling the nervousness in self-organized systems.

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Section: Multi-agent Systemsmentioning

confidence: 99%

Collective Intelligence in Self-Organized Industrial Cyber-Physical Systems

2022

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“…We summarize the most related works here. Jennings (1996) introduce a task negotiation approach for autonomous robots in warehousing systems. However, when the environment becomes stochastic, the approach cannot be applied.…”

Section: Rl-based Automated Guided Vehicle (Agv)-related Workmentioning

confidence: 99%

A reinforcement learning approach for transaction scheduling in a shuttle‐based storage and retrieval system

Ekren

Arslan

2022

Int Trans Operational Res

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With recent Industry 4.0 developments, companies tend to automate their industries. Warehousing companies also take part in this trend. A shuttle‐based storage and retrieval system (SBS/RS) is an automated storage and retrieval system technology experiencing recent drastic market growth. This technology is mostly utilized in large distribution centers processing mini‐loads. With the recent increase in e‐commerce practices, fast delivery requirements with low volume orders have increased. SBS/RS provides ultrahigh‐speed load handling due to having an excess amount of shuttles in the system. However, not only the physical design of an automated warehousing technology but also the design of operational system policies would help with fast handling targets. In this work, in an effort to increase the performance of an SBS/RS, we apply a machine learning (ML) (i.e., Q‐learning) approach on a newly proposed tier‐to‐tier SBS/RS design, redesigned from a traditional tier‐captive SBS/RS. The novelty of this paper is twofold: First, we propose a novel SBS/RS design where shuttles can travel between tiers in the system; second, due to the complexity of operation of shuttles in that newly proposed design, we implement an ML‐based algorithm for transaction selection in that system. The ML‐based solution is compared with traditional scheduling approaches: first‐in‐first‐out and shortest process time (i.e., travel) scheduling rules. The results indicate that in most cases, the Q‐learning approach performs better than the two static scheduling approaches.

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“…Artificial Neural Network (ANN) is a computational model and an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information [19]. Amato et al [20] opine that ANN "is a mathematical representation of the human neural architecture, reflecting its "learning", "generalization" and "abstraction" abilities".…”

Section: Conceptual Framework Of Artificial Neural Networkmentioning

confidence: 99%

Untitled

2020

IJCSES

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Artificial Intelligence systems (especially computer-aided diagnosis and artificial neural networks) are increasingly finding many uses in medical diagnosis application in recent times. These methods are adaptive learning algorithms that are capable of handling multiple and heterogeneous types of clinical data with a view of integrating them into categorized outputs. In this study, we briefly review and discuss the concept, capabilities, and applicability of artificial neural network techniques to medical diagnosis, through consideration of some selected physical and mental diseases. The study focuses on scholarly researches within the years, 2010 to 2019. Findings show that no electronic online clinical database exists in Nigeria and the Sub-Saharan countries, most review researches in this area focused mainly on physical diseases without considering mental illnesses, the application of ANN in mental and comorbid disorders have not been thoroughly studied, ANN models and algorithms consider mainly homogeneous input data sources and not heterogeneous input data sources, and ANN models on multi-objective output systems are few as compared to single output ANN models.

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A Distributed Artificial Intelligence

Cited by 5 publications

References 9 publications

Collective Intelligence in Self-Organized Industrial Cyber-Physical Systems

Collective Intelligence in Self-Organized Industrial Cyber-Physical Systems

A reinforcement learning approach for transaction scheduling in a shuttle‐based storage and retrieval system

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