Structure plays a key role in learning performance. In centralized computational systems, hyperparameter optimization and regularization techniques such as dropout are computational means to enhance learning performance by adjusting the deep hierarchical structure. However, in decentralized deep learning by the Internet of Things, the structure is an actual network of autonomous interconnected devices such as smart phones that interact via complex network protocols. Self-adaptation of the learning structure is a challenge. Uncertainties such as network latency, node and link failures or even bottlenecks by limited processing capacity and energy availability can significantly downgrade learning performance. Network self-organization and self-management is complex, while it requires additional computational and network resources that hinder the feasibility of decentralized deep learning. In contrast, this paper introduces a self-adaptive learning approach based on holarchic learning structures for exploring, mitigating and boosting learning performance in distributed environments with uncertainties. A large-scale performance analysis with 864,000 experiments fed with synthetic and real-world data from smart grid and smart city pilot projects confirm the cost-effectiveness of holarchic structures for decentralized deep learning.
General-purpose holonic designs that integrate the above principles are proposed via holonic multi-agent system platforms @cite_7 @cite_46 ; multi-level modeling and simulation approaches @cite_1 ; and hierarchical problem solving @cite_8 . Customised holonic designs are been applied to various domains, including hierarchical planning @cite_24 , traffic control @cite_28 , manufacturing @cite_11 @cite_43 and smart grids @cite_51 @cite_38 @cite_39 . The success of these applications motivate the adoption of similar holonic principles for designing deep learning systems for large-scale distributed environments.
@cite_38 @cite_7 @cite_8 @cite_28 @cite_1 @cite_39 @cite_24 @cite_43 @cite_46 @cite_51 @cite_11
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Many resource allocation problems are hard to solve even with state-of-the-art constraint optimisation software upon reaching a certain scale. Our approach to deal with this increasing complexity is to employ a hierarchical âregio-centralâ mechanism. It requires two techniques: (1) the synthesis of several models of agents providing a certain resource into a centrally and efficiently solvable optimisation problem and (2) the creation of an abstracted version of this centralised model that reduces its complexity when passing it on to higher layers. We present algorithms to create such synthesised and abstracted models in a fully automated way and demonstrate empirically that the obtained solutions are comparable to central solutions but scale better in an example taken from energy management. This paper is an overview of our research program in intelligent systems. Our object of study is constructed complex systems, which are software and hardware systems mediated or managed by computers. We describe how biological systems provide stiff competition for constructed complex systems in the areas of autonomy and intelligence, robustness, adaptability, and communication. We describe our computationally reflective integration infrastructure, called wrappings', and show how it can provide many of the necessary flexibilities. We also describe two directions of research in computational semiotics, which for us means the study of the use of symbols by computing systems. We describe our conceptual categories', which are a method of knowledge representation that supports these flexibilities, and some new results on symbol systems, which leads to some new mathematical questions about what can be represented in formal systems and how they can be extended automatically. These are then combined to describe our architecture, which we are currently in the process of implementing. With the ever growing usage of the world wide IT networks, agent technologies and multiagent systems (MAS) are attracting more and more attention. Multiagent systems are designed to be open systems, wheras agent technologies aim at the design of agents that perform well in environments that are not necessarily well-structured and benevolent. Emergent system behaviour is one of the most interesting phenomena one can investigate in MAS. However, there is more to MAS design than the interaction between a number of agents. For an effective system behaviour we need structure and organisation. This paper presents basic concepts of a theory for holonic multiagent systems with the aim to define the building blocks of a theory that can explain organisation and dynamic reorganisation in MAS. In doing so it tries to bridge the well-known micro-macro gap in MAS theories. The basic concepts are illustrated with three application scenarios: flexible manufacturing, order dispatching in haulage companies, and train coupling and sharing. Simulation is an appropriate approach for studying complex systems that are inacessible through direct observations and measurements. In a simulation involving a great number of interacting entities, it is difficult to create a reliable and tractable abstraction of the real reference system. One of the involved problems is amount of computational resources required to handle microscopic simulation of large number of entities. One solution is to use macroscopic models. However, this type of models may be at hand unavailable or not reliable, or it doesn’t allow observations of individual behaviours. In this paper a multilevel simulation model is proposed to dynamically adapt the level of simulated behaviours while being as faithful as possible to the reference model. Our approach is based on Holonic Multi-Agent Systems and provides a generic scheduling model for multilevel simulations. Designing the control infrastructure of future “smart” power grids is a challenging task. Future grids will integrate a wide variety of heterogeneous producers and consumers that are unpredictable and operate at various scales. Information and Communication Technology (ICT) solutions will have to control these in order to attain global objectives at the macrolevel, while also considering private interests at the microlevel. This article proposes a generic holonic architecture to help the development of ICT control systems that meet these requirements. We show how this architecture can integrate heterogeneous control designs, including state-of-the-art smart grid solutions. To illustrate the applicability and utility of this generic architecture, we exemplify its use via a concrete proof-of-concept implementation for a holonic controller, which integrates two types of control solutions and manages a multiscale, multiobjective grid simulator in several scenarios. We believe that the proposed contribution is essential for helping to understand, to reason about, and to develop the “smart” side of future power grids. Prior to this work, an algorithmic and rule-based blackboard system (ARBS) had been developed over a ten-year period. ARBS benefited from a versatile rule structure and the ability to mix computational styles either as separate knowledgesources or by embedding algorithms within rules. It was a serial system – any knowledge source that was able to contribute had to wait its turn. We report here on a new distributed system, DARBS, in which the knowledge sources are parallel processes. Based around the client server model, DARBS comprises a centralised database server, i.e. the blackboard, and a number of knowledge source clients. As the clients are separate processes, possibly on separate networked computers, they can contribute to the solution of a problem whenever they have a contribution to make. DARBS therefore achieves the well-established but elusive ideal of opportunism. It behaves as a distributed agent-based system, with the proviso that all communication is via the blackboard. DARBS is currently being applied to automatic interpretation of nondestructive evaluation (NDE) data and control of plasma deposition processes. Holonic Manufacturing Systems (HMS) was first proposed as a new manufacturing paradigm at the beginning of 1990 and has subsequently received a lot of attention in academic and industrial research. The application of holonic concepts to manufacturing was initially motivated by the inadequacy of existing manufacturing systems in the following two aspects: (i) dealing with the evolution of products within an existing production facility, and (ii) maintaining a satisfactory performance outside of normal operating conditions. In spite of the large number of developments reported in Holonic Control Architectures and Holonic Control Algorithms, there is very little work reported on Methodologies for HMS. Manufacturing requirements impose significant properties on HMS making the modelling process of HMS a complex and difficult task. In this work we describe how to engineer Holonic Manufacturing Systems using a Multi-agent System method. Our approach is based on holonic systems specific requirements, and it incorporates software engineering principles in order to assist the system designers at each development stage. It implements a collaborative-engineering process for HMS, and provides clear and unambiguous analysis and design guidelines. Complex software systems development require appropriate high-level features to better and easily tackle the new requirements in terms of interactions, concurrency and distribution. This requires a paradigm change in software engineering and corresponding programming languages. We are convinced that agent-oriented programming may be the support for this change by focusing on a small corpus of commonly accepted concepts and the corresponding programming language in line with the current developers' programming practices. This papers introduces SARL, a new general-purpose agent-oriented programming language undertaking this challenge. SARL comes with its full support in the Eclipse IDE for compilation and debugging, and a new version 2.0 of the Janus platform for execution purposes. The main perspective that guided the creation of SARL is the establishment of an open and easily extensible language. Our expectation is to provide the community with a common forum in terms of a first working test bed to study and compare various programming alternatives and associated metamodels. The paper investigates the self-stabilization of hierarchically structured markets. We propose a new approach that is motivated by the physical structure of the energy grid and generalizes classical market structures in a natural way. Hierarchical markets have several advantages compared to monolithic markets, i.e., improved reliability and scalability, locality of information, and proximity of energy production and consumption. By simulating scenarios based on real world consumption and production data including households, different renewable energy sources, and other plant types, we present a proof-of-concept of stability of the hierarchical markets in various simulations. Properly architected holonic systems can enhance the ability of each set of players in the industrial automation and control market to deliver added value by encapsulating, reusing and deploying their specialized intellectual property at succeedingly higher levels of integration. Such an architecture expands on the HMS concept of cooperation domains to include both low-level control (LLC) and high-level control (HLC) domains. LLC refers to normal, non-holonic control and automation functions, while HLC refers to the integration of these functions into holons through the use of software agent technology. Function blocks, as defined in the International Electrotechnical Commission (IEC) 61499 series of standards, can be used for encapsulation, reuse, distribution and integration of both LLC and HLC functions, while HLC functionality can be standardized as defined by the Foundation for Intelligent Physical Agents (FIPA).