RETRACTED ARTICLE: A multi-agent architectural solution for coherent distributed reconfigurations of function blocks

The paper deals with distributed Multi-Agent Reconfigurable Embedded Control Systems following the International Industrial Standard IEC61499 in which a Function Block (abbreviated by FB) is an event-triggered software component owning data and a control application is a network of distributed blocks that should satisfy functional and temporal properties according to user requirements. We define an architecture of reconfigurable multi-agent systems in which a Reconfiguration Agent is affected to each device of the execution environment to apply local reconfigurations, and a Coordination Agent is proposed for coordinations between devices in order to guarantee safe and adequate distributed reconfigurations. A Communication Protocol is proposed to handle coordinations between agents by using well-defined Coordination Matrices. We specify both reconfiguration agents to be modelled by nested state machines, and the Coordination Agent according to the formalism Net Condition-Event Systems (Abbreviated by NCES) which is an extension of Petri nets. To validate the whole architecture, we check by applying the model checker SESA in each device functional and temporal properties to be described according to the temporal logic “Computation Tree Logic”. We have also to check all possible coordinations between devices by verifying that whenever a reconfiguration is applied in a device, the Coordination Agent and other concerned devices react as described in user requirements. We present a tool applying simulations of this distributed architecture in order to check interactions and reactivities of agents. The paper’s contributions are applied to two Benchmark Production Systems available in our research laboratory.     

Designing function blocks for distributed process planning and adaptive control

The objective of this research is to develop methodologies and a framework for distributed process planning and adaptive control using function blocks. Facilitated by a real-time monitoring system, the proposed methodologies can be applied to integrate with functions of dynamic scheduling in a distributed environment. A function block-enabled process planning approach is proposed to handle dynamic changes during process plan generation and execution. This paper focuses mainly on distributed process planning, particularly on the development of a function block designer that can encapsulate generic process plans into function blocks for runtime execution. As function blocks can sense environmental changes on a shop floor, it is expected that a so-generated process plan can adapt itself to the shop floor environment with dynamically optimized solutions for plan execution and process monitoring.     

Distributed MASON: A scalable distributed multi-agent simulation environment

Computational Social Science (CSS) involves interdisciplinary fields and exploits computational methods, such as social network analysis as well as computer simulation with the goal of better understanding social phenomena.Agent-Based Models (ABMs) represent an effective research tool for CSS and consist of a class of models, which, aim to emulate or predict complex phenomena through a set of simple rules (i.e., independent actions, interactions and adaptation), performed by multiple agents. The efficiency and scalability of ABMs systems are typically obtained distributing the overall computation on several machines, which interact with each other in order to simulate a specific model. Unfortunately, the design of a distributed simulation model is particularly challenging, especially for domain experts who sporadically are computer scientists and are not used to developing parallel code.D-MASON framework is a distributed version of the MASON library for designing and executing ABMs in a distributed environment ensuring scalability and easiness. D-MASON enable the developer to exploit the computing power of distributed environment in a transparent manner; the developer has to do simple incremental modifications to existing MASON models, without re-designing them.This paper presents several novel features and architectural improvements introduced in the D-MASON framework: an improved space partitioning strategy, a distributed 3D field, a distributed network field, a decentralized communication layer, a novel memory consistency mechanism and the integration to cloud environments.Full documentation, additional tutorials, and other material can be found at https://github.com/isislab-unisa/dmason where the framework can be downloaded.     

A multi-agent architecture for supporting distributed normality-based intelligent surveillance

Intelligent surveillance involves the use of AI techniques to monitor environments whose analysis is becoming more and more complex because of the large number of sensors used and the need of monitoring multiple events of interest simultaneously. Most of the current surveillance systems provide solutions for particular problems but still suffer from lack of flexibility and scalability when they are used on different or related surveillance problems. To overcome this limitation, two aspects should be addressed: a knowledge-based surveillance model flexible enough to deal with different events of interest and an architecture that gives support to this model when deploying the surveillance system within a particular scenario. This paper discusses the architecture devised to deploy intelligent surveillance systems by means of a set of autonomous agents that are responsible for the management of different surveillance tasks and for cooperating to monitor complex environments. This multi-agent architecture is inspired by a normality-based formal model used to define the knowledge needed to analyze general-purpose surveillance concepts. We use the architecture to deploy a surveillance system to monitor an urban traffic scenario.     

A multi-agent system for distributed multi-project scheduling: An auction-based negotiation approach

Simultaneously running multiple projects are quite common in industries. These projects require local (always available to the concerned project) and global (shared among the projects) resources that are available in limited quantity. The limited availability of the global resources coupled with compelling schedule requirements at different projects leads to resource conflicts among projects. Effectively resolving these resource conflicts is a challenging task for practicing managers. This paper proposes a novel distributed multi-agent system using auctions based negotiation (DMAS/ABN) approach for resolving the resource conflicts and allocating multiple different types of shared resources amongst multiple competing projects. The existing multi-agent system (MAS) using auction makes use of exact methods (e.g. dynamic programming relaxation) for solving winner determination problem to resolve resource conflicts and allocation of single unit of only one type of shared resource. Consequently these methods fail to converge for some multi-project instances and unsuitable for real life large problems. In this paper the multi-unit combinatorial auction is proposed and winner determination problem is solved by efficient new heuristic.The proposed approach can solve complex large-sized multi-project instances without any limiting assumptions regarding the number of activities, shared resources or the number of projects. Additionally our approach further allows to random project release-time of projects which arrives dynamically over the planning horizon. The DMAS/ABN is tested on standard set of 140 problem instances. The results obtained are benchmarked against the three state-of-the-art decentralized algorithms and two existing centralized methods. For 82 of 140 instances DMAS/ABN found new best solutions with respect to average project delay (APD) and produced schedules on an average 16.79% (with maximum 57.09%) lower APD than all the five methods for solving the same class of problems.     

A freehand-sketch environment for architectural design supported by a multi-agent system

This paper presents an on-line system for capturing and interpreting architectural sketches. The prototype is based on a multi-agent system, which enables real-time management of recognition scenarios. We describe the different types of agents, their characteristics, the basic mechanisms involved in interpreting freehand architectural drawings and the collaboration modes between agents. Finally, we illustrate the general operations of the system by a short example.     

A multi-agent system for the integration of distributed environmental information

This paper describes a multi-agent platform to be used for the integration of environmental information that may be distributed over a network. The system is designed to work as a collection of collaborating agents. Information sources are encapsulated as data source agents (DSAs) that accept messages in an agent communication language. Here we describe how queries can be entered into the system and information collected from multiple sources. A key component of the query module is the planner agent, which takes a query and transforms it first to a calculus, then to an algebraic expression in order to break it into subqueries which an executor agent can send to the DSAs. As part of this process, the query is translated from user-level ontologies to lower level ontologies relevant to the DSAs. Query results need not be returned within an ACL message, but may instead be represented by a Common Object Request Broker Architecture (CORBA) object reference which may be used to obtain the result set. The architecture and operation of these agent components is described and an example is presented of how environmental information can be queried.     

A management scheme for distributed cross-layer reconfigurations in the context of cognitive B3G infrastructures

Current research efforts in wireless communications are targeted at the evolution of B3G (Beyond the 3rd Generation) wireless infrastructures. The operation of B3G infrastructures envisions dynamic adaptations to external stimuli, which can be facilitated through the exploitation of cognitive networking potentials. Cognitive networks dispose mechanisms for dynamically selecting their configuration (algorithms and parameter values, at different layers of the protocol stack), through appropriate management functionality that takes into account the context of operation (environment characteristics and requirements), profiles, goals, policies and knowledge that derives from previous experience. This paper focuses on such management functionality and it addresses a problem, dealing with “Distributed, Cross-Layer Reconfigurations” (DCLR), which aims at assessing and selecting the most appropriate configuration per network element in a cognitive network. In essence, this work contributes in four main areas. First, a fully distributed formulation and solution to the DCLR problem is provided, which is important for the management of a particular reconfigurable element in a cognitive context. Second, robust learning and adaptation, strategies are proposed, for estimating and gaining knowledge of the performance potentials of alternate reconfigurations. Third, a computationally efficient solution to the problem of exploiting the performance potentials of reconfigurations is provided, in order to rate reconfigurations and finally select the best ones. Finally, results that expose the behaviour and efficiency of the proposed schemes, are presented.     

A distributed simplex algorithm for degenerate linear programs and multi-agent assignments

In this paper we propose a novel distributed algorithm to solve degenerate linear programs on asynchronous peer-to-peer networks with distributed information structures. We propose a distributed version of the well-known simplex algorithm for general degenerate linear programs. A network of agents, running our algorithm, will agree on a common optimal solution, even if the optimal solution is not unique, or will determine infeasibility or unboundedness of the problem. We establish how the multi-agent assignment problem can be efficiently solved by means of our distributed simplex algorithm. We provide simulations supporting the conjecture that the completion time scales linearly with the diameter of the communication graph.     

A distributed multi-agent production planning and scheduling framework for mobile robots

Inspired by the new achievements in mobile robotics having as a result mobile robots able to execute different production tasks, we consider a factory producing a set of distinct products via or with the additional help of mobile robots. This particularly flexible layout requires the definition and the solution of a complex planning and scheduling problem. In order to minimize production costs, dynamic determination of the number of robots for each production task and the individual robot allocation are needed. We propose a solution in terms of a two-level decentralized Multi-Agent System (MAS) framework: at the first, production planning level, agents are tasks which compete for robots (resources at this level); at the second, scheduling level, agents are robots which reallocate themselves among different tasks to satisfy the requests coming from the first level. An iterative auction based negotiation protocol is used at the first level while the second level solves a Multi-Robot Task Allocation (MRTA) problem through a distributed version of the Hungarian Method. A comparison of the results with a centralized approach is presented.     

Retraction Note: Edge-Oriented Spatial Interpolation for Error Concealment of Consecutive Blocks

Journal of Computer Science and Technology - Retraction: Wei Fu and Guang-Zhong Xing. Edge-Oriented Spatial Interpolation for Error Concealment of Consecutive Blocks. Journal of Computer Science...     

A multi-agent and cloud-edge orchestration framework of digital twin for distributed production control

The demands for mass individualization and networked collaborative manufacturing are increasing, bringing significant challenges to effectively organizing idle distributed manufacturing resources. To improve production efficiency and applicability in the distributed manufacturing environment, this paper proposes a multi-agent and cloud-edge orchestration framework for production control. A multi-agent system is established both at the cloud and the edge to achieve the operation mechanism of cloud-edge orchestration. By leveraging Digital Twin (DT) technology and Industrial Internet of Things (IIoT), real-time status data of the distributed manufacturing resources are collected and processed to perform the decision-making and manufacturing execution by the corresponding agent with permission. Based on the generated data of distributed shop floors and factories, the cloud production line model is established to support the optimal configuration of the distributed idle manufacturing resources by applying a systematic evaluation method and digital twin technology, which reflects the actual manufacturing scenario of the whole production process. In addition, a rescheduling decision prediction model for distributed control adjustment on the cloud is developed, which is driven by Convolutional Neural Network (CNN) combined with Bi-directional Long Short-Term Memory (BiLSTM) and attention mechanism. A self-adaptive strategy that makes the real-time exceptions results available on the cloud production line for holistic rescheduling decisions is brought to make the distributed manufacturing resources intelligent enough to address the influences of different degrees of exceptions at the edge. The applicability and efficiency of the proposed framework are verified through a design case.     

A survey on parallel and distributed multi-agent systems for high performance computing simulations

Simulation has become an indispensable tool for researchers to explore systems without having recourse to real experiments. Depending on the characteristics of the modeled system, methods used to represent the system may vary. Multi-agent systems are often used to model and simulate complex systems. In any cases, increasing the size and the precision of the model increases the amount of computation, requiring the use of parallel systems when it becomes too large. In this paper, we focus on parallel platforms that support multi-agent simulations and their execution on high performance resources as parallel clusters. Our contribution is a survey on existing platforms and their evaluation in the context of high performance computing. We present a qualitative analysis of several multi-agent platforms, their tests in high performance computing execution environments, and the performance results for the only two platforms that fulfill the high performance computing constraints.     

Global consensus of second-order unknown multi-agent systems: A distributed fuzzy iterative learning scheme

This article focuses on global fuzzy consensus control of unknown second-order nonlinear multi-agent systems based on adaptive iterative learning scheme. In order to achieve global consensus, a replacement idea is introduced, where fuzzy systems are used as feedforward compensators to model unknown nonlinear dynamics relying on tracking signals. Considering that the network communication is distributed, a kind of hybrid control protocol is designed to avoid the complete dependence on the tracking signals. In addition, considering the complexity of the external environment, this article extends the above distributed protocol to the case of unknown control directions to study global consensus. Finally, the feasibility of the proposed protocols is verified by Matlab numerical simulations.     

A multi-agent approach using perceptron-based learning for robust operation of distributed chemical reactor networks

Controlling the individual reactors of a chemical reactor network producing different grades of a product requires intelligent reconfiguration strategies. Agent-based approaches are ideal for such distributed manufacturing processes, since they provide flexible, robust, and emergent solutions under dynamically changing process conditions. This paper proposes a multi-layered, multi-agent framework based on a decentralized online learning approach for the supervision of grade transitions in autocatalytic reactor networks. The values for the manipulated variables and the path to the target reactor are determined to give the least disturbance to the system. Case studies illustrate the performance of the approach in managing grade transition and disturbance rejection in a reactor network.     

Retraction Note: Edge-Oriented Spatial Interpolation for Error Concealment of Consecutive B locks

Retraction:Wei Fu and Guang Zhong Xing.Edge-Oriented Spatial Interpolation for Error Concealment of Consecutive Blocks.Journal of Computer Science and Technology 2007,22(3):494-inside back cover.DOI:.10.1007/s11390-007-9044-4.This article was published twice in Journal of Electronics(China),2007,24(2):214-217 under the title of.“A Modified Edge-Oriented Spatial Interpolation Algorithm for Consecutive Blocks Error Concealment”,and in Journal of Computer Science and Technology,2007,22(3):494-497 under the title of“Edge-Oriented Spatial Interpolation for Error Concealment of Consecutive Blocks”.Due to disputes about where the article first appeared this article has been retracted.     

Retraction Note to: A joint model for analyzing topic and sentiment dynamics from large-scale online news

World Wide Web - The Editors-in-Chief have retracted “A joint model for analyzing topic and sentiment dynamics from large-scale online news” [1] because the article shows significant...     

Energy-aware traffic engineering: A routing-based distributed solution for connection-oriented IP networks

The present paper deals with energy saving in IP networks and proposes a distributed energy-aware traffic engineering solution, named DAISIES, for switching off network links according to traffic variations. DAISIES works in a connection-oriented network, e.g. an IP/MPLS network, and follows a routing-based approach, i.e. it acts on the routing algorithm whilst link switch-off/on are consequence of routing decisions. The basic idea is to re-compute the path of each traffic demand when its requested capacity changes. A specific cost function is used to compute link weights into the shortest path routing algorithm with the goal of keeping unused as many links as possible. The main advantages of DAISIES can be summarized as follows: (i) no changes are required to current routing and signaling protocols, (ii) packet loss is completely avoided, (iii) both traffic decreasing and increasing and changing network conditions are automatically managed, and (iv) link switch-off/on take place transparently to the routing protocol and to other nodes. The performance of the proposed solution is evaluated in terms of energy saving relative to a static network optimized to support the peak traffic. Results show that DAISIES is able to save about 30% of energy in several traffic conditions. Moreover, it is shown that it is possible keeping the additional complexity low and still reaching high energy efficiency.