306,897 research outputs found

    The use of agent to incorporate network awareness into dynamic proxy framework: an overview

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    It has been observed that research in mobile agent focused on the development of platforms and on the application of the concept. Network awareness is one of the applications of mobile agent, concerning of how agent determine and make the most efficient use of network resources. This paper describes the role and functionality of mobile agent in the context of a dynamic proxy framework named the Chek Proxy Framework (CPF). Attention is paid on setting out a multi-agent-based framework to enrich CPF with a Network Awareness Module (NAM), which is implemented in an agent named ObjectBasket (OB). NAM is a single framework that integrates resource discovery, load monitoring and migration, and fault management, to operate in a network with dynamic proxy servers. The main objective of NAM is to deliver robustness and best-effort QoS guarantees into the existing CPF system adaptively, based on the availability of resources in the network. In this paper, we present the architecture of NAM, status generation algorithm, implementation rules, and the management of faults and overloading.Facultad de Informátic

    Intrinsic Regulators of Actomyosin Contractility Engendering Pulsatile Behaviors

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    Actomyosin contractility regulates various biological processes including cell migration, muscle contraction, and tissue morphogenesis. Cell cortex underlying a membrane, which is a representative actomyosin network in eukaryote cells, exhibits dynamic contractile behaviors. Interestingly, the cell cortex shows reversible aggregation of actin and myosin called pulsatile contraction in diverse cellular phenomena, such as embryogenesis and tissue morphogenesis. While contractile behaviors have been studied in several in vitro experiments and computational studies, none of them demonstrated the pulsatile contraction of actomyosin networks observed in vivo. Here, we used an agent-based computational model based on Brownian dynamics to identify factors facilitating the pulsatile contraction of actomyosin networks. We first tested effects of several important parameters on the morphology, stress generation, and dynamic properties of actomyosin networks in order to understand how they regulate contraction of actomyosin networks. We found that the pulsatile contraction only occurs when there is a subtle balance between force generation from motors, force relaxation via actin turnover, and force sustainment via network connectivity. Our study provides critical insights into understanding the mechanisms and roles of the pulsatile contraction in cells

    A parallelized micro-simulation platform for population and mobility behavior. Application to Belgium.

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    In this book we aim at developing an agent-based micro-simulation framework for (large) population evolution and mobility behaviour. More specifically we focus on the agents generation and the traffic simulation parts of the platform, and its application to Belgium. Hence we firstly develop a synthetic population generator whose main characteristics are its sample-free nature, its ability to cope with moderate data inconsistencies and different levels of aggregation. We then generate the traffic demand forecasting with a stochastic and flexible activity-based model relying on weak data requirements. Finally, a traffic simulation is completed by considering the assignment of the generated demand on the road network. We give the initial developments of a strategic agent-based alternative to the conventional simulation-based dynamic traffic assignment models

    Location Awareness in Multi-Agent Control of Distributed Energy Resources

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    The integration of Distributed Energy Resource (DER) technologies such as heat pumps, electric vehicles and small-scale generation into the electricity grid at the household level is limited by technical constraints. This work argues that location is an important aspect for the control and integration of DER and that network topology can inferred without the use of a centralised network model. It addresses DER integration challenges by presenting a novel approach that uses a decentralised multi-agent system where equipment controllers learn and use their location within the low-voltage section of the power system. Models of electrical networks exhibiting technical constraints were developed. Through theoretical analysis and real network data collection, various sources of location data were identified and new geographical and electrical techniques were developed for deriving network topology using Global Positioning System (GPS) and 24-hour voltage logs. The multi-agent system paradigm and societal structures were examined as an approach to a multi-stakeholder domain and congregations were used as an aid to decentralisation in a non-hierarchical, non-market-based approach. Through formal description of the agent attitude INTEND2, the novel technique of Intention Transfer was applied to an agent congregation to provide an opt-in, collaborative system. Test facilities for multi-agent systems were developed and culminated in a new embedded controller test platform that integrated a real-time dynamic electrical network simulator to provide a full-feedback system integrated with control hardware. Finally, a multi-agent control system was developed and implemented that used location data in providing demand-side response to a voltage excursion, with the goals of improving power quality, reducing generator disconnections, and deferring network reinforcement. The resulting communicating and self-organising energy agent community, as demonstrated on a unique hardware-in-the-loop platform, provides an application model and test facility to inspire agent-based, location-aware smart grid applications across the power systems domain

    Active Response Using Host-Based Intrusion Detection System and Software-Defined Networking

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    This research proposes AHNSR: Active Host-based Network Security Response by utilizing Host-based Intrusion Detection Systems (HIDS) with Software-Defined Networking (SDN) to enhance system security by allowing dynamic active response and reconstruction from a global network topology perspective. Responses include traffic redirection, host quarantining, filtering, and more. A testable SDN-controlled network is constructed with multiple hosts, OpenFlow enabled switches, and a Floodlight controller, all linked to a custom, novel interface for the Open-Source SECurity (OSSEC) HIDS framework. OSSEC is implemented in a server-agent architecture, allowing scalability and OS independence. System effectiveness is evaluated against the following factors: alert density and a selective Floodlight module response types. At the expected operational load of 500 events per second (EPS), results reveal a mean system response time of 0.5564 seconds from log generation to flow table update via Floodlights Access Control List module. Load testing further assesses performance at 10 - 10000 EPS for all tested response modules

    Route Generation for a Synthetic Character (BOT) Using a Partial or Incomplete Knowledge Route Generation Algorithm in UT2004 Virtual Environment

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    This paper presents a new Route Generation Algorithm that accurately and realistically represents human route planning and navigation for Military Operations in Urban Terrain (MOUT). The accuracy of this algorithm in representing human behavior is measured using the Unreal Tournament(Trademark) 2004 (UT2004) Game Engine to provide the simulation environment in which the differences between the routes taken by the human player and those of a Synthetic Agent (BOT) executing the A-star algorithm and the new Route Generation Algorithm can be compared. The new Route Generation Algorithm computes the BOT route based on partial or incomplete knowledge received from the UT2004 game engine during game play. To allow BOT navigation to occur continuously throughout the game play with incomplete knowledge of the terrain, a spatial network model of the UT2004 MOUT terrain is captured and stored in an Oracle 11 9 Spatial Data Object (SOO). The SOO allows a partial data query to be executed to generate continuous route updates based on the terrain knowledge, and stored dynamic BOT, Player and environmental parameters returned by the query. The partial data query permits the dynamic adjustment of the planned routes by the Route Generation Algorithm based on the current state of the environment during a simulation. The dynamic nature of this algorithm more accurately allows the BOT to mimic the routes taken by the human executing under the same conditions thereby improving the realism of the BOT in a MOUT simulation environment

    Goals are Enough: Inducing AdHoc cooperation among unseen Multi-Agent systems in IMFs

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    Intent-based management will play a critical role in achieving customers' expectations in the next-generation mobile networks. Traditional methods cannot perform efficient resource management since they tend to handle each expectation independently. Existing approaches, e.g., based on multi-agent reinforcement learning (MARL) allocate resources in an efficient fashion when there are conflicting expectations on the network slice. However, in reality, systems are often far more complex to be addressed by a standalone MARL formulation. Often there exists a hierarchical structure of intent fulfilment where multiple pre-trained, self-interested agents may need to be further orchestrated by a supervisor or controller agent. Such agents may arrive in the system adhoc, which then needs to be orchestrated along with other available agents. Retraining the whole system every time is often infeasible given the associated time and cost. Given the challenges, such adhoc coordination of pre-trained systems could be achieved through an intelligent supervisor agent which incentivizes pre-trained RL/MARL agents through sets of dynamic contracts (goals or bonuses) and encourages them to act as a cohesive unit towards fulfilling a global expectation. Some approaches use a rule-based supervisor agent and deploy the hierarchical constituent agents sequentially, based on human-coded rules. In the current work, we propose a framework whereby pre-trained agents can be orchestrated in parallel leveraging an AI-based supervisor agent. For this, we propose to use Adhoc-Teaming approaches which assign optimal goals to the MARL agents and incentivize them to exhibit certain desired behaviours. Results on the network emulator show that the proposed approach results in faster and improved fulfilment of expectations when compared to rule-based approaches and even generalizes to changes in environments.Comment: Accepted for publication in IEEE CCNC 2024 conferenc

    A multi-agent model for assessing electricity tariffs

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    This paper describes the framework for modelling a multi-agent approach for assessing dynamic pricing of electricity and demand response. It combines and agent-based model with decision-making data, and a standard load-flow model. The multi-agent model described here represents a tool in investigating not only the relation between different dynamic tariffs and consumer load profiles, but also the change in behaviour and impact on low-voltage electricity distribution networks.The authors acknowledge the contribution of the EPSRC Transforming Energy Demand Through Digital Innovation Programme, grant agreement numbers EP/I000194/1 and EP/I000119/1, to the ADEPT project
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