An agent-based dynamic information network for supply chain management

One of the main research issues in supply chain management is to improve the global efficiency of supply chains. However, the improvement efforts often fail because supply chains are complex, are subject to frequent changes, and collaboration and information sharing in the supply chains are often infeasible. This paper presents a practical collaboration framework for supply chain management wherein multi-agent systems form dynamic information networks and coordinate their production and order planning according to synchronized estimation of market demands. In the framework, agents employ an iterative relaxation contract net protocol to find the most desirable suppliers by using data envelopment analysis. Furthermore, the chain of buyers and suppliers, from the end markets to raw material suppliers, form dynamic information networks for synchronized planning. This paper presents an agent-based dynamic information network for supply chain management and discusses the associated pros and cons

Platform-level Distributed Warfare Model-based on Multi-Agent System Framework

The multi-agent paradigm has become a useful tool in solving military problems. However, one of key challenges in multi-agent model for distributed warfare could be how to describe the microcosmic  tactical warfare platforms actions. In this paper, a platform-level distributed warfare model based on multi-agent system framework is designed to tackle this challenge. The basic ideas include:  Establishing multi-agent model by mapping from tactical warfare system’s members, i.e., warfare platforms, to respective agents; performing task decomposition and task allocation by using task-tree decomposition method and improved contract net protocol model technique; and implementing simulation by presenting battlefield terrain environment analysis algorithm based on grid approach. The  simulation demonstration results show that our model provides a feasible and effective approach to supporting the abstraction and representation of microcosmic tactical actions for complex warfare system.Defence Science Journal, 2012, 62(1), pp.180-186, DOI:http://dx.doi.org/10.14429/dsj.62.96

Heterogeneous Agent Development: A Multi-Agent System for Testing Stock Trading Algorithms

Intelligent agents have often been used as a method for simulating an active equity market environment. While agents have been used extensively in trading and market simulations, agents have not been used in a system that only evaluates trading algorithms. To accomplish the simulations, agents are developed in a single or proprietary programming language. The use of agents developed in Microsoft’s .Net framework and CLR provides flexibility, scalability, compatibility, and interoperability beyond traditional agent development environments. This paper presents a multi-agent system developed using native JAVA, VB.Net, C# and PHP, all in the .Net environment. The system will demonstrate its abilities by comparing two equity trading algorithms

Radical Agent-based Approach for Intelligence Analysis

This paper presents a novel agent-based framework as a decision aid tool for intelligence analysis. This technology extends net-centric information processing and abstraction as well as fusion and multi-source integration strategies. Our information agents traverse and mediate disparate ontologies in different formats providing a foundation for semantic interoperability. The presented system provides knowledge discovery by accessing a large number of information sources in a particular domain and organizing them into a network of information agents. Each agent provides expertise on a specific topic by drawing on relevant information from other information agents in related knowledge domains. Unique advantages include net-centric scalability, principled information assurance, as well as ground breaking knowledge discovery in service of intelligence analysis

The Regional Multi-Agent Simulator (RegMAS): assessing the impact of the "Health Check" in an Italian region

Agent-based models (AMB) allow to conceive social systems as the result of individually-acting agents. When they are applied to agriculture, they can simulate the fundamental behaviour at the micro-level of the individual farmers, without the need of aggregating them in “representative” agents. RegMAS (Regional Multi Agent Simulator) is an open-source spatially explicit multi-agent model framework specifically designed for long-term simulations of effects of policies on agricultural systems. Using iterated conventional optimisation problems as agents' behavioural rules, it allows for a bidirectional integration between geophysical and social models where spatially distributed characteristics are taken into account in the linear programming problem of the optimising agents as individual resources. The model is applied to asses the impact of the Health Check, the imminent further Common Agricultural Policy (CAP) reform, on farms structures, incomes and land use in a hilly area of a central Italian region (Marche). Our results suggest that the Health Check, while increasing the farmer profit net of CAP support, is substantially neutral on the overall farmer incomes, also through a reduction of the off-farm labour. Neverless, a limited negative effects may arise in the farms numerousness, with the consequence of a land abandonment that is noticeable only on mountain areas, where distances between farmers are greater.Agent based model, health check, regional economics, Agricultural and Food Policy, Research Methods/ Statistical Methods,

Large-Scale Multi-Agent Transport: Theory, Algorithms and Analysis

The problem of transport of multi-agent systems has received much attention in a wide range of engineering and biological contexts, such as spatial coverage optimization, collective migration, estimation and mapping of unknown environments. In particular, the emphasis has been on the search for scalable decentralized algorithms that are applicable to large-scale multi-agent systems.For large multi-agent collectives, it is appropriate to describe the configuration of the collective and its evolution using macroscopic quantities, while actuation rests at the microscopic scale at the level of individual agents. Moreover, the control problem faces a multitude of information constraints imposed by the multi-agent setting, such as limitations in sensing, communication and localization. Viewed in this way, the problem naturally extends across scales and this motivates a search for algorithms that respect information constraints at the microscopic level while guaranteeing performance at the macroscopic level.We address the above concerns in this dissertation on three fronts: theory, algorithms and analysis. We begin with the development of a multiscale theory of gradient descent-based multi-agent transport that bridges the microscopic and macroscopic perspectives and sets out a general framework for the design and analysis of decentralized algorithms for transport. We then consider the problem of optimal transport of multi-agent systems, wherein the objective is the minimization of the net cost of transport under constraints of distributed computation. This is followed by a treatment of multi-agent transport under constraints on sensing and communication, in the absence of location information, where we study the problem of self-organization in swarms of agents. Motivated by the problem of multi-agent navigation and tracking of moving targets, we then present a study of moving-horizon estimation of nonlinear systems viewed as a transport of probability measures. Finally, we investigate the robustness of multi-agent networks to agent failure, via the problem of identifying critical nodes in large-scale networks

Active flow control for three-dimensional cylinders through deep reinforcement learning

This paper presents for the first time successful results of active flow control with multiple independently controlled zero-net-mass-flux synthetic jets. The jets are placed on a three-dimensional cylinder along its span with the aim of reducing the drag coefficient. The method is based on a deep-reinforcement-learning framework that couples a computational-fluid-dynamics solver with an agent using the proximal-policy-optimization algorithm. We implement a multi-agent reinforcement-learning framework which offers numerous advantages: it exploits local invariants, makes the control adaptable to different geometries, facilitates transfer learning and cross-application of agents and results in significant training speedup. In this contribution we report significant drag reduction after applying the DRL-based control in three different configurations of the problem.Comment: ETMM14 2023 conference proceeding pape

Architecture and negotiation protocols for a smart parking system

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáSmart City uses emerging technologies to improve citizens’ quality of life. A branch of this topic is the Smart Parking, where the parking system implements intelligent mechanisms to simplify to the searching of parking spots and consequently decrease the traffic of cars. This work proposes an architecture using Multi-Agent System (MAS), enhanced with some holonic systems principles, that is capable to be applied to different range of parking systems, e.g., considering trucks, cars, or bicycles. Being a distributed architecture, a special attention is devoted to study the negotiation protocols that will regulate the behavior of autonomous and cooperative actors in the system, namely drivers and parking spots, during allocation process of parking spots to drivers. For this purpose, the Contract Net Protocol (CNP), English Auction, Dutch Auction and Faratin Auction were the tested, being the CNP the selected protocol for this problem. Also addressing the distributed nature of the system, some efforts were focused on the security of the messages exchanged between the agents was proposed using Secure Socket Layer (SSL). The proposed multi-agent systems architecture was implemented using JADE (Java Agent DEvelopment Framework), which is a FIPA-compliant agent development framework that simplifies the development of agent-based applications. The exchange of messages follows the FIPA-ACL protocol using the CNP protocol for the negotiation. The communication between the agents and the User Interface is performed through the use of Message Queuing Telemetry Transport (MQTT) protocol