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M.Sc. / Advancements in technology used in financial markets have led to substantial automation of tasks within the financial industry. Data analysis, trade execution and trade processing have been automated, reducing costs and increasing productivity. Algorithmic trading is the automated execution of trades on an electronic trading platform; it has been used to gain competitive advantage in financial markets since the early 1990s. Algorithmic trading applications, which must analyse information and determine whether to buy or sell, are well suited to the use of autonomous software agents. Multi-agent systems are better suited to the increasing complexity of algorithmic trading systems and the flexibility required by rapidly changing markets than single-agent systems. The granularity of components (agents) in multi-agent systems also promotes reuse and simplifies individual agent design. Algorithmic trading is, however, subject to challenges specifically in terms of data volume, speed of access and speed of processing. In order to utilise a multi-agent system solution the interactions between agents which allow distributed problem solving must be as efficient as possible. This dissertation investigates the use of indirect coordination to improve the efficiency of interactions between agents in multi-agent systems and to simplify agent design. Indirect coordination utilises environment abstractions known as artefacts to facilitate interaction between agents; such interaction can be simple data transfer or requests, complex coordination protocols as well as negotiation protocols. The investigation resulted in a framework that allows agents to transition between direct and indirect interaction techniques based on the specific interaction task at hand. The framework is built on two existing platforms, ii Java Agent DEvelopment Framework (JADE) and Common ARTifact Infrastructure for AGents Open environments (CARTAGO). These platforms are combined into the JADE-CARTAGO Algorithmic Trading (JCAT) framework that provides the infrastructure needed for both direct and indirect interactions. Investigations into the performance of the JCAT framework have shown that artefacts improve interaction efficiency by reducing data loss in tasks such as information publishing, and perform as well as direct communication within certain constraints for other tasks. When limiting the number of agents in an interaction to 50 agents, artefacts perform at least as well as direct communication using agent communication language messages.

abstract: This work describes an approach for distance computation between agents in a multi-agent swarm. Unlike other approaches, this work relies solely on signal Angleof- Arrival (AoA) data and local trajectory data. Each agent in the swarm is able to discretely determine distance and bearing to every other neighbor agent in the swarm. From this information, I propose a lightweight method for sensor coverage of an unknown area based on the work of Sameera Poduri. I also show that this technique performs well with limited calibration distances. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2020

Um subconjunto importante da pesquisa em sistemas multiagentes (SMA) baseiase no estudo das organizações. A organização define a estrutura do SMA e as regras que os agentes devem seguir, a fim de aumentar a eficiência do sistema. No entanto, dado um domínio, a escolha da organização que melhor resolve o problema ainda é uma questão sem resposta. Assim, abordagens empíricas para a avaliação de organizações são importantes, pois fornecem indícios valiosos sobre os custos e benefícios de diferentes configurações organizacionais, ajudando desenvolvedores e projetistas na definição da organização a ser adotada. Neste contexto, este trabalho, compara e avalia o impacto da mudança de parâmetros organizacionais no desempenho de um SMA, cujo objetivo é competir no cenário Agents on Mars proposto no Multi-Agent Programming Contest (MAPC). / An important subset of multi-agent systems (MAS) are based on the study of organizations. The organization defines the MAS structure and the rules which the agents must follow, increasing the MAS efficiency. Given an application domain, however, the choice of a particular organization that better solves the problem is still an open problem. Therefore, empirical approaches to the evaluation of organizations are important since they provide valuable evidences about the costs and benefits of different organizational settings, helping developers and designers to define the organization to be adopted. In this context, this work compares and evaluates the impact of organizational changes in the performance of a MAS, whose goal is to evolve in the \"Agents on Mars\" scenario proposed in the Multi-Agent Programming Contest (MAPC).

Um subconjunto importante da pesquisa em sistemas multiagentes (SMA) baseiase no estudo das organizações. A organização define a estrutura do SMA e as regras que os agentes devem seguir, a fim de aumentar a eficiência do sistema. No entanto, dado um domínio, a escolha da organização que melhor resolve o problema ainda é uma questão sem resposta. Assim, abordagens empíricas para a avaliação de organizações são importantes, pois fornecem indícios valiosos sobre os custos e benefícios de diferentes configurações organizacionais, ajudando desenvolvedores e projetistas na definição da organização a ser adotada. Neste contexto, este trabalho, compara e avalia o impacto da mudança de parâmetros organizacionais no desempenho de um SMA, cujo objetivo é competir no cenário Agents on Mars proposto no Multi-Agent Programming Contest (MAPC). / An important subset of multi-agent systems (MAS) are based on the study of organizations. The organization defines the MAS structure and the rules which the agents must follow, increasing the MAS efficiency. Given an application domain, however, the choice of a particular organization that better solves the problem is still an open problem. Therefore, empirical approaches to the evaluation of organizations are important since they provide valuable evidences about the costs and benefits of different organizational settings, helping developers and designers to define the organization to be adopted. In this context, this work compares and evaluates the impact of organizational changes in the performance of a MAS, whose goal is to evolve in the \"Agents on Mars\" scenario proposed in the Multi-Agent Programming Contest (MAPC).

Published Article / To enable meaningful and consistent communication between different software systems in a particular domain (such as manufacturing, law or medicine), a standardised vocabulary and communication language is required by all the systems involved. Concepts in the domain about which the systems want to communicate are formalized in an ontology by establishing the meaning of concepts and creating relationships between them. The inputs to this process in found by analysing the physical domain and its processes. The resulting ontology structure is a computer useable representation of the physical domain about which the systems want to communicate. To enable the long term persistence of the actual data contained in these concepts and the enforcement of various business rules, a sufficiently powerful database system is required. This paper presents the design of a manufacturing ontology and its accompanying relational database schema that will be used in a manufacturing test domain.

Manufacturers are facing increasing pressure to reduce the development costs and deployment times for automated assembly systems. This is especially true for a variety of precision mechatronic products. To meet new and changing market needs, the difficulties of integrating their systems must be significantly reduced. Since 1994, the Microdynamic Systems Laboratory at Carnegie Mellon University has been developing an automation framework, called Agile Assembly Architecture (AAA). Additionally to the concept, a prototype instantiation, in the form of a modular tabletop precision assembly system termed Minifactory, has been developed. The platform, provided by the Minifactory and AAA, is able to support and integrate various precision manufacturing processes. These are needed to assemble a large variety of small mechatronic products. In this thesis various enhancements for a second generation agent-based micro assembly system are designed, implemented, tested and improved. The project includes devising methods for tray feeding of precision high-value parts, micro fastening techniques and additional work on visual- and force-servoing. To help achieving these functions, modular and reconfigurable robot end-effectors for handling millimeter sized parts have been designed and built for the existing robotic agents. New concepts for robot end effectors to grasp and release tiny parts, including image processing and intelligent control software, were required and needed to be implemented in the prototype setup. These concepts need to distinguish themselves largely from traditional handling paradigms, in order to solve problems introduced by electrostatic and surface tension forces, that are dominant in manipulating parts that are millimeter and less in size. In order to have a modular system, the factory the main part of this project was the initialization and auto calibration of the different agents. The main focus, of this research, is on improving the design, deployment and reconfiguration capabilities of automated assembly systems for precision mechatronic products. This helps to shorten the development process as well as the assembly of factory systems.  A strategic application for this approach is the automated assembly of small sensors, actuators, medical devices and chip-scale atomic systems such as atomic clocks, magnetometers and gyroscopes.

Networks of communicating agents require distributed algorithms for a variety of tasks in the field of network analysis and control. For applications such as swarms of autonomous vehicles, ad hoc and wireless sensor networks, and such military and civilian applications as exploring and patrolling a robust autonomous system that uses a distributed algorithm for self-partitioning can be significantly helpful. A single team of autonomous vehicles in a field may need to self-dissemble into multiple teams, conducive to completing multiple control tasks. Moreover, because communicating agents are subject to changes, namely, addition or failure of an agent or link, a distributed or decentralized algorithm is favorable over having a central agent. A framework to help with the study of self-partitioning of such multi agent systems that have most basic mobility model not only saves our time in conception but also gives us a cost effective prototype without negotiating the physical realization of the proposed idea. In this thesis I present my work on the implementation of a flexible and distributed stochastic partitioning algorithm on the Lego® Mindstorms’ NXT on a graphical programming platform using National Instruments’ LabVIEW™ forming a team of communicating agents via NXT-Bee radio module. We single out mobility, communication and self-partition as the core elements of the work. The goal is to randomly explore a precinct for reference sites. Agents who have discovered the reference sites announce their target acquisition to form a network formed based upon the distance of each agent with the other wherein the self-partitioning begins to find an optimal partition. Further, to illustrate the work, an experimental test-bench of five Lego NXT robots is presented.

This project first explored various methods of designing a communication and control system for an AGV. It then implemented a SOA based communication system in ROS on the selected AGV. The ROS package created in the project implemented functions of the Aria and ArNetworking libraries from Adept. The next step of the project implemented the functions of teleoperation, mapping and transfer of maps and navigation from Aria into ROS. The packages built implemented these functions in different ways to test the best method to transfer Aria functions into ROS. A generic set of rules were then formulated that aided the conversion of these functions for users unfamiliar with either of the two packages (ROS and Aria).

Machine condition monitoring is gaining importance in industry due to the need to increase machine reliability and decrease the possible loss of production due to machine breakdown. Often the data available to build a condition monitoring system does not fully represent the system. It is also often common that the data becomes available in small batches over a period of time. Hence, it is important to build a system that is able to accommodate new data as it becomes available without compromising the performance of the previously learned data. In real-world applications, more than one condition monitoring technology is used to monitor the condition of a machine. This leads to large amounts of data, which require a highly skilled diagnostic specialist to analyze. In this thesis, artificial intelligence (AI) techniques are used to build a condition monitoring system that has incremental learning capabilities. Two incremental learning algorithms are implemented, the first method uses Fuzzy ARTMAP (FAM) algorithm and the second uses Learn++ algorithm. In addition, intelligent agents and multi-agent systems are used to build a condition monitoring system that is able to accommodate various analysis techniques. Experimentation was performed on two sets of condition monitoring data; the dissolved gas analysis (DGA) data obtained from high voltage bushings and the vibration data obtained from motor bearing. Results show that both Learn++ and FAM are able to accommodate new data without compromising the performance of classifiers on previously learned information. Results also show that intelligent agent and multi-agent system are able to achieve modularity and flexibility.