An implementation of the Electric Field Approach applied to the simulated RoboCup is presented, together with a demonstration of a learning system. Results are presented from the optimization of the Electric Field parameters in a limited situation, using the learning system. Learning techniques used in contemporary RoboCup research are also described including a brief presentation of their results.

Les cartes représentent l'information géographique d'une zone donnée de manière d'autant plus simplifiée que l'échelle de la carte est petite. Le procédé de simplification, appelé généralisation cartographique, est soumis au respect de contraintes de lisibilité, d'adéquation de la représentation avec le niveau d'abstraction souhaité et de cohérence avec la réalité. La volonté d'automatiser le processus de création de cartes à partir de bases de données géographiques, a conduit à la création d'algorithmes permettant d'effectuer cette simplification objet par objet. Néanmoins, les choix des algorithmes, tout comme leur paramétrage, sont autant influencés par l'objet sur lequel ils s'appliquent que par les autres objets en relation (e.g. bâtiment à proximité d'un autre, route parallèle à un alignement de bâtiments). Ce constat a motivé l'utilisation de modèles multi-agents pour la généralisation automatisée de cartes. Le principe de ces modèles multi-agents repose sur la modélisation des objets (e.g. bâtiment, tronçon de route, îlot urbain) sous forme d'agents qui cherchent à se généraliser de façon à satisfaire leurs contraintes. Plusieurs modèles multi-agents ont été proposés, chacun ayant une approche différente des interactions entre niveaux. Ici, nous entendons par niveau, par exemple, la distinction entre les agents individuels comme un bâtiment, des agents représentant un groupe d’autres agents, comme un îlot urbain composé des routes l’entourant et des bâtiments inclus dans l’îlot.Nous étudions l'unification de ces modèles en nous appuyant sur le paradigme multi-niveaux PADAWAN, afin de faciliter les interactions entre agents de niveaux différents. Nous proposons ainsi le modèle DIOGEN, adaptant les principes d’interaction entre agents de niveaux différents à la généralisation cartographique guidée par des contraintes, ce qui a permis d’unifier les précédents modèles AGENT, CartACom et GAEL, tout en disposant de nouvelles capacités prometteuses.Nous avons évalué notre proposition sur un ensemble de cas d’étude. Parmi ces cas, nous nous sommes penchés sur la généralisation de carte de randonnée, où les itinéraires sont symbolisés individuellement avec des symboles différents, à la manière des plans de bus. La présence de plusieurs symboles d’itinéraires sur une même route support amène des problèmes de généralisation particuliers, comme le choix du positionnement des itinéraires de part et d’autre de la route, ou les implications pour les autres objets de la carte (e.g. points d’intérêts, bâtiments) se retrouvant sous le symbole de l’itinéraire, problèmes que nous essayons de résoudre en nous appuyant sur notre proposition de représentation formelle multi-niveaux.Ce travail nous a ensuite conduit à identifier des comportements multi-niveaux récurrents. Nous les avons exprimés de façon générique sous forme de patterns d’analyse, affranchies des spécificités de la généralisation cartographique, et de la résolution de problèmes contraints / Maps show geographic information of a given area in a simplified way, particularly when the scale is small. The simplification process, called cartographic generalisation, is submitted to several constraints : legibility, adequation to the abstraction level, and consistency with reality. The will to automate the maps creation process from geographical databases led to the creation of algorithms allowing the simplification object by object. However the choice of the algorithms, as their settings, are influenced by the object on which it is applied, and by the other objects in relation with this object (e.g. a building close to another one, a road parallel to a buildings alignment). This motivates the use of multi-agents models for automated map generalisation. Several multi-agent models were proposed, each of them having a different approach to manage multi-levels relations. Here, what we call a level is, for instance, the distinction between individual agents, like a building, and agents representing a group of other agents, like a urban block composed by the surrounding roads and buildings inside.We study the unification of existing models, using the multi-level paradigm PADAWAN, in order to simplify interactions between agents in different levels. We propose the DIOGEN model, in which the principle of interactions between agents of different levels is adapted to cartographic generalisation guided by constraints, those allowing to unify the existing models AGENT, CartACom and GAEL, and giving promising features.We evaluate our proposal on different case studies. Among them, we study the generalisation of trekking maps, where the routes are symbolized individually by a different couloured line symbols, like on bus maps. The presence of several route symbols on a same road leads to specific generalisation issues, like the choice of the side of each route symbol position, or the implications for the other objects on the map (e.g. points of interest, buildings) under the route symbol – issues tackled using our proposal of formal multi-levels representation.This work leads us to the identification of recurrent behaviours. We express them as analysis patterns, in a way that is independent from cartographic generalisation and constraint solving problems

Energy is consumed every day at home as we perform simple tasks, such as watching television, washing dishes and heating/cooling home spaces during season of extreme weather conditions, using appliances, or turning on lights. Most often, the energy resources used in residential systems are obtained from natural gas, coal and oil. Moreover, climate change has increased awareness of a need for expendable, energy resources. As a result, carbon dioxide emissions are increasing and creating a negative effect on our environment and on our health. In fact, growing energy demands and limited natural resource might have negative impacts on our future. Therefore, saving energy is becoming an important issue in our society and it is receiving more attention from the research community. This thesis introduces a intelligent energy controller algorithm based on software agent approach that reduce the energy consumption at home for both heating and cooling spaces by considering the user’s occupancy, outdoor temperature and user’s preferences as input to the system. Thus the proposed approach takes into consideration the occupant’s preferred temperature, the occupied and unoccupied spaces, as well as the time spent in each area of the home. A Java based simulator has been implemented to simulate the algorithm for saving energy in heating and cooling systems. The results from the simulator are compared to the results of using HOT2000, which is Canada’s leading residential energy analysis and rating software developed by CanmetENERGY’s Housing, Buildings, Communities and Simulation (HBCS) group. We have calculated how much energy a home modelled will use under emulated conditions. The results showed that the implementation of the proposed energy controller algorithm can save up to 50% in energy consumption in homes dedicated to heating and cooling systems compared to the results obtained by using HOT2000.

Unexpected changes occurring in complex and dynamic domains render supporting systems unsuited to the new conditions. Examples of such domains include business ecosystems, digital service ecosystems, manufacturing, transport, and city modelling. These are regarded as ecosystem domains. Multi-agent systems are seen as an appropriate technology for their support, yet they lack the required ability to adapt to unexpected changes. The research presented in this thesis aims to create a multi-agent system based in-silico model endowed with the capability of adaptation to unexpected changes occurring in ecosystem domains. The approach taken consists of applying adaptation properties of complex adaptive systems, such as natural ecosystems, to multi-agent systems to create one which can cope with unexpected changes. A dynamic agent-based ecosystem model called DAEM is formalised by combining characteristics of natural ecosystem and principles of adaptive multi-agent systems. A set of experiments is presented using a DAEM prototype to demonstrate its resilience to unexpected changes in a hypothetical ecosystem. A comparison is made against a simulated typical solution for showing how DAEM is more resilient to unexpected changes than the typical approach. This supports the claim of this thesis that DAEM represents a significant contribution to knowledge. A software embodiment of DAEM to drive adaptation in ecosystem domains is presented and placed in an execution context evaluated by two practical examples of ecosystem domains. These show how DAEM suggests interactions to the supporting system of the execution context, and incorporates taken decisions into the ecosystem regarding interactions with other individuals. This supports the claim that the DAEM software embodiment is suitable for providing adaptation support in ecosystem domains, thus representing another significant contribution of this thesis. The contributions to knowledge of this thesis are then a) a formal model of a dynamic agent-based ecosystem called DAEM; and b) a software embodiment of DAEM, called DAEM layer, to support adaptation in ecosystem domains. Future work includes further tests to analyse patterns and make estimations in existing ecosystems, among others.

This lever deals with the realization of the iterpreter of an Driven Agent by the PROLOG implementation. The model was used by Jason implemented in Java that interprets the language of AgentSpeak(L). For the purposes of this project, the program in AgentSpeak(L) is first converted to an internal language form. For the demonstration, one of the examples included in the Jason program, specifically "cleaning robors", was used. The interpreter can interpret the system as a FRAg and can react in the enviroment.

This lever deals with the realization of the iterpreter of an Driven Agent by the PROLOG implementation. The model was used by Jason implemented in Java that interprets the language of AgentSpeak(L). An interpreter and a program for processing agent systems in the language AgentSpeak (L) were created. This interpreter can work with multiple agents, can implement a system with an environment and use the FRAg system for interpretation. Examples of agent systems in AgentSpeak (L) were proposed to describe the functionality of the interpreter, and subsequently the advantages and disadvantages of the FRAg system were described.

The goal of this master's thesis is to design and implement framework that can be used for development of agent systems. Framework is implemented in Java and encapsulates JADE library. Framework is used for implementation of adventure game. There are several characters (agents) with specific roles who cooperate and try to achieve their goals.

<p>Food security is a major concern of human civilization. A way to ensure food security is to grow plants in a greenhouse under controlled conditions. Even under careful greenhouse production, stress in plants can emerge, and can cause damaging disease. To prevent yield loss farmers, apply resources, e.g., water, fertilizers, pesticides, higher/lower humidity, lighting, and temperature, uniformly in the infected areas. Research, however, shows that the practice leads to non-optimal profit and environmental protection.</p><p>Precision agriculture (PA) is an approach to address such challenges. It aims to apply the right amount or recourses at the right time and place. PA has been able to maximize crop yield while minimizing operation cost and environmental damage. The problem is how to obtain timely, precise information at each location to optimally treat the plants. There is scant research addressing strategies, algorithms, and protocols for analytics in PA. A monitoring and treating systems are the foci of this dissertation.</p><p>The designed systems comprise of agent- and system-level protocols and algorithms. There are four parts: (1) Collaborative Control Protocol for Cyber-Physical System (CCP-CPS); (2) Collaborative Control Protocol for Early Detection of Stress in Plants (CCP-ED); (3) Optimal Inspection Profit for Precision Agriculture; and (4) Multi-Agent System Optimization in Greenhouse for Treating Plants. CCP-CPS, a backbone of the system, establishes communication line among agents. CCP-ED optimizes the local workflow and interactions of agents. Next, the Adaptive Search algorithm, a key algorithm in CCP-ED, has analyzed to obtain the optimal procedure. Lastly, when stressed plants are detected, specific agents are dispatched to treat plants in a particular location with specific treatment. </p><p>Experimental results show that collaboration among agents statistically and significantly improves performance in terms of cost, efficiency, and robustness. CCP-CPS stabilizes system operations and significantly improves both robustness and responsiveness. CCP-ED enabling collaboration among local agents, significantly improves the number of infected plants found, and system efficiency. Also, the optimal Adaptive Search algorithm, which considers system errors and plant characteristics, significantly reduces the operation cost while improving performance. Finally, with collaboration among agents, the system can effectively perform a complex task that requires multiple agents, such as treating stressed plants with a significantly lower operation cost compared to the current practice.</p>

The thesis introduces an innovative UAV detection system. The commercial UAV market is booming. Meanwhile, the risks and threats from improper UAV usages are also booming. CUAS is to protect the public and facilities. The problem is a lack of an intelligent platform which can adapt many sensors for UAV detection. The hypothesis is that, the system can track the UAV’s movement by applying the multi-agent system (MAS) to UAV route track. The experiment proves that the multi-agent system benefits for the UAV track. <br>

<div>Enhancing the capability to control a tremendous range of physical actuators and sensors, combined with wireless technology and the Internet of Things (IoT), apparel technologies play a significant role in supporting safe, comfortable and healthy living, observing each customer’s conditions. Since apparel technologies have advanced to enable humans to work as a team with the clothing they wear, the interaction between a human and apparel is further enhanced with the introduction of sensors, wireless network, and artificially intelligent techniques. A variety of wearable technologies have been developed and spread to meet the needs of customers, however, some wearable devices are considered as non-practical tech-oriented, not consumer-oriented.</div><div>The purpose of this research is to develop an apparel system which integrates intelligent autonomous agents, human-based sensors, wireless network protocol, mobile application management system and a zipper robot. This research is an augmentation to the existing research and literature, which are limited to the zipping and unzipping process without much built in intelligence. This research is to face the challenges of the elderly and people with self-care difficulties. The intent is to provide a scientific path for intelligent zipper robot systems with potential, not only to help people, but also to be commercialized.</div><div>The research develops an intelligent system to control of zippers fixed on garments, based on the profile and desire of the human. The theoretical and practical elements of developing small, integrated, intelligent zipper robots that interact with an application by using a lightweight MQTT protocol for application in the daily lives of diverse populations of people with physical challenges. The system functions as intelligent automatized garment to ensure users could positively utilize a zipper robot device to assist in putting on garments which also makes them feel comfortable wearing and interacting with the system. This research is an approach towards the “future of fashion”, and the goal is to incentivize and inspire others to develop new instances of wearable robots and sensors that help people with specific needs to live a better life.</div>