The few past years have witnessed a huge leap in the field of the smart grid communication networks in which many theories are being developed, and many applications are being evolved to accommodate the implementation of the smart grid concepts. Distribution power systems are considered to be one of the first leading fields having the strong desire of applying the smart grid concepts; resulting in the emersion of the smart distribution power systems, which are the future visualization of the distribution systems having both the ability of smart acting, and the capabilities of automation, self-healing, and decentralized control. For the sake of the real implementation of the smart distribution power systems, the main functions performed by the traditional systems have to be performed by the new smart systems as well, taking into account the new features and properties of those smart systems. One of those main functions is the ability of power networks optimal reconfiguration to minimize the system’s power loss while preserving the system radial topology. The proposed reconfiguration methodology targets the utilization of a hybrid genetic algorithm with two fuzzy controllers that could converge to the global optimal network configuration with the fastest convergence rate consuming the least computational time. The first fuzzy controller is designed to reject any infeasible system configurations that might show up in the population of the genetic algorithm and violate the system radial topology, while the second fuzzy controller is designed to adapt the mutation rate of the genetic algorithm. Consequently, a novel multi-agent system is proposed and designed to perform the reconfiguration application in smart distribution power systems employing the concepts of distributed processing and decentralized control demanded by those systems. A multi-agent system employs a group of intelligent agents that have the capabilities of autonomy, reactivity, pro-activity, and sociality. Those agents cooperate with each other in order to perform a certain function through their powerful abilities to communicate, socialize, and make a common decision in a decentralized fashion based on the information retrieved from the surrounding environment and compiles with their ultimate objective.

The few past years have witnessed a huge leap in the field of the smart grid communication networks in which many theories are being developed, and many applications are being evolved to accommodate the implementation of the smart grid concepts. Distribution power systems are considered to be one of the first leading fields having the strong desire of applying the smart grid concepts; resulting in the emersion of the smart distribution power systems, which are the future visualization of the distribution systems having both the ability of smart acting, and the capabilities of automation, self-healing, and decentralized control. For the sake of the real implementation of the smart distribution power systems, the main functions performed by the traditional systems have to be performed by the new smart systems as well, taking into account the new features and properties of those smart systems. One of those main functions is the ability of power networks optimal reconfiguration to minimize the system’s power loss while preserving the system radial topology. The proposed reconfiguration methodology targets the utilization of a hybrid genetic algorithm with two fuzzy controllers that could converge to the global optimal network configuration with the fastest convergence rate consuming the least computational time. The first fuzzy controller is designed to reject any infeasible system configurations that might show up in the population of the genetic algorithm and violate the system radial topology, while the second fuzzy controller is designed to adapt the mutation rate of the genetic algorithm. Consequently, a novel multi-agent system is proposed and designed to perform the reconfiguration application in smart distribution power systems employing the concepts of distributed processing and decentralized control demanded by those systems. A multi-agent system employs a group of intelligent agents that have the capabilities of autonomy, reactivity, pro-activity, and sociality. Those agents cooperate with each other in order to perform a certain function through their powerful abilities to communicate, socialize, and make a common decision in a decentralized fashion based on the information retrieved from the surrounding environment and compiles with their ultimate objective.

The theory of Agency, specifically that developed by Jesen and Meckling (1976), will be the subject of examination. Agency theory has been the subject of extensive research since its introduction in modern form by Jensen and Meckling (1976). The generality of the theory of Agency appears unquestionable and it has been widely adopted. Surprisingly, however, the model correctly predicts particular phenomena under investigation in only the simplest of instances, and even in the simplest of instances there are cases where the simple agency model has limited success. Possible reasons for this failure may lie in the assumed universalist foundation and in the common formulation regarding agent behaviour, that all agents are self-interested rationalists seeking to maximise their own utility to the disregard of their principal's interest. While the hypothesis of self-interested rationalism may be apt in some contexts it may be misleading or inadequate in others. This is especially so when the narrow interpretations of self-interested rationalism are used. Human beings are more complex in their totality than can be represented in any parsimonious model. This is particularly a problem when model predictions are not empirically supported. Aspects omitted in a model may be a source of the misfit between prediction and observation. An extended conceptualisation and reformulation of agent behaviour is presented. An approach is developed that addresses the context of agent behaviour, the socio-environment within which the agent interacts. The context particularly refers to the institutional affiliations and interactions that influence agent behaviour through their belief structure (i.e., their Belief-Desire-Intention, BDI, model of rational action). Through the use of an institutional framework contextual analysis is incorporated into the theory of agency and ultimately agent behaviour. This agent is termed a socio-environmental rationalist agent (SERA) which is contrasted with the self-interested rationalist (SIR) agent in the existing agency literature. This research utilises an object-oriented approach to develop a simulation of the extended conceptualisation and reformulation of agent behaviour. Simulations investigate agent behaviours and outcomes at the micro (specifically through individualised SERA and SIR formulations) and macro (specifically through a multi-agent SERA community formulation in the context of the EU financial accounting harmonisation process) levels. Netlogo is the simulation tool through which this is attained. The simulation demonstrates how alternative formulations of rationality lead to different outcomes and these differences are evident at both levels. Importantly the extended model has outputs that are more in tune with current empirical evidence. The analysis thus demonstrates the plausibility of the extended conceptualisation and reformulation and the need to incorporate the context of behaviour more fully within the analysis of the principal-agent relationship. Through this extended examination of agent behaviour further theoretical and practical insights regarding the understanding of agent behaviour, the principal-agent problem and relationship, multi-agent communities, and of business and society in general may be attained. This dissertation provides one step in advancing our fundamental understanding of the principal-agent problem. The scope and power of agency analysis can be substantially extended using the approach and methods outlined, particularly beyond that present in existing Agency research.

Software systems involving autonomous interacting software entities (or agents) present new challenges in computer science and software engineering. A particularly challenging problem is the engineering of various forms of interaction among agents. Interaction may be aimed at enabling agents to coordinate their activities, cooperate to reach common objectives, or exchange resources to better achieve their individual objectives. This thesis is concerned with negotiation: a process through which multiple self-interested agents can reach agreement over the exchange of scarce resources. In particular, I focus on settings where agents have limited or uncertain information, precluding them from making optimal individual decisions. I demonstrate that this form of bounded-rationality may lead agents to sub-optimal negotiation agreements. I argue that rational dialogue based on the exchange of arguments can enable agents to overcome this problem. Since agents make decisions based on particular underlying reasons, namely their interests, beliefs and planning knowledge, then rational dialogue over these reasons can enable agents to refine their individual decisions and consequently reach better agreements. I refer to this form of interaction as “interested-based negotiation.” (For complete abstract open document)

A multi-agent system consists of a collection of agents that interact with each other to fulfil their tasks. Individual agents can have different motivations for engaging in interactions. Also, agents can possibly recognise the goals of the other participants in the interaction. To successfully interact, an agent should exhibit the ability to balance reactivity, pro-activeness (autonomy) and sociability. That is, individual agents should deliberate not only on what they themselves know about the working environment and their desires, but also on what they know about the beliefs and desires of the other agents in their group. Multi-agent systems have proven to be a useful tool for modelling and solving problems that exhibit complex and distributed structures. Examples include real-time traffic control and monitoring, work-flow management and information retrieval in computer networks. There are two broad challenges that the agent community is currently investigating. One is the development of the formalisms for representing the knowledge the agents have about their actions, goals, plans for achieving their goals and other agents. The second challenge is the development of the reasoning mechanisms agents use to achieve autonomy during the course of their interactions. Our research interests lie in a model for the interactions among the agents, whereby the behaviour of the individual agents can be specified in a declarative manner and these specifications can be made executable. Therefore, we investigate the methods that effectively represent the agents' knowledge about their working environment (which includes other agents), to derive unrealised information from the agents' knowledge by considering that the agents can obtain only a partial image of their working environment. The research also deals with the logical reasoning about the knowledge of the other agents to achieve a better interaction. Our approach is to apply the notions of modality and non-monotonic reasoning to formalise and to confront the problem of incomplete and conflicting information when modelling multi-agent systems. The approach maintains the richness in the description of the logical method while providing an efficient and easy-to-implement reasoning mechanism. In addition to the theoretical analysis, we investigate n-person argumentation as an application that benefits from the efficiency of our approach.

Research in mobile ad-hoc networks has focused on situations in whichnodes have no control over their movements. We investigate animportant but overlooked domain in which nodes do have controlover their movements. Reinforcement learning methods can be used tocontrol both packet routing decisions and node mobility, dramaticallyimproving the connectivity of the network. We first motivate theproblem by presenting theoretical bounds for the connectivityimprovement of partially mobile networks and then present superiorempirical results under a variety of different scenarios in which themobile nodes in our ad-hoc network are embedded with adaptive routingpolicies and learned movement policies.

Most Artificial Intelligence (AI) work can be characterized as either ``high-level'' (e.g., logical, symbolic) or ``low-level'' (e.g., connectionist networks, behavior-based robotics). Each approach suffers from particular drawbacks. High-level AI uses abstractions that often have no relation to the way real, biological brains work. Low-level AI, on the other hand, tends to lack the powerful abstractions that are needed to express complex structures and relationships. I have tried to combine the best features of both approaches, by building a set of programming abstractions defined in terms of simple, biologically plausible components. At the ``ground level'', I define a primitive, perceptron-like computational unit. I then show how more abstract computational units may be implemented in terms of the primitive units, and show the utility of the abstract units in sample networks. The new units make it possible to build networks using concepts such as long-term memories, short-term memories, and frames. As a demonstration of these abstractions, I have implemented a simulator for ``creatures'' controlled by a network of abstract units. The creatures exist in a simple 2D world, and exhibit behaviors such as catching mobile prey and sorting colored blocks into matching boxes. This program demonstrates that it is possible to build systems that can interact effectively with a dynamic physical environment, yet use symbolic representations to control aspects of their behavior.

MOTL/O embodies the MOTL paradigm and models organizational adaptation. We report progress on developing computational tools for systematically altering organizational components. This adds a novel dimension to MOTL (Hexmoor, et.al., 2008). This extension is necessary to allow communities of agents or robots to reconfigure their organizational structure in response to changes in the environment. Traditional approach of a hierarchical command and control (C2) structure is ineffective (Alberts & Hayes, 2003). Recently, an edge organization has been proposed as a more suitable alternative Command and control structure in the current information age, due to its empowerment of the edge members, better shared awareness among all the members in the organization, interoperability and most importantly, agility and adaptability to dynamic situations (Chang, 2005). We will explore principled mechanisms for converting a hierarchical organization to an edge type organization. Other than structural differences, organizations differ in information flow network and information sharing strategies. We move toward a solution for organizational adaptation. Beyond current project, many other types of organizational adaptation are possible and require much further research that we anticipate for our future work. This task will lay the foundation for automatic organizational adaptation. This report begins by outlining related work and background in section 2. In section 3 we

The problem of searching for objects of interest occurs in important applications ranging from rescue, security, transportation, to medicine. With the increasing use of autonomous vehicles as search platforms, there is a need for fast algorithms that can generate search plans for multiple agents in response to new information. In this dissertation, we develop new techniques for automated generation of search plans for different classes of search problems. First, we study the problem of searching for a stationary object in a discrete search space with multiple agents where each agent can access only a subset of the search space. In these problems, agents can fail to detect an object when inspecting a location. We show that when the probabilities of detection only depend on the locations, this problem can be reformulated as a minimum cost network optimization problem, and develop a fast specialized algorithm for the solution. We prove that our algorithm finds the optimal solution in finite time, and has worst-case computation performance that is faster than general minimum cost flow algorithms. We then generalize it to the case where the probabilities of detection depend on the agents and the locations, and propose a greedy algorithm that is 1/2-approximate. Second, we study the problem of searching for a moving object in a discrete search space with multiple agents where each agent can access only a subset of a discrete search space at any time and agents can fail to detect objects when searching a location at a given time. We provide necessary conditions for an optimal search plan, extending prior results in search theory. For the case where the probabilities of detection depend on the locations and the time periods, we develop a forward-backward iterative algorithm based on coordinate descent techniques to obtain solutions. To avoid local optimum, we derive a convex relaxation of the dynamic search problem and show this can be solved optimally using coordinate descent techniques. The solutions of the relaxed problem are used to provide random starting conditions for the iterative algorithm. We also address the problem where the probabilities of detection depend on the agents as well as the locations and the time periods, and show that a greedy-style algorithm is 1/2-approximate. Third, we study problems when multiple objects of interest being searched are physically scattered among locations on a graph and the agents are subject to motion constraints captured by the graph edges as well as budget constraints. We model such problem as an orienteering problem, when searching with a single agent, or a team orienteering problem, when searching with multiple agents. We develop novel real-time efficient algorithms for both problems. Fourth, we investigate classes of continuous-region multi-agent adaptive search problems as stochastic control problems with imperfect information. We allow the agent measurement errors to be either correlated or independent across agents. The structure of these problems, with objectives related to information entropy, allows for a complete characterization of the optimal strategies and the optimal cost. We derive a lower bound on the performance of the minimum mean-square error estimator, and provide upper bounds on the estimation error for special cases. For agents with independent errors, we show that the optimal sensing strategies can be obtained in terms of the solution of decoupled scalar convex optimization problems, followed by a joint region selection procedure. We further consider search of multiple objects and provide an explicit construction for adaptively determining the sensing actions.

Cette thèse s'intéresse a l'apprentissage du comportement des avions dans le ciel. À partir de ces comportements l'objectif est de pouvoir générer du trafic aérien de manière autonome, légère et flexible pour alimenter une simulation. Les méthodes actuelles de simulation aériennes demandent beaucoup de préparation avant la simulation pour concevoir le scénario et d'interventions humaines pendant la simulation pour que le trafic aérien soit réaliste. Générer du trafic est une tâche complexe car le comportement des avions dépend de beaucoup de variables et des décisions de plusieurs d'acteurs : le contrôleur aérien décide de la trajectoire à suivre parmi toutes les possibilités qu'il perçoit, puis le pilote réagit plus ou moins rapidement de façon plus ou moins strict. Un système multi-agent adaptatif observe des trajectoires d'avions réelles pour apprendre comment les avions se comportent dans la réalité. Les différents agents impliquées coopèrent et modifient les liens qui les relient. Ce réseau entre les agents fini par représenter le comportement global de l'ensemble des avions et peut être interrogé par des agents avions en simulation pour savoir ce qu'ils doivent faire en fonction de leur situation courante. Nous présentons EVAA (Environnement Virtuel Auto-Adaptatif) capable d'apprendre le comportement des avions et de générer du trafic en fonction de ces comportements de manière totalement autonome. / This thesis is about learning the behavior of the aircrafts in the sky. With those behaviors the goal is to generate traffic in an autonomous and flexible way into a simulation. The current methods of air traffic simulation need to prepare the scenario before the simulation and the interventions of humans during the simulation to make the traffic realistic. Traffic generation is a complex task because the behaviors of the planes depends on many variables and several actors : the air traffic controller decide what trajectory to follow among many possibilities, then the pilot react , more or less promptly, to this order in a, more or less rigorous, way. An adaptive multi-agent system monitors trajectories of real aircrafts to learn how the planes behave in the real sky. The agents involved in this process cooperate and update the links between them to create a network representing the global behavior of all aircrafts. This network can then be queried by an aircraft agent in a simulation to know what it should do according to its current situation. We present EVAA (Self-Adaptive Virtual Environment) able to learn the behavior of aircrafts and to generate air traffic by using those behaviors in a autonomous way.