In order to succeed, it is imperative that all e-commerce systems include an effective and reliable trust and reputation modeling system. This is particularly true of decentralized e-commerce systems in which autonomous software engage in commercial transactions. Many researchers have sought to overcome the complexities of modeling a subjective, human concept like trust, resulting in several trust and reputation models. While these models each present a unique offering and solution to the problem, several issues persist. Most of the models require direct experience in the e-commerce system in order to make effective trust decisions. This leaves new agents and agents who only casually use the e-commerce system vulnerable. Additionally, the reputation ratings of agents who are relatively new to the system are often indistinguishable from scores for poorly performing agents. Finally, more tactics to defend against agents who exploit the characteristics of the open, distributed system for their own malicious needs are required. To address these issues, a new metric is devised and presented: seniority. Based on agent age and activity level within the e-commerce system, seniority provides a means of judging the credibility of other agents with little or no prior experience in the system. As the results of experimental analysis reveals, employing a reputation model that uses seniority provides considerable value to agents who are new agents, casual buyer agents and all other purchasing agents in the e-commerce system. This new metric therefore offers a significant contribution toward the development of enhanced and new trust and reputation models for deployment in real-world distributed e-commerce environments.

Natural disasters and extreme events are often characterized by their violence and unpredictability, resulting in consequences that in severe cases result in devastating physical and ecological damage as well as countless fatalities. In August 2005, Hurricane Katrina hit the Southern coast of the United States wielding serious weather and storm surges. The brunt of Katrina’s force was felt in Louisiana, where the hurricane has been estimated to total more than $108 billion in damage and over 1,800 casualties. Hurricane Rita followed Katrina in September 2005 and further contributed $12 billion in damage and 7 fatalities to the coastal communities of Louisiana and Texas. Prior to making landfall, residents of New Orleans received a voluntary, and then a mandatory, evacuation order in an attempt to encourage people to move themselves out of Hurricane Katrina’s predicted destructive path. Consistent with current practice in nearly all states, this evacuation order did not include or convey any information to individuals regarding route selection, shelter availability and assignment, or evacuation timing. This practice leaves the general population free to determine their own routes, destinations and evacuation times independently. Such freedom often results in inefficient and chaotic utilization of the roadways within an evacuation region, quickly creating bottlenecks along evacuation routes that can slow individual egress and lead to significant and potentially dangerous exposure of the evacuees to the impending storm. One way to assist the over-burdened and over-exposed population during extreme event evacuation is to provide an evacuation strategy that gives specific information on individual route selection, evacuation timing and shelter destination assignment derived from effective, strategic pre-planning. For this purpose, we present a mixed integer linear program to devise effective and controlled evacuation networks to be utilized during extreme event egress. To solve our proposed model, we develop a solution methodology based on Benders Decomposition and test its performance through an experimental design using the Central Texas region as our case study area. We show that our solution methods are efficient for large-scale instances of realistic size and that our methods surpass the size and computational limitations currently imposed by more traditional approaches such as branch-and-cut. To further test our model under conditions of uncertain individual choice/behavior, we create an agent-based simulation capable of modeling varying levels of evacuee compliance to the suggested optimal routes and varying degrees of communication between evacuees and between evacuees and the evacuation authority. By providing evacuees with information on when to evacuate, where to evacuate and how to get to their prescribed destination, we are able to observe significant cost and time increases for our case study evacuation scenarios while reducing the potential exposure of evacuees to the hurricane through more efficient network usage. We provide discussion on scenario performance and show the trade-offs and benefits of alternative batch-time evacuation strategies using global and individual effectiveness measures. Through these experiments and the developed methodology, we are able to further motivate the need for a more coordinated and informative approach to extreme event evacuation.

In order to succeed, it is imperative that all e-commerce systems include an effective and reliable trust and reputation modeling system. This is particularly true of decentralized e-commerce systems in which autonomous software engage in commercial transactions. Many researchers have sought to overcome the complexities of modeling a subjective, human concept like trust, resulting in several trust and reputation models. While these models each present a unique offering and solution to the problem, several issues persist. Most of the models require direct experience in the e-commerce system in order to make effective trust decisions. This leaves new agents and agents who only casually use the e-commerce system vulnerable. Additionally, the reputation ratings of agents who are relatively new to the system are often indistinguishable from scores for poorly performing agents. Finally, more tactics to defend against agents who exploit the characteristics of the open, distributed system for their own malicious needs are required. To address these issues, a new metric is devised and presented: seniority. Based on agent age and activity level within the e-commerce system, seniority provides a means of judging the credibility of other agents with little or no prior experience in the system. As the results of experimental analysis reveals, employing a reputation model that uses seniority provides considerable value to agents who are new agents, casual buyer agents and all other purchasing agents in the e-commerce system. This new metric therefore offers a significant contribution toward the development of enhanced and new trust and reputation models for deployment in real-world distributed e-commerce environments.

En els sistemes multiagent els models de confiança són una eina important perquè les interaccions entre agents siguin efectives. Ara bé, la confiança és una noció inherentment subjectiva, i per això els agents necessiten informació addicional per poder comunicar les avaluacions de confiança. Aquesta tesi doctoral se centra en dos mètodes per comunicar la confiança: l'alineament de la confiança i l'adaptació de la confiança. En el primer mètode, el problema de la comunicació de la confiança es modela com un problema d'alineament. Mostrem que les solucions actuals basades en ontologies comunes o en l'alineament d'ontologies generen problemes addicionals. Per això proposem com a alternativa alinear la confiança, basant-nos en les interaccions que dos agents comparteixen per tal d'aprendre un alineament. Fent servir el marc matemàtic de la teoria de canals formalitzem com les avaluacions subjectives de dos agents sobre la confiança es relacionen a través de les interaccions que fonamenten aquestes avaluacions. Com que els agents no poden accedir a les avaluacions de confiança dels altres, cal establir una comunicació. Especifiquem la rellevància i la consistència com a propietats necessàries per a aquesta comunicació. L'agent receptor de la confiança comunicada pot generalitzar els missatges fent servir la θ-subsumció, el que duu a un model predictiu que permet a un agent traduir futures comunicacions rebudes del mateix agent emissor. Mostrem aquest procés d'alineament a la pràctica fent servir TILDE, un algorisme de regressió de primer ordre, per tal d'aprendre un alineament. També il·lustrem la seva aplicació en un escenari d'exemple. De forma empírica demostrem: (1) que la dificultat d'aprendre un alineament depèn de la complexitat relativa dels diversos models de confiança; (2) que el nostre mètode millora altres mètodes existents d'alineament de la confiança; i (3) que el nostre mètode funciona bé sota condicions d'engany. El segon mètode per comunicar la confiança es basa en permetre que els agents raonin sobre llurs models de confiança i que personalitzin les comunicacions per adaptar-se millor a les necessitats d'un altre agent. Els mètodes actuals no permeten la suficient introspecció o adaptació del models de confiança. Per això presentem AdaptTrust, un mètode per incorporar un model computacional de confiança a l'arquitectura cognitiva d'un agent. En AdapTrust les creences i els objectius d'un agent influencien les prioritats entre aquells factors que són importants per a la computació de la confiança. Aquests al seu torn defineixen els valors dels paràmetres del model de confiança, i així l'agent pot dur a terme canvis en el seu model computacional de confiança a base de raonar sobre les seves creences i objectius. D'aquesta manera és capaç de modificar proactivament el seu model i produir avaluacions de confiança que millor s'adaptin a les seves necessitats actuals. Donem una formalització declarativa d'aquest sistema integrant-lo en una representació ⎯ fonamentada en un sistema multicontext ⎯ d'una arquitectura d'agent basada en creences, desitjos i intencions (BDI). També mostrem com amb el nostre marc es poden incorporar tres dels actuals models de confiança en el sistema de raonament d'un agent. Finalment fem servir AdapTrust en un marc d'argumentació que permet als agents construir una justificació per a llurs avaluacions de confiança. A través d'aquest marc els agents justifiquen les seves avaluacions segons unes prioritats entre factors, les quals al seu torn són justificades per les creences i objectius dels agents. Aquestes justificacions es poden comunicar a altres agents a través d'un diàleg formal. Així un agent, a base d'argumentar i raonar sobre les prioritats d'un altre agent, pot adaptar el seu model de confiança per oferir-li una recomanació de confiança personalitzada. Aquest sistema l'hem comprovat empíricament i hem vist que millora els actuals sistemes que permeten argumentar sobre avaluacions de confiança. / In open multi-agent systems, trust models are an important tool for agents to achieve effective interactions; however, trust is an inherently subjective concept, and thus for the agents to communicate about trust meaningfully, additional information is required. This thesis focuses on Trust Alignment and Trust Adaptation, two approaches for communicating about trust. The first approach is to model the problem of communicating trust as a problem of alignment. We show that currently proposed solutions, such as common ontologies or ontology alignment methods, lead to additional problems, and propose trust alignment as an alternative. We propose to use the interactions that two agents share as a basis for learning an alignment. We model this using the mathematical framework of Channel Theory, which allows us to formalise how two agents' subjective trust evaluations are related through the interactions that support them. Because the agents do not have access to each other's trust evaluations, they must communicate; we specify relevance and consistency, two necessary properties for this communication. The receiver of the communicated trust evaluations can generalise the messages using θ-subsumption, leading to a predictive model that allows an agent to translate future communications from the same sender. We demonstrate this alignment process in practice, using TILDE, a first-order regression algorithm, to learn an alignment and demonstrate its functioning in an example scenario. We find empirically that: (1) the difficulty of learning an alignment depends on the relative complexity of different trust models; (2) our method outperforms other methods for trust alignment; and (3) our alignment method deals well with deception. The second approach to communicating about trust is to allow agents to reason about their trust model and personalise communications to better suit the other agent's needs. Contemporary models do not allow for enough introspection into ⎯ or adaptation of ⎯ the trust model, so we present AdapTrust, a method for incorporating a computational trust model into the cognitive architecture of the agent. In AdapTrust, the agent's beliefs and goals influence the priorities between factors that are important to the trust calculation. These, in turn, define the values for parameters of the trust model, and the agent can effect changes in its computational trust model, by reasoning about its beliefs and goals. This way it can proactively change its model to produce trust evaluations that are better suited to its current needs. We give a declarative formalisation of this system by integrating it into a multi-context system representation of a beliefs-desires-intentions (BDI) agent architecture. We show that three contemporary trust models can be incorporated into an agent's reasoning system using our framework. Subsequently, we use AdapTrust in an argumentation framework that allows agents to create a justification for their trust evaluations. Agents justify their evaluations in terms of priorities between factors, which in turn are justified by their beliefs and goals. These justifications can be communicated to other agents in a formal dialogue, and by arguing and reasoning about other agents' priorities, goals and beliefs, the agent may adapt its trust model to provide a personalised trust recommendation for another agent. We test this system empirically and see that it performs better than the current state-of-the-art system for arguing about trust evaluations.

Ce travail de recherche est consacré à la formalisation et à la réalisation d'un processus de diagnostic pluridisplinaire. La particularité d'un tel diagnostic résulte du fait qu'il nécessite de nombreux spécialistes, chacun ayant des connaissances sur leur domaine. Le problème principal réside dans les interconnexions entre les domaines. Ces interconnexions peuvent ou non être connues et influer sur le diagnostic. Dans ce manuscrit, nous proposons de réaliser un diagnostic pluridisciplinaire l'aide d'un système multi-agents. Les agents élaborent un diagnostic local à un domaine puis, fusionnent leurs diagnostics afin d'obtenir le diagnostic pluridisciplinaire. Dans ce but, nous proposons un cadre d'argumentation et une méthode de fusion des diagnostics. Ensemble, ces deux propositions forment le modèle ANDi.

The technology for the realization of wireless sensors has been available for a long time, but due to progress  and  development  in  electrical  engineering  such  sensors  can  be  manufactured  cost effectively  and  in  large  numbers  nowadays.  This  availability  and  the  possibility  of  creating cooperating  wireless  networks  which  consist  of  such  sensors  nodes,  has  led  to  a  rapidly  growing popularity  of  a  technology  named  Wireless  Sensor  Networks  (WSN).  Its  disadvantage  is  a  high complexity in the task of programming applications based on WSN, which is a result of its distributed and  embedded  characteristic.  To  overcome  this  shortcoming,  software  agents  have  been  identified as  a  suitable  programming  paradigm.  The  agent  based  approach  commonly  uses  a  middleware  for the execution of the software agent. This thesis is meant to compare such agent middleware in their performance in the WSN domain. Therefore two prototypes of applications based on different agent models are implemented for a given set of middleware. After the implementation measurements are extracted  in  various  experiments,  which  give  information  about  the  runtime  performance  of  every middleware in the test set.  In the following analysis it is examined whether each middleware under test  is  suited  for  the  implemented  applications  in  WSN.  Thereupon,  the  results  are  discussed  and compared with the author’s expectations. Finally a short outlook of further possible development and improvements is presented.

This thesis focuses on deadbeat disturbance rejection for discrete-time linear multi-agent systems. The multi-agent systems, on which Spieser and Shams’ decentralized deadbeat output regulation problem is based, are extended by including disturbance agents. Specifically, we assume that there are one or more disturbance agents interacting with the plant agents in some known manner. The disturbance signals are assumed to be unmeasured and, for simplicity, constant. Control agents are introduced to interact with the plant agents, and each control agent is assigned a target plant agent. The goal is to drive the outputs of all plant agents to zero in finite time, despite the presence of the disturbances. In the decentralized deadbeat output regulation problem, two analysis schemes were introduced: targeting analysis, which is used to determine whether or not control laws can be found to regulate, not all the agents, but only the target agents; and growing analysis, which is used to determine the behaviour of all the non-target agents when the control laws are applied. In this thesis these two analyses are adopted to the deadbeat disturbance rejection problem. A new necessary condition for successful disturbance rejection is derived, namely that a control agent must be connected to the same plant agent to which a disturbance agent is connected. This result puts a bound on the minimum number of control agents and constraints the locations of control agents. Then, given the premise that both targeting and growing analyses succeed in the special case where the disturbances are all ignored, a new control approach is proposed for the linear case based on the idea of integral control and the regulation methods of Spieser and Shams. Preliminary studies show that this approach is also suitable for some nonlinear systems.

The multi-agent robotics paradigm has attracted much attention due to the variety of pertinent applications that are well-served by the use of a multiplicity of agents (including space robotics, search and rescue, and mobile sensor networks). The use of this paradigm for most applications, however, demands economical, lightweight agent designs for reasons of longer operational life, lower economic cost, faster and easily-verified designs, etc. An important contributing factor to an agent’s cost is its control architecture. Due to the emergence of novel implementation technologies carrying the promise of economical implementation, we consider the development of a technology-independent specification for computational machinery. To that end, the use of cybernetics toolsets (control and dynamical systems theory) is appropriate, enabling a principled specifi- cation of robotic control architectures in mathematical terms that could be mapped directly to diverse implementation substrates. This dissertation, hence, addresses the problem of developing a technologyindependent specification for lightweight control architectures to enable robotic agents to serve in a multi-agent scheme. We present the principled design of static and dynamical regulators that elicit useful behaviors, and integrate these within an overall architecture for both single and multi-agent control. Since the use of control theory can be limited in unstructured environments, a major focus of the work is on the engineering of emergent behavior. The proposed scheme is highly decentralized, requiring only local sensing and no inter-agent communication. Beyond several simulation-based studies, we provide experimental results for a two-agent system, based on a custom implementation employing field-programmable gate arrays.

The electric power systems in U.S. Navy ships supply energy to sophisticated systems for weapons, communications, navigation and operation. The reliability and survivability of the Shipboard Power System (SPS) are critical to the mission of a surface combatant ship, especially under battle conditions. In the event of battle, various weapons might attack a ship. When a weapon hits the ship it can cause severe damage to the electrical system on the ship. This damage can lead to de-energization of critical loads on a ship that can eventually decrease a ship’s ability to survive the attack. It is very important, therefore, to maintain availability of energy to the connected loads that keep the power systems operational. Technology exists that enables the detection of an incoming weapon and prediction of the geographic area where the incoming weapon will hit the ship. This information can then be used to take reconfiguration actions before the actual hit so that the actual damage caused by the weapon hit is reduced. The Power System Automation Lab (PSAL) has proposed a unique concept called "Predictive Reconfiguration" which refers to performing reconfiguration of a ship’s power system before a weapon hit to reduce the potential damage to the electrical system caused by the impending weapon hit. The concept also includes reconfiguring the electrical system to restore power to as much of the healthy system as possible after the weapon hit. This dissertation presents a new methodology for Predictive Reconfiguration of a Shipboard Power System (SPS). This probabilistic approach includes a method to assess the damage that will be caused by a weapon hit. This method calculates the expected probability of damage for each electrical component on the ship. Also a heuristic method is included, which uses the expected probability of damage to determine reconfiguration steps to reconfigure the ship’s electrical network to reduce the damage caused by a weapon hit. This dissertation also presents a modified approach for performing a reconfiguration for restoration after the weapon hits the system. In this modified approach, an expert system based restoration method restores power to loads de-energized due to the weapon hit. These de-energized loads are restored in a priority order. The methods were implemented using multi-agent technology. A test SPS model based on the electrical layout of a non-nuclear surface combatant ship was presented. Complex scenarios representing electrical casualties caused due to a weapon hit, on the test SPS model, were presented. The results of the Predictive Reconfiguration methodology for complex scenarios were presented to illustrate the effectiveness of the developed methodology.

In current distribution automations design, fault flags generated by overcurrent relays are used to detect the feeder fault section. With the integration of distributed generations (DG), fault currents could be contributed from different directions and jeopardize the fault detection function. A large fault current contributed by a DG flows from downstream of a feeder could be detected by the overcurrent relay and lead to the confusion in fault detection function. In this thesis, adjunction current measurements and fault flags are utilized to minimize the possibility of mis-identification of fault section. The structure and data flow of a Java agent development framework (JADE) is adopted for feeder fault detection, identification and service restoration (FDIR). Based on information from local measurements and other agents, the FDIR function can be better conducted by local agents. Test results indicate that multi-agent systems can be used to improve system reliability and reduce service interruption time.