This thesis work is in the area of modeling trust in multi-agent systems, systems of software agents designed to act on behalf of users (buyers and sellers), in applications such as e-commerce. The focus is on developing an approach for buyers to model the trustworthiness of sellers in order to make effective decisions about which sellers to select for business. One challenge is the problem of unfair ratings, which arises when modeling the trust of sellers relies on ratings provided by other buyers (called advisors). Existing approaches for coping with this problem fail in scenarios where the majority of advisors are dishonest, buyers do not have much personal experience with sellers, advisors try to flood the trust modeling system with unfair ratings, and sellers vary their behavior widely. We propose a novel personalized approach for effectively modeling trustworthiness of advisors, allowing a buyer to 1) model the private reputation of an advisor based on their ratings for commonly rated sellers 2) model the public reputation of the advisor based on all ratings for the sellers ever rated by that agent 3) flexibly weight the private and public reputation into one combined measure of the trustworthiness of the advisor. Our approach tracks ratings provided according to their time windows and limits the ratings accepted, in order to cope with advisors flooding the system and to deal with changes in agents' behavior. Experimental evidence demonstrates that our model outperforms other models in detecting dishonest advisors and is able to assist buyers to gain the largest profit when doing business with sellers. Equipped with this richer method for modeling trustworthiness of advisors, we then embed this reasoning into a novel trust-based incentive mechanism to encourage agents to be honest. In this mechanism, buyers select the most trustworthy advisors as their neighbors from which they can ask advice about sellers, forming a social network. In contrast with other researchers, we also have sellers model the reputation of buyers. Sellers will offer better rewards to satisfy buyers that are well respected in the social network, in order to build their own reputation. We provide precise formulae used by sellers when reasoning about immediate and future profit to determine their bidding behavior and the rewards to buyers, and emphasize the importance for buyers to adopt a strategy to limit the number of sellers that are considered for each good to be purchased. We theoretically prove that our mechanism promotes honesty from buyers in reporting seller ratings, and honesty from sellers in delivering products as promised. We also provide a series of experimental results in a simulated dynamic environment where agents may be arriving and departing. This provides a stronger defense of the mechanism as one that is robust to important conditions in the marketplace. Our experiments clearly show the gains in profit enjoyed by both honest sellers and honest buyers when our mechanism is introduced and our proposed strategies are followed. In general, our research will serve to promote honesty amongst buyers and sellers in e-marketplaces. Our particular proposal of allowing sellers to model buyers opens a new direction in trust modeling research. The novel direction of designing an incentive mechanism based on trust modeling and using this mechanism to further help trust modeling by diminishing the problem of unfair ratings will hope to bridge researchers in the areas of trust modeling and mechanism design.

This thesis work is in the area of modeling trust in multi-agent systems, systems of software agents designed to act on behalf of users (buyers and sellers), in applications such as e-commerce. The focus is on developing an approach for buyers to model the trustworthiness of sellers in order to make effective decisions about which sellers to select for business. One challenge is the problem of unfair ratings, which arises when modeling the trust of sellers relies on ratings provided by other buyers (called advisors). Existing approaches for coping with this problem fail in scenarios where the majority of advisors are dishonest, buyers do not have much personal experience with sellers, advisors try to flood the trust modeling system with unfair ratings, and sellers vary their behavior widely. We propose a novel personalized approach for effectively modeling trustworthiness of advisors, allowing a buyer to 1) model the private reputation of an advisor based on their ratings for commonly rated sellers 2) model the public reputation of the advisor based on all ratings for the sellers ever rated by that agent 3) flexibly weight the private and public reputation into one combined measure of the trustworthiness of the advisor. Our approach tracks ratings provided according to their time windows and limits the ratings accepted, in order to cope with advisors flooding the system and to deal with changes in agents' behavior. Experimental evidence demonstrates that our model outperforms other models in detecting dishonest advisors and is able to assist buyers to gain the largest profit when doing business with sellers. Equipped with this richer method for modeling trustworthiness of advisors, we then embed this reasoning into a novel trust-based incentive mechanism to encourage agents to be honest. In this mechanism, buyers select the most trustworthy advisors as their neighbors from which they can ask advice about sellers, forming a social network. In contrast with other researchers, we also have sellers model the reputation of buyers. Sellers will offer better rewards to satisfy buyers that are well respected in the social network, in order to build their own reputation. We provide precise formulae used by sellers when reasoning about immediate and future profit to determine their bidding behavior and the rewards to buyers, and emphasize the importance for buyers to adopt a strategy to limit the number of sellers that are considered for each good to be purchased. We theoretically prove that our mechanism promotes honesty from buyers in reporting seller ratings, and honesty from sellers in delivering products as promised. We also provide a series of experimental results in a simulated dynamic environment where agents may be arriving and departing. This provides a stronger defense of the mechanism as one that is robust to important conditions in the marketplace. Our experiments clearly show the gains in profit enjoyed by both honest sellers and honest buyers when our mechanism is introduced and our proposed strategies are followed. In general, our research will serve to promote honesty amongst buyers and sellers in e-marketplaces. Our particular proposal of allowing sellers to model buyers opens a new direction in trust modeling research. The novel direction of designing an incentive mechanism based on trust modeling and using this mechanism to further help trust modeling by diminishing the problem of unfair ratings will hope to bridge researchers in the areas of trust modeling and mechanism design.

The purpose of this thesis is to develop a testing environment for mobile robot experiments, to examine methods for multi-robot platooning through hostile environments, and test these algorithms on mobile robots. Such a system will allow us to rapidly address and test problems that arise concerning robot swarms and consequent interactions. In order to create this hardware simulation environment a test bed will be created using ROS or Robot Operating System. This platform is highly modular and extensible for future development. Trajectory generation for the robots will use smoothing splines, B-splines, and A* search. Each method has distinct properties which will be analyzed and rated with respect to its effectiveness with regards to robotic platooning. A few issues to be considered include: Is the optimal path taken with respect to distance and threats? Is the formation of the robots maintained or compromised during traversal of the path? And finally, what sorts of compromises or additions are needed to make each method effective? This work will be helpful for choosing route planning methods in future work and will provide a large code base for rapid prototyping.

In this thesis, we address the challenge of information overload in online participatory messaging environments using an artificial intelligence approach drawn from research in multiagent systems trust modeling. In particular, we reason about which messages to show to users based on modeling both credibility and similarity, motivated by a need to discriminate between (false) popular and truly beneficial messages. Our work focuses on environments wherein users' ratings on messages reveal their preferences and where the trustworthiness of those ratings then needs to be modeled, in order to make effective recommendations. We first present one solution, CredTrust, and demonstrate its efficacy in comparison with LOAR --- an established trust-based recommender system applicable to participatory media networks which fails to incorporate the modeling of credibility. Validation for our framework is provided through the simulation of an environment where the ground truth of the benefit of a message to a user is known. We are able to show that our approach performs well in terms of successfully recommending those messages with high predicted benefit and avoiding those messages with low predicted benefit. We continue by developing a new model for making recommendations that is grounded in Bayesian statistics and uses Partially Observable Markov Decision Processes (POMDPs). This model is an important next step, as both CredTrust and LOAR encode particular functions of user features (viz., similarity and credibility) when making recommendations; our new model, denoted POMDPTrust, learns the appropriate evaluation functions in order to make ``correct" belief updates about the usefulness of messages. We validate our new approach in simulation, showing that it outperforms both LOAR and CredTrust in a variety of agent scenarios. Furthermore, we demonstrate how POMDPTrust performs well against real world data sets from Reddit.com and Epinions.com. In all, we offer a novel trust model which is shown, through simulation and real-world experimentation, to be an effective agent-based solution to the problem of managing the messages posted by users in participatory media networks.

In the field of multiagent systems, trust and reputation systems are intended to assist agents in finding trustworthy partners with whom to interact. Earlier work of ours identified in theory a number of security vulnerabilities in trust and reputation systems, weaknesses that might be exploited by malicious agents to bypass the protections offered by such systems. In this work, we begin by developing the TREET testbed, a simulation platform that allows for extensive evaluation and flexible experimentation with trust and reputation technologies. We use this testbed to experimentally validate the practicality and gravity of attacks against vulnerabilities. Of particular interest are attacks that are collusive in nature: groups of agents (coalitions) working together to improve their expected rewards. But the issue of coalitions is not unique to trust and reputation; rather, it cuts across a range of fields in multiagent systems and beyond. In some scenarios, coalitions may be unwanted or forbidden; in others they may be benign or even desirable. In this document, we propose a method for detecting coalitions and identifying coalition members, a capability that is likely to be valuable in many of the diverse fields where coalitions may be of interest. Our method makes use of clustering in benefit space (a high-dimensional space reflecting how agents benefit others in the system) in order to identify groups of agents who benefit similar sets of agents. A statistical technique is then used to identify which clusters contain coalitions. Experimentation using the TREET platform verifies the effectiveness of this approach. A series of enhancements to our method are also introduced, which improve the accuracy and robustness of the algorithm. To demonstrate how this broadly-applicable tool can be used to address domain-specific problems, we focus again on trust and reputation systems. We show how, by incorporating our work into one such system (the existing Beta Reputation System), we can provide resistance to collusion. We conclude with a detailed discussion of the value of our work for a wide range of environments, including a variety of multiagent systems and real-world settings.

This work presents a framework by which a massive multiagent organization can be controlled and modified without resorting to micromanagement and without needing advanced knowledge of potentially complex organizations. In addition to their designated duties, agents in the proposed framework perform some method of determining optimal traits such as configurations, plans, knowledge bases and so forth. Traits follow survival of the fittest rules in which more successful traits overpower less successful ones. Subproblem partitions develop emergently as successful solutions are disseminated to and aggregated by unsuccessful agents. Provisions are provided to allow the administrator to guide the search process by injecting solutions known to work for a particular agent. The performance of the framework is evaluated via comparison to individual state-space search.

Efforts towards a low carbon economy are challenging the electricity industry. On the supply-side, centralised carbon-intensive power plants are set to gradually decrease their contribution to the generation mix, whilst distributed renewable generation is to successively increase its share. On the demand-side, electricity use is expected to increase in the future due to the electrification of heating and transport. Moreover, the demand-side is to become more active allowing end-users to invest in generation and storage technologies, such as solar photovoltaics (PV) and home batteries. As a result, some network reinforcements might be needed and instrumentation at the users’ end is to be required, such as controllers and home energy management systems (HEMS). The electricity grid must balance supply and demand at all times in order to maintain technical constraints of frequency, voltage, and current; and this will become more challenging as a result of this transition. Failure to meet these constraints compromises the service and could damage the power grid assets and end-users’ appliances. Balancing generation, although responsive, is carbon-intensive and associated with inefficient asset utilisation, as these generators are mostly used during peak hours and sit idle the rest of the time. Furthermore, energy storage is a potential solution to assist the balancing problem in the presence of non-dispatchable low-carbon generators; however, it is substantially expensive to store energy in large amounts. Therefore, demand response (DR) has been envisioned as a complementary solution to increase the system’s resilience to weather-dependent, stochastic, and intermittent generation along with variable and temperature-correlated electric load. In the domestic setting, operational flexibility of some appliances, such as heaters and electric cars, can be coordinated amongst several households so as to help balance supply and demand, and reduce the need of balancing generators. Against this background, the electricity supply system requires new organisational paradigms that integrate DR effectively. Although some dynamic pricing schemes have been proposed to guide DR, such as time of use (ToU) and real-time pricing (RTP), it is still unclear how to control oscillatory massive responses (e.g., large fleet of electric cars simultaneously responding to a favourable price). Hence, this thesis proposes an alternative approach in which households proactively submit DR offers that express their preferences to their respective retailer in exchange for a discount. This research develops a computational model of domestic electricity use, and simulates appliances with operational flexibility in order to evaluate the effects and benefits of DR for both retailers and households. It provides a representation for this flexibility so that it can be integrated into specific DR offers. Retailers and households are modelled as computational agents. Furthermore, two market-based mechanisms are proposed to determine the allocation of DR offers. More specifically, a one-sided Vickrey-Clarke-Groves (VCG)-based mechanism and penalty schemes were designed for electricity retailers to coordinate their customers’ DR efforts so as to ameliorate the imbalance of their trading schedules. Similarly, a two-sided McAfee-based mechanism was designed to integrate DR offers into a multi-retailer setting in order to reduce zonal imbalances. A suitable method was developed to construct DR block offers that could be traded amongst retailers. Both mechanisms are dominant-strategy incentive-compatible and trade off a small amount of economic efficiency in order to maintain individual rationality, truthful reporting, weak budget balance and tractable computation. Moreover, privacy preserving is achieved by including computational agents from the independent system operator (ISO) as intermediaries between each retailer and its domestic customers, and amongst retailers. The theoretical properties of these mechanisms were proved using worst-case analysis, and their economic effects were evaluated in simulations based on data from a survey of UK household electricity use. In addition, forecasting methods were assessed on the end-users’ side in order to make better DR offers and avoid penalties. The results show that, under reasonable assumptions, the proposed coordination mechanisms achieve significant savings for both end-users and retailers, as they reduce the required amount of expensive balancing generation.

Ao tentar agendar uma atividade que dependa da presença de outras pessoas, geralmente acabamos desperdiçando tempo precioso avaliando os possíveis horários e verificando se os mesmos são aceitos por todos envolvidos. Embora a modelagem e a resolução do problema de agendamento multiagente pareçam estar completamente entendidas e ainda diversos algoritmos possam ser encontrados na literatura, uma questão ainda existe: Como definir horários compatíveis para uma atividade compartilhada sem que os usuários tenham que manualmente escolher horários livres de seus calendários até que todos envolvidos aceitem um horário. A principal contribuição é um algoritmo chamado Descobridor Asíncrono de Horários (ATF) baseado no Rastreamento Asíncrono (ABT) que permite que aplicações encontrem horários compatíveis para atividades compartilhadas requerendo mínima intervenção manual dos usuários. Esta dissertação revisita o Problema Temporal Simples (STP) e a sua versão multiagente (MaSTP), demonstra como eles podem ser utilizados para resolver o problema de agentamentos e ao final apresenta o ATF, a avaliação experimental e a análise de complexidade. / In order to schedule an activity that depends on other people, we very often end up wasting precious time trying to find compatible times and evaluating if they are accepted by all involved. Even though modeling and solving multiagent scheduling problems seem completely understood and several algorithms can be found in the literature, one limitation still stands up: How to find a compatible time slot for an activity shared by many users without requiring the users themselves to spend time going through their calendar and choosing time slots until everybody agrees. The main contribution of this work is an algorithm called Asynchronous Time Finder (ATF) based on the Asynchronous Backtracking (ABT) that enables applications to find compatible times when scheduling shared activities among several users while requiring minimal user interaction. This dissertation starts by revisiting the Simple Temporal Problem (STP) and its multiagent version (MaSTP), it then shows how they can be used to solve the problem of managing agendas and then finally it presents the ATF giving an experimental evaluation and the analysis of its complexity.

Ao tentar agendar uma atividade que dependa da presença de outras pessoas, geralmente acabamos desperdiçando tempo precioso avaliando os possíveis horários e verificando se os mesmos são aceitos por todos envolvidos. Embora a modelagem e a resolução do problema de agendamento multiagente pareçam estar completamente entendidas e ainda diversos algoritmos possam ser encontrados na literatura, uma questão ainda existe: Como definir horários compatíveis para uma atividade compartilhada sem que os usuários tenham que manualmente escolher horários livres de seus calendários até que todos envolvidos aceitem um horário. A principal contribuição é um algoritmo chamado Descobridor Asíncrono de Horários (ATF) baseado no Rastreamento Asíncrono (ABT) que permite que aplicações encontrem horários compatíveis para atividades compartilhadas requerendo mínima intervenção manual dos usuários. Esta dissertação revisita o Problema Temporal Simples (STP) e a sua versão multiagente (MaSTP), demonstra como eles podem ser utilizados para resolver o problema de agentamentos e ao final apresenta o ATF, a avaliação experimental e a análise de complexidade. / In order to schedule an activity that depends on other people, we very often end up wasting precious time trying to find compatible times and evaluating if they are accepted by all involved. Even though modeling and solving multiagent scheduling problems seem completely understood and several algorithms can be found in the literature, one limitation still stands up: How to find a compatible time slot for an activity shared by many users without requiring the users themselves to spend time going through their calendar and choosing time slots until everybody agrees. The main contribution of this work is an algorithm called Asynchronous Time Finder (ATF) based on the Asynchronous Backtracking (ABT) that enables applications to find compatible times when scheduling shared activities among several users while requiring minimal user interaction. This dissertation starts by revisiting the Simple Temporal Problem (STP) and its multiagent version (MaSTP), it then shows how they can be used to solve the problem of managing agendas and then finally it presents the ATF giving an experimental evaluation and the analysis of its complexity.

Ao tentar agendar uma atividade que dependa da presença de outras pessoas, geralmente acabamos desperdiçando tempo precioso avaliando os possíveis horários e verificando se os mesmos são aceitos por todos envolvidos. Embora a modelagem e a resolução do problema de agendamento multiagente pareçam estar completamente entendidas e ainda diversos algoritmos possam ser encontrados na literatura, uma questão ainda existe: Como definir horários compatíveis para uma atividade compartilhada sem que os usuários tenham que manualmente escolher horários livres de seus calendários até que todos envolvidos aceitem um horário. A principal contribuição é um algoritmo chamado Descobridor Asíncrono de Horários (ATF) baseado no Rastreamento Asíncrono (ABT) que permite que aplicações encontrem horários compatíveis para atividades compartilhadas requerendo mínima intervenção manual dos usuários. Esta dissertação revisita o Problema Temporal Simples (STP) e a sua versão multiagente (MaSTP), demonstra como eles podem ser utilizados para resolver o problema de agentamentos e ao final apresenta o ATF, a avaliação experimental e a análise de complexidade. / In order to schedule an activity that depends on other people, we very often end up wasting precious time trying to find compatible times and evaluating if they are accepted by all involved. Even though modeling and solving multiagent scheduling problems seem completely understood and several algorithms can be found in the literature, one limitation still stands up: How to find a compatible time slot for an activity shared by many users without requiring the users themselves to spend time going through their calendar and choosing time slots until everybody agrees. The main contribution of this work is an algorithm called Asynchronous Time Finder (ATF) based on the Asynchronous Backtracking (ABT) that enables applications to find compatible times when scheduling shared activities among several users while requiring minimal user interaction. This dissertation starts by revisiting the Simple Temporal Problem (STP) and its multiagent version (MaSTP), it then shows how they can be used to solve the problem of managing agendas and then finally it presents the ATF giving an experimental evaluation and the analysis of its complexity.