TruPercept: Trust Modelling for Autonomous Vehicle Cooperative Perception from Synthetic Data

Inter-vehicle communication for autonomous vehicles (AVs) stands to provide significant benefits in terms of perception robustness. We propose a novel approach for AVs to communicate perceptual observations, tempered by trust modelling of peers providing reports. Based on the accuracy of reported object detections as verified locally, communicated messages can be fused to augment perception performance beyond line of sight and at great distance from the ego vehicle. Also presented is a new synthetic dataset which can be used to test cooperative perception. The TruPercept dataset includes unreliable and malicious behaviour scenarios to experiment with some challenges cooperative perception introduces. The TruPercept runtime and evaluation framework allows modular component replacement to facilitate ablation studies as well as the creation of new trust scenarios we are able to show

The Role of Trust in Distributed Design

Automated support of design teams, consisting of both human and automated systems, requires an understanding of the role of trust in distributed design processes. By explicating trust, an individual designer's decisions become better understood and may be better supported. Each individual designer has his or her private goals in a cooperative design setting, in which requirement conflicts and resource competitions abound. However, there are group goals that also need to be reached. This paper presents an overview of research related to trust in the context of agents and design, a computational knowledge-level model of trust based on the seven beliefs distinguished by Castelfranchi and Falcone, and an example of the use of the trust model in a specific design process, namely, Website design from the perspective of a single designer. The results are discussed in the context of distributed design in open systems. Copyright © 2004 Cambridge University Press

Proceedings of the 2nd International Workshop on Security in Mobile Multiagent Systems

This report contains the Proceedings of the Second Workshop on Security on Security of Mobile Multiagent Systems (SEMAS2002). The Workshop was held in Montreal, Canada as a satellite event to the 5th International Conference on Autonomous Agents in 2001. The far reaching influence of the Internet has resulted in an increased interest in agent technologies, which are poised to play a key role in the implementation of successful Internet and WWW-based applications in the future. While there is still considerable hype concerning agent technologies, there is also an increasing awareness of the problems involved. In particular, that these applications will not be successful unless security issues can be adequately handled. Although there is a large body of work on cryptographic techniques that provide basic building-blocks to solve specific security problems, relatively little work has been done in investigating security in the multiagent system context. Related problems are secure communication between agents, implementation of trust models/authentication procedures or even reflections of agents on security mechanisms. The introduction of mobile software agents significantly increases the risks involved in Internet and WWW-based applications. For example, if we allow agents to enter our hosts or private networks, we must offer the agents a platform so that they can execute correctly but at the same time ensure that they will not have deleterious effects on our hosts or any other agents / processes in our network. If we send out mobile agents, we should also be able to provide guarantees about specific aspects of their behaviour, i.e., we are not only interested in whether the agents carry out-out their intended task correctly. They must defend themselves against attacks initiated by other agents, and survive in potentially malicious environments. Agent technologies can also be used to support network security. For example in the context of intrusion detection, intelligent guardian agents may be used to analyse the behaviour of agents on a firewall or intelligent monitoring agents can be used to analyse the behaviour of agents migrating through a network. Part of the inspiration for such multi-agent systems comes from primitive animal behaviour, such as that of guardian ants protecting their hill or from biological immune systems

Reputation based Buyer Strategies for Seller Selection in Electronic Markets

Reputation based adaptive buying agents that reason about sellers for purchase decisions have been designed for B2C ecommerce markets. Previous research in the area of buyer agent strategies for choosing seller agents in ecommerce markets has focused on frequent purchases. In this thesis, we present reputation based strategies for buyer agents to choose seller agents in a decentralized multi agent based ecommerce markets for frequent as well as infrequent purchases. We consider a marketplace where the behavior of seller agents and buyer agents can vary, they can enter and leave the market any time, they may be dishonest, and quality of the product can be gauged after actually receiving the product. Buyer agents exchange seller agents' information, which is based on their own experiences, with other buyer agents in the market. However, there is no guarantee that when other buyer agents provide information, they are truthful or share similar opinions. First we present a method for buyer agent to model a seller agent's reputation. The buyer agent computes a seller agent's reputation based on its ability to meet its expectations of product quality and price as compared to its competitors. We show that a buying agent acting alone, utilizing our model of maintaining seller agents' reputation and buying strategy does better than buying agents acting alone employing strategies proposed previously by other researchers for frequent as well as for infrequent purchases. Next we present two methods for buyer agents to identify other trustworthy buyer agent friends who are honest and have similar opinions regarding seller agents, based on sharing of seller agents' information with each other. In the first method, buyer agent utilizes other buyer agents' opinions and ratings of seller agents to identify trustworthy buyer agent friends. Reputation of seller agents provided by trustworthy buyer agent friends is adjusted to account for the differences in the rating systems and combined with its own information on seller agents to choose high quality, low priced seller agent. In the second method, buyer agent only utilizes other buyer agents' opinions of seller agents to identify trustworthy buyer agent friends. Ratings are assigned to seller agents by the buyer agent based on trustworthy friend buyer agents' opinions and combined with its own rating on seller agents to choose a high quality, low priced seller agent to purchase from. We conducted experiments to show that both methods are successful in distinguishing between trustworthy buyer agent friends, whose opinions should be utilized in decision making, and untrustworthy buyer agent friends who are either dishonest, or have different opinions. We also show that buyer agents using our models of identifying trustworthy buyer agent friends have higher performance than a buyer agent acting alone for infrequent purchases and for increasing numbers of sellers in the market. Finally we analyze the performances of buyer agents with risk taking and conservative attitudes. A buyer agent with risk taking attitude considers a new seller agent as reputable initially and tends to purchase from a new seller agent if they are offering the lowest price among reputable seller agents. A buyer agent with conservative attitude is cautious in its approach and explores new seller agents at a rate proportional to the ratio of unexplored seller agents to the all the seller agents who have sent bids. Our results show that, when buyer agents are making decisions based on their own information, a buyer agent with conservative attitude has the best performance. When buyer agents are utilizing information provided by their trusted friends, a buyer agent with risk taking attitude and using only trusted friend buyer agents' opinions of seller agents has the best performance. In summary, the main contributions of this dissertation are: 1.A new reputation based way to model seller agents by buyer agents based on direct interactions. 2.A protocol to exchange reputation information about seller agents with other buyer agent friends based on the friends' direct interaction with seller agents. 3.Two methods of identifying trustworthy buyer agent friends who are honest and share similar opinions, and utilizing the information provided by them to maximize a buyer agent's chances of choosing a high quality, low priced seller agent to purchase from

An Architectural Description Language for Secure Multi-Agent Systems

Multi-Agent Systems (MAS) architectures are gaining popularity for building open, distributed, and evolving information systems. Unfortunately, despite considerable work in the fields of software architecture and MAS during the last decade, few research efforts have aimed at defining languages for designing and formalising secure agent architectures. This paper proposes a novel Architectural Description Language (ADL) for describing Belief-Desire-Intention (BDI) secure MAS. We specify each element of our ADL using the Z specification language and we employ two example case studies: one to assist us in the description of the proposed language and help readers of the article to better understand the fundamentals of the language; and one to demonstrate its applicability

Local and Cooperative Autonomous Vehicle Perception from Synthetic Datasets

The purpose of this work is to increase the performance of autonomous vehicle 3D object detection using synthetic data. This work introduces the Precise Synthetic Image and LiDAR (PreSIL) dataset for autonomous vehicle perception. Grand Theft Auto V (GTA V), a commercial video game, has a large, detailed world with realistic graphics, which provides a diverse data collection environment. Existing works creating synthetic Light Detection and Ranging (LiDAR) data for autonomous driving with GTA V have not released their datasets, rely on an in-game raycasting function which represents people as cylinders, and can fail to capture vehicles past 30 metres. This work describes a novel LiDAR simulator within GTA V which collides with detailed models for all entities no matter the type or position. The PreSIL dataset consists of over 50,000 frames and includes high-definition images with full resolution depth information, semantic segmentation (images), point-wise segmentation (point clouds), and detailed annotations for all vehicles and people. Collecting additional data with the PreSIL framework is entirely automatic and requires no human intervention of any kind. The effectiveness of the PreSIL dataset is demonstrated by showing an improvement of up to 5% average precision on the KITTI 3D Object Detection benchmark challenge when state-of-the-art 3D object detection networks are pre-trained with the PreSIL dataset. The PreSIL dataset and generation code are available at https://tinyurl.com/y3tb9sxy Synthetic data also enables data generation which is genuinely hard to create in the real world. In the next major chapter of this thesis, a new synthethic dataset, the TruPercept dataset, is created with perceptual information from multiple viewpoints. A novel system is proposed for cooperative perception, perception including information from multiple viewpoints. The TruPercept model is presented. TruPercept integrates trust modelling for vehicular ad hoc networks (VANETs) with information from perception, with a focus on 3D object detection. A discussion is presented on how this might create a safer driving experience for fully autonomous vehicles. The TruPercept dataset is used to experimentally evaluate the TruPercept model against traditional local perception (single viewpoint) models. The TruPercept model is also contrasted with existing methods for trust modeling used in ad hoc network environments. This thesis also offers insights into how V2V communication for perception can be managed through trust modeling, aiming to improve object detection accuracy, across contexts with varying ease of observability. The TruPercept model and data are available at https://tinyurl.com/y2nwy52

Credibilidade e reputação em agentes inteligentes. Aplicação ao comércio electrónico

Desde o seu aparecimento, a Internet teve um desenvolvimento e uma taxa de crescimento quase exponencial. Os mercados de comércio electrónico têm vindo a acompanhar esta tendência de crescimento, tornando-se cada vez mais comuns e populares entre comerciantes ou compradores/vendedores de ocasião. A par deste crescimento também foi aumentando a complexidade e sofisticação dos sistemas responsáveis por promover os diferentes mercados. No seguimento desta evolução surgiram os Agentes Inteligentes devido à sua capacidade de encontrar e escolher, de uma forma relativamente eficiente, o melhor negócio, tendo por base as propostas e restrições existentes. Desde a primeira aplicação dos Agentes Inteligentes aos mercados de comércio electrónico que os investigadores desta área, têm tentado sempre auto-superar-se arranjando modelos de Agentes Inteligentes melhores e mais eficientes. Uma das técnicas usadas, para a tentativa de obtenção deste objectivo, é a transferência dos comportamentos Humanos, no que toca a negociação e decisão, para estes mesmos Agentes Inteligentes. O objectivo desta dissertação é averiguar se os Modelos de Avaliação de Credibilidade e Reputação são uma adição útil ao processo de negociação dos Agente Inteligentes. O objectivo geral dos modelos deste tipo é minimizar as situações de fraude ou incumprimento sistemático dos acordos realizados aquando do processo de negociação. Neste contexto, foi proposto um Modelo de Avaliação de Credibilidade e Reputação aplicável aos mercados de comércio electrónico actuais e que consigam dar uma resposta adequada o seu elevado nível de exigência. Além deste modelo proposto também foi desenvolvido um simulador Multi-Agente com a capacidade de simular vários cenários e permitir, desta forma, comprovar a aplicabilidade do modelo proposto. Por último, foram realizadas várias experiências sobre o simulador desenvolvido, de forma a ser possível retirar algumas conclusões para o presente trabalho. Sendo a conclusão mais importante a verificação/validação de que a utilização de mecanismos de credibilidade e reputação são uma mais-valia para os mercado de comércio electrónico.Since its emergence, the Internet has had a development and a growth rate almost exponentially. The markets for electronic commerce have been accompanying almost side-by-side this growth trend, becoming increasingly common and popular among traders and occasional buyers/sellers. With this growth, also the complexity and sophistication of the systems has increased. On the following of these developments came the Intelligent Agents due to their ability to find and choose with a relatively efficient form, the best deal, based on the goal objective and existing restrictions. Since the first application of Intelligent Agents for e-commerce markets that researchers in this area are always trying to overcome themselves by developing better and more efficient Intelligent Agents models. One of the techniques used to attempt to achieve this objective, is the transfer of human behavior, when it comes to negotiating and decision, for these Intelligent Agents. The objective of this dissertation is to evaluate if the Evaluation Models of Trust and Reputation are a useful addition to the negotiation process. The main objective of this type of models is to try to minimize the occurrences of frauds or systematic failure to comply with the agreements reached during the negotiation process. In this context, a Model to Assess Credibility and Reputation applicable to the current e-commerce markets was proposed, which should be capable of responding adequately to these markets known for being highly demanding. Other than this model, a Multi-Agent Simulator capable of simulating several scenarios was also developed, thus making it possible to confirm the applicability of the proposed model. Lastly, several experiments were carried out on the developed simulator so that it is possible to draw some conclusions for this work. The most important conclusion is the verification/validation that credibility and reputation mechanisms are an asset to e-commerce markets

K x N Trust-Based Agent Reputation

In this research, a multi-agent system called KMAS is presented that models an environment of intelligent, autonomous, rational, and adaptive agents that reason about trust, and adapt trust based on experience. Agents reason and adapt using a modification of the k-Nearest Neighbor algorithm called (k X n) Nearest Neighbor where k neighbors recommend reputation values for trust during each of n interactions. Reputation allows a single agent to receive recommendations about the trustworthiness of others. One goal is to present a recommendation model of trust that outperforms MAS architectures relying solely on direct agent interaction. A second goal is to converge KMAS to an emergent system state where only successful cooperation is allowed. Three experiments are chosen to compare KMAS against a non-(k X n) MAS, and between different variations of KMAS execution. Research results show KMAS converges to the desired state, and in the context of this research, KMAS outperforms a direct interaction-based system