Managing distributed situation awareness in a team of agents

The research presented in this thesis investigates the best ways to manage Distributed Situation Awareness (DSA) for a team of agents tasked to conduct search activity with limited resources (battery life, memory use, computational power, etc.). In the first part of the thesis, an algorithm to coordinate agents (e.g., UAVs) is developed. This is based on Delaunay triangulation with the aim of supporting efficient, adaptable, scalable, and predictable search. Results from simulation and physical experiments with UAVs show good performance in terms of resources utilisation, adaptability, scalability, and predictability of the developed method in comparison with the existing fixed-pattern, pseudorandom, and hybrid methods. The second aspect of the thesis employs Bayesian Belief Networks (BBNs) to define and manage DSA based on the information obtained from the agents' search activity. Algorithms and methods were developed to describe how agents update the BBN to model the system’s DSA, predict plausible future states of the agents’ search area, handle uncertainties, manage agents’ beliefs (based on sensor differences), monitor agents’ interactions, and maintains adaptable BBN for DSA management using structural learning. The evaluation uses environment situation information obtained from agents’ sensors during search activity, and the results proved superior performance over well-known alternative methods in terms of situation prediction accuracy, uncertainty handling, and adaptability. Therefore, the thesis’s main contributions are (i) the development of a simple search planning algorithm that combines the strength of fixed-pattern and pseudorandom methods with resources utilisation, scalability, adaptability, and predictability features; (ii) a formal model of DSA using BBN that can be updated and learnt during the mission; (iii) investigation of the relationship between agents search coordination and DSA management

Technological roadmap on AI planning and scheduling

At the beginning of the new century, Information Technologies had become basic and indispensable constituents of the production and preparation processes for all kinds of goods and services and with that are largely influencing both the working and private life of nearly every citizen. This development will continue and even further grow with the continually increasing use of the Internet in production, business, science, education, and everyday societal and private undertaking. Recent years have shown, however, that a dramatic enhancement of software capabilities is required, when aiming to continuously provide advanced and competitive products and services in all these fast developing sectors. It includes the development of intelligent systems – systems that are more autonomous, flexible, and robust than today’s conventional software. Intelligent Planning and Scheduling is a key enabling technology for intelligent systems. It has been developed and matured over the last three decades and has successfully been employed for a variety of applications in commerce, industry, education, medicine, public transport, defense, and government. This document reviews the state-of-the-art in key application and technical areas of Intelligent Planning and Scheduling. It identifies the most important research, development, and technology transfer efforts required in the coming 3 to 10 years and shows the way forward to meet these challenges in the short-, medium- and longer-term future. The roadmap has been developed under the regime of PLANET – the European Network of Excellence in AI Planning. This network, established by the European Commission in 1998, is the co-ordinating framework for research, development, and technology transfer in the field of Intelligent Planning and Scheduling in Europe. A large number of people have contributed to this document including the members of PLANET non- European international experts, and a number of independent expert peer reviewers. All of them are acknowledged in a separate section of this document. Intelligent Planning and Scheduling is a far-reaching technology. Accepting the challenges and progressing along the directions pointed out in this roadmap will enable a new generation of intelligent application systems in a wide variety of industrial, commercial, public, and private sectors

Proceedings of The Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW 2010)

http://ceur-ws.org/Vol-627/allproceedings.pdfInternational audienceMALLOW-2010 is a third edition of a series initiated in 2007 in Durham, and pursued in 2009 in Turin. The objective, as initially stated, is to "provide a venue where: the cost of participation was minimum; participants were able to attend various workshops, so fostering collaboration and cross-fertilization; there was a friendly atmosphere and plenty of time for networking, by maximizing the time participants spent together"

Challenges in artificial socio-cognitive systems: A study based on intelligent vehicles

This record contains the (video) data and source code created in relation to the submitted thesis of the same title.The videos included in this collection have been derived using the 3D view components included in the BSF software framework, during a number of scenarios explained more fully in the related thesis: "Challenges in artificial socio-cognitive systems: A study based on intelligent vehicles" Additional views such as the graph views have been created from the rdfUtilities package. These scenarios can be re-run by using the included version of the BSF framework which is provided as zip file. From the command line, run "ant -p" to see available projects, which includes the traffic simulation, institutions, 3D view, and more

Security Policies That Make Sense for Complex Systems: Comprehensible Formalism for the System Consumer

Information Systems today rarely are contained within a single user workstation, server, or networked environment. Data can be transparently accessed from any location, and maintained across various network infrastructures. Cloud computing paradigms commoditize the hardware and software environments and allow an enterprise to lease computing resources by the hour, minute, or number of instances required to complete a processing task. An access control policy mediates access requests between authorized users of an information system and the system\u27s resources. Access control policies are defined at any given level of abstraction, such as the file, directory, system, or network, and can be instantiated in layers of increasing (or decreasing) abstraction. For the system end-user, the functional allocation of security policy to discrete system components, or subsystems, may be too complex for comprehension. In this dissertation, the concept of a metapolicy, or policy that governs execution of subordinate security policies, is introduced. From the user\u27s perspective, the metapolicy provides the rules for system governance that are functionally applied across the system\u27s components for policy enforcement. The metapolicy provides a method to communicate updated higher-level policy information to all components of a system; it minimizes the overhead associated with access control decisions by making access decisions at the highest level possible in the policy hierarchy. Formal definitions of policy often involve mathematical proof, formal logic, or set theoretic notation. Such policy definitions may be beyond the capability of a system user who simply wants to control information sharing. For thousands of years, mankind has used narrative and storytelling as a way to convey knowledge. This dissertation discusses how the concepts of storytelling can be embodied in computational narrative and used as a top-level requirements specification. The definition of metapolicy is further discussed, as is the relationship between the metapolicy and various access control mechanisms. The use of storytelling to derive the metapolicy and its applicability to formal requirements definition is discussed. The author\u27s hypothesis on the use of narrative to explain security policy to the system user is validated through the use of a series of survey instruments. The survey instrument applies either a traditional requirements specification language or a brief narrative to describe a security policy and asks the subject to interpret the statements. The results of this research are promising and reflect a synthesis of the disciplines of neuroscience, security, and formal methods to present a potentially more comprehensible knowledge representation of security policy

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts