Imitation learning through games: theory, implementation and evaluation

Despite a history of games-based research, academia has generally regarded commercial games as a distraction from the serious business of AI, rather than as an opportunity to leverage this existing domain to the advancement of our knowledge. Similarly, the computer game industry still relies on techniques that were developed several decades ago, and has shown little interest in adopting more progressive academic approaches. In recent times, however, these attitudes have begun to change; under- and post-graduate games development courses are increasingly common, while the industry itself is slowly but surely beginning to recognise the potential offered by modern machine-learning approaches, though games which actually implement said approaches on more than a token scale remain scarce. One area which has not yet received much attention from either academia or industry is imitation learning, which seeks to expedite the learning process by exploiting data harvested from demonstrations of a given task. While substantial work has been done in developing imitation techniques for humanoid robot movement, there has been very little exploration of the challenges posed by interactive computer games. Given that such games generally encode reasoning and decision-making behaviours which are inherently more complex and potentially more interesting than limb motion data, that they often provide inbuilt facilities for recording human play, that the generation and collection of training samples is therefore far easier than in robotics, and that many games have vast pre-existing libraries of these recorded demonstrations, it is fair to say that computer games represent an extremely fertile domain for imitation learning research. In this thesis, we argue in favour of using modern, commercial computer games to study, model and reproduce humanlike behaviour. We provide an overview of the biological and robotic imitation literature as well as the current status of game AI, highlighting techniques which may be adapted for the purposes of game-based imitation. We then proceed to describe our contributions to the field of imitation learning itself, which encompass three distinct categories: theory, implementation and evaluation. We first describe the development of a fully-featured Java API - the Quake2 Agent Simulation Environment (QASE) - designed to facilitate both research and education in imitation and general machine-learning, using the game Quake 2 as a testbed. We outline our motivation for developing QASE, discussing the shortcomings of existing APIs and the steps which we have taken to circumvent them. We describe QASE’s network layer, which acts as an interface between the local AI routines and the Quake 2 server on which the game environment is maintained, before detailing the API’s agent architecture, which includes an interface to the MatLab programming environment and the ability to parse and analyse full recordings of game sessions. We conclude the chapter with a discussion of QASE’s adoption by numerous universities as both an undergraduate teaching tool and research platform. We then proceed to describe the various imitative mechanisms which we have developed using QASE and its MatLab integration facilities. We first outline a behaviour model based on a well-known psychological model of human planning. Drawing upon previous research, we also identify a set of believability criteria - elements of agent behaviour which are of particular importance in determining the “humanness” of its in-game appearance. We then detail a reinforcement-learning approach to imitating the human player’s navigation of his environment, centred upon his pursuit of items as strategic goals. In the subsequent section, we describe the integration of this strategic system with a Bayesian mechanism for the imitation of tactical and motion-modelling behaviours. Finally, we outline a model for the imitation of reactive combat behaviours; specifically, weapon-selection and aiming. Experiments are presented in each case to demonstrate the imitative mechanisms’ ability to accurately reproduce observed behaviours. Finally, we criticise the lack of any existing methodology to formally gauge the believability of game agents, and observe that the few previous attempts have been extremely ad-hoc and informal. We therefore propose a generalised approach to such testing; the Bot-Oriented Turing Test (BOTT). This takes the form of an anonymous online questionnaire, an accompanying protocol to which examiners should adhere, and the formulation of a believability index which numerically expresses each agent’s humanness as indicated by its observers, weighted by their experience and the accuracy with which the agents were identified. To both validate the survey approach and to determine the efficacy of our imitative models, we present a series of experiments which use the believability test to evaluate our own imitation agents against both human players and traditional artificial bots. We demonstrate that our imitation agents perform substantially better than even a highly-regarded rule-based agent, and indeed approach the believability of actual human players. Some suggestions for future directions in our research, as well as a broader discussion of open questions, conclude this thesis

Cyber Security and Critical Infrastructures 2nd Volume

The second volume of the book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles, including an editorial that explains the current challenges, innovative solutions and real-world experiences that include critical infrastructure and 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems

Radio Communications

In the last decades the restless evolution of information and communication technologies (ICT) brought to a deep transformation of our habits. The growth of the Internet and the advances in hardware and software implementations modified our way to communicate and to share information. In this book, an overview of the major issues faced today by researchers in the field of radio communications is given through 35 high quality chapters written by specialists working in universities and research centers all over the world. Various aspects will be deeply discussed: channel modeling, beamforming, multiple antennas, cooperative networks, opportunistic scheduling, advanced admission control, handover management, systems performance assessment, routing issues in mobility conditions, localization, web security. Advanced techniques for the radio resource management will be discussed both in single and multiple radio technologies; either in infrastructure, mesh or ad hoc networks

Privacidade em redes de próxima geração

Doutoramento em Engenharia InformáticaIn the modern society, communications and digital transactions are becoming the norm rather than the exception. As we allow networked computing devices into our every-day actions, we build a digital lifestyle where networks and devices enrich our interactions. However, as we move our information towards a connected digital environment, privacy becomes extremely important as most of our personal information can be found in the network. This is especially relevant as we design and adopt next generation networks that provide ubiquitous access to services and content, increasing the impact and pervasiveness of existing networks. The environments that provide widespread connectivity and services usually rely on network protocols that have few privacy considerations, compromising user privacy. The presented work focuses on the network aspects of privacy, considering how network protocols threaten user privacy, especially on next generation networks scenarios. We target the identifiers that are present in each network protocol and support its designed function. By studying how the network identifiers can compromise user privacy, we explore how these threats can stem from the identifier itself and from relationships established between several protocol identifiers. Following the study focused on identifiers, we show that privacy in the network can be explored along two dimensions: a vertical dimension that establishes privacy relationships across several layers and protocols, reaching the user, and a horizontal dimension that highlights the threats exposed by individual protocols, usually confined to a single layer. With these concepts, we outline an integrated perspective on privacy in the network, embracing both vertical and horizontal interactions of privacy. This approach enables the discussion of several mechanisms to address privacy threats on individual layers, leading to architectural instantiations focused on user privacy. We also show how the different dimensions of privacy can provide insight into the relationships that exist in a layered network stack, providing a potential path towards designing and implementing future privacy-aware network architectures.Na sociedade moderna, as comunicações e transacções digitais estão a tornar-se a regra e não a excepção. À medida que permitimos a intromissão de dispositivos electrónicos de rede no nosso quotidiano, vamos construíndo um estilo de vida digital onde redes e dispositivos enrirquecem as nossas interacções. Contudo, ao caminharmos para um ambiente digital em rede, a nossa privacidade vai-se revestindo de maior importãncia, pois a nossa informação pessoal passa a encontrar-se cada vez mais na rede. Isto torna-se particularmente relevante ao adoptarmos redes de próxima geração, que permitem acesso ubíquo a redes, serviços e conteúdos, aumentando o impacte e pervasividade das redes actuais. Os ambientes onde a conectividade e os serviços se tornam uma constante, assentam em protocolos de rede que normalmente contemplam poucas considerações sobre privacidade, comprometendo desta forma o utlizador. O presente trabalho centra-se nos aspectos de privacidade que dizem respeito à rede devido à forma como os protocolos são utilizados nas diferentes camadas, e que resultando em ameaças à privacidade do utilizador. Abordamos especificamente os identificadores presentes nos protocolos de rede, e que são essenciais à sua função. Neste contexto exploramos a possibilidade destes identificadores comprometerem a privacidade do utilizador através da informação neles contida, bem como das relações que podem ser estabelecidas entre identificadores de diferentes protocolos. Após este estudo centrado nos identificadores, mostramos como a privacidade em redes pode ser explorada ao longo de duas dimensões: uma dimensão que acentua as relações verticais de privacidade, cruzando vários protocolos até chegar ao utilizador, e uma dimensão horizontal que destaca as ameaças causadas por cada protocolo, de forma individual, normalmente limitadas a uma única camada. Através destes conceitos, mostramos uma visão integrada de privacidade em redes, abrangendo tanto as interacçoes de privacidade verticais como as horizontais. Esta visão permite discutir vários mecanismos para mitigar ameaças específicas a cada camada de rede, resultando em instânciações arquitecturais orientadas à privacidade do utilizador. Finalmente, mostramos como as diferentes dimensões de privacidade podem fornecer uma visão diferente sobre as relações estabelecidas na pilha protocolar que assenta em camadas, mostrando um caminho possível para o desenvolvimento de futuras arquitecturas de rede com suporte para privacidade

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies

This publication comprises the papers presented at the 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland, on May 9-11, 1995. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed