Evolving‌ ‌artificial‌ ‌neural‌ ‌networks‌‌ ‌to‌ ‌imitate‌ ‌human‌ ‌behaviour‌‌ ‌in‌ ‌Shinobi‌ ‌III‌ ‌:‌ ‌return‌ ‌of‌ ‌the‌ ‌Ninja‌ ‌master‌

Notre société est de plus en plus friande d’outils informatiques. Ce phénomène s’est particulièrement accru lors de cette dernière décennie suite, entre autres, à l’émergence d’un nouveau paradigme d’Intelligence Artificielle. Plus précisément, le couplage de deux techniques algorithmiques, les Réseaux de Neurones Profonds et la Descente de Gradient Stochastique, propulsé par une force de calcul exponentiellement croissante, est devenu et continue de devenir un atout majeur dans de nombreuses nouvelles technologies. Néanmoins, alors que le progrès suit son cours, certains se demandent toujours si d’autres méthodes pourraient similairement, voire davantage, bénéficier de ces diverses avancées matérielles. Afin de pousser cette étude, nous nous attelons dans ce mémoire aux Algorithmes Évolutionnaires et leur application aux Réseaux de Neurones Dynamiques, deux techniques dotées d’un grand nombre de propriétés avantageuses n’ayant toutefois pas trouvé leur place au sein de l’Intelligence Artificielle contemporaine. Nous trouvons qu’en élaborant de nouvelles méthodes tout en exploitant une forte puissance informatique, il nous devient alors possible de développer des agents à haute performance sur des bases de référence ainsi que d’autres agents se comportant de façon très similaire à des sujets humains sur le jeu vidéo Shinobi III : Return of The Ninja Master, cas typique de tâches complexes que seule l’optimisation par gradient était capable d’aborder jusqu’alors.Our society is increasingly fond of computational tools. This phenomenon has greatly increased over the past decade following, among other factors, the emergence of a new Artificial Intelligence paradigm. Specifically, the coupling of two algorithmic techniques, Deep Neural Networks and Stochastic Gradient Descent, thrusted by an exponentially increasing computing capacity, has and is continuing to become a major asset in many modern technologies. However, as progress takes its course, some still wonder whether other methods could similarly or even more greatly benefit from these various hardware advances. In order to further this study, we delve in this thesis into Evolutionary Algorithms and their application to Dynamic Neural Networks, two techniques which despite enjoying many advantageous properties have yet to find their niche in contemporary Artificial Intelligence. We find that by elaborating new methods while exploiting strong computational resources, it becomes possible to develop strongly performing agents on a variety of benchmarks but also some other agents behaving very similarly to human subjects on the video game Shinobi III : Return of The Ninja Master, typical complex tasks previously out of reach for non-gradient-based optimization

Evolving Artificial Neural Networks To Imitate Human Behaviour In Shinobi III : Return of the Ninja Master

Our society is increasingly fond of computational tools. This phenomenon has greatly increased over the past decade following, among other factors, the emergence of a new Artificial Intelligence paradigm. Specifically, the coupling of two algorithmic techniques, Deep Neural Networks and Stochastic Gradient Descent, thrusted by an exponentially increasing computing capacity, has and is continuing to become a major asset in many modern technologies. However, as progress takes its course, some still wonder whether other methods could similarly or even more greatly benefit from these various hardware advances. In order to further this study, we delve in this thesis into Evolutionary Algorithms and their application to Dynamic Neural Networks, two techniques which despite enjoying many advantageous properties have yet to find their niche in contemporary Artificial Intelligence. We find that by elaborating new methods while exploiting strong computational resources, it becomes possible to develop strongly performing agents on a variety of benchmarks but also some other agents behaving very similarly to human subjects on the video game Shinobi III : Return of The Ninja Master, typical complex tasks previously out of reach for non-gradient-based optimization

Evolving Intelligent Multimodal Gameplay Agents and Decision Makers with Neuroevolution

�Super Mario Bros� is a difficult platforming game that requires the use of multiple behavioral modes to complete different gameplay elements such as: collecting coins, dodging enemies and getting to the end of the level. Methods for creating intelligent game playing agents have previously used human designed behavior policy for each gameplay state or by combining gameplay goals into a single task to be learned. This thesis assesses the development and method of training machines to promote multiple modes of behavior within neural network controllers. These controllers utilize the concept of evolution through multi-objective optimization for the test bench platform game system �MarioAI�. Artificial neural networks were evolved to exhibit complex and multimodal behavior using multiple sub objectives of the game; and thus overcome the non-linear, noisy, and fractured game environment. Experiments were conducted with the purpose of creating multiple Pareto-optimal solutions of quality with differing behavioral aspects. These solutions were then discerned by a Decision Maker Neural Network Ensemble that had been evolved to pick the best solution according to game level. This Decision Maker Ensemble proved to be able to learn on minimal information and provide the highest overall game score. The results of this thesis show that it�s possible to train agents on sub objectives to teach multiple forms of complex behavior that can then be abstractly chosen by an evolved Decision Maker to provide a better outcome than agents that were trained specifically towards that single solution.Electrical Engineerin

Visual Attention in Dynamic Environments and its Application to Playing Online Games

Abstract In this thesis we present a prototype of Cognitive Programs (CPs) - an executive controller built on top of Selective Tuning (ST) model of attention. CPs enable top-down control of visual system and interaction between the low-level vision and higher-level task demands. Abstract We implement a subset of CPs for playing online video games in real time using only visual input. Two commercial closed-source games - Canabalt and Robot Unicorn Attack - are used for evaluation. Their simple gameplay and minimal controls put the emphasis on reaction speed and attention over planning. Abstract Our implementation of Cognitive Programs plays both games at human expert level, which experimentally proves the validity of the concept. Additionally we resolved multiple theoretical and engineering issues, e.g. extending the CPs to dynamic environments, finding suitable data structures for describing the task and information flow within the network and determining the correct timing for each process

Enhancing player experience in computer games: A computational Intelligence approach.

Ph.DDOCTOR OF PHILOSOPH

MAPiS 2019 - First MAP-i Seminar: proceedings

This book contains a selection of Informatics papers accepted for presentation and discussion at “MAPiS 2019 - First MAP-i Seminar”, held in Aveiro, Portugal, January 31, 2019. MAPiS is the first conference organized by the MAP-i first year students, in the context of the Seminar course. The MAP-i Doctoral Programme in Computer Science is a joint Doctoral Programme in Computer Science of the University of Minho, the University of Aveiro and the University of Porto. This programme aims to form highly-qualified professionals, fostering their capacity and knowledge to the research area. This Conference was organized by the first grade students attending the Seminar Course. The aim of the course was to introduce concepts which are complementary to scientific and technological education, but fundamental to both completing a PhD successfully and entailing a career on scientific research. The students had contact with the typical procedures and difficulties of organizing and participate in such a complex event. These students were in charge of the organization and management of all the aspects of the event, such as the accommodation of participants or revision of the papers. The works presented in the Conference and the papers submitted were also developed by these students, fomenting their enthusiasm regarding the investigation in the Informatics area. (...)publishe

Foundations of Trusted Autonomy

Trusted Autonomy; Automation Technology; Autonomous Systems; Self-Governance; Trusted Autonomous Systems; Design of Algorithms and Methodologie

SpiNNaker - A Spiking Neural Network Architecture

20 years in conception and 15 in construction, the SpiNNaker project has delivered the world’s largest neuromorphic computing platform incorporating over a million ARM mobile phone processors and capable of modelling spiking neural networks of the scale of a mouse brain in biological real time. This machine, hosted at the University of Manchester in the UK, is freely available under the auspices of the EU Flagship Human Brain Project. This book tells the story of the origins of the machine, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over. It also presents exemplar applications from ‘Talk’, a SpiNNaker-controlled robotic exhibit at the Manchester Art Gallery as part of ‘The Imitation Game’, a set of works commissioned in 2016 in honour of Alan Turing, through to a way to solve hard computing problems using stochastic neural networks. The book concludes with a look to the future, and the SpiNNaker-2 machine which is yet to come