Autonomous Navigation in (the Animal and) the Machine

Understanding the principles underlying autonomous navigation might be the most enticing quest the computational neuroscientist can undertake. Autonomous operation, also known as voluntary behavior, is the result of higher cognitive mechanisms and what is known as executive function in psychology. A rudimentary knowledge of the brain can explain where and to a certain degree how parts of a computation are expressed. However, achieving a satisfactory understanding of the neural computation involved in voluntary behavior is beyond today’s neuroscience. In contrast with the study of the brain, with a comprehensive body of theory for trying to understand system with unmatched complexity, the field of AI is to a larger extent guided by examples of achievements. Although the two sciences differ in methods, theoretical foundation, scientific vigour, and direct applicability, the intersection between the two may be a viable approach toward understanding autonomy. This project is an example of how both fields may benefit from such a venture. The findings presented in this thesis may be interesting for behavioral neuroscience, exploring how operant functions can be combined to form voluntary behavior. The presented theory can also be considered as documentation of a successful implementation of autonomous navigation in Euclidean space. Findings are grouped into three parts, as expressed in this thesis. First, pertinent back- ground theory is presented in Part I – collecting key findings from psychology and from AI relating to autonomous navigation. Part II presents a theoretical contribution to RL theory developed during the design and implementation of the emulator for navigational autonomy, before experimental findings from a selection of published papers are attached as Part III. Note how this thesis emphasizes the understanding of volition and autonomous navigation rather than accomplishments by the agent, reflecting the aim of this project – to understand the basic principles of autonomous navigation to a sufficient degree to be able to recreate its effect by first principles

Survey of Inter-satellite Communication for Small Satellite Systems: Physical Layer to Network Layer View

Small satellite systems enable whole new class of missions for navigation, communications, remote sensing and scientific research for both civilian and military purposes. As individual spacecraft are limited by the size, mass and power constraints, mass-produced small satellites in large constellations or clusters could be useful in many science missions such as gravity mapping, tracking of forest fires, finding water resources, etc. Constellation of satellites provide improved spatial and temporal resolution of the target. Small satellite constellations contribute innovative applications by replacing a single asset with several very capable spacecraft which opens the door to new applications. With increasing levels of autonomy, there will be a need for remote communication networks to enable communication between spacecraft. These space based networks will need to configure and maintain dynamic routes, manage intermediate nodes, and reconfigure themselves to achieve mission objectives. Hence, inter-satellite communication is a key aspect when satellites fly in formation. In this paper, we present the various researches being conducted in the small satellite community for implementing inter-satellite communications based on the Open System Interconnection (OSI) model. This paper also reviews the various design parameters applicable to the first three layers of the OSI model, i.e., physical, data link and network layer. Based on the survey, we also present a comprehensive list of design parameters useful for achieving inter-satellite communications for multiple small satellite missions. Specific topics include proposed solutions for some of the challenges faced by small satellite systems, enabling operations using a network of small satellites, and some examples of small satellite missions involving formation flying aspects.Comment: 51 pages, 21 Figures, 11 Tables, accepted in IEEE Communications Surveys and Tutorial

Journey of Artificial Intelligence Frontier: A Comprehensive Overview

The field of Artificial Intelligence AI is a transformational force with limitless promise in the age of fast technological growth This paper sets out on a thorough tour through the frontiers of AI providing a detailed understanding of its complex environment Starting with a historical context followed by the development of AI seeing its beginnings and growth On this journey fundamental ideas are explored looking at things like Machine Learning Neural Networks and Natural Language Processing Taking center stage are ethical issues and societal repercussions emphasising the significance of responsible AI application This voyage comes to a close by looking ahead to AI s potential for human-AI collaboration ground-breaking discoveries and the difficult obstacles that lie ahead This provides with a well-informed view on AI s past present and the unexplored regions it promises to explore by thoroughly navigating this terrai

On Building Generalizable Learning Agents

It has been a long-standing goal in Artificial Intelligence (AI) to build machines that can solve tasks that humans can. Thanks to the recent rapid progress in data-driven methods, which train agents to solve tasks by learning from massive training data, there have been many successes in applying such learning approaches to handle and even solve a number of extremely challenging tasks, including image classification, language generation, robotics control, and several multi-player games. The key factor for all these data-driven successes is that the trained agents can generalize to test scenarios that are unseen during training. This generalization capability is the foundation for building any practical AI system. This thesis studies generalization, the fundamental challenge in AI, and proposes solutions to improve the generalization performances of learning agents in a variety of problems. We start by providing a formal formulation of the generalization problem in the context of reinforcement learning and proposing 4 principles within this formulation to guide the design of training techniques for improved generalization. We validate the effectiveness of our proposed principles by considering 4 different domains, from simple to complex, and developing domain-specific techniques following these principles. Particularly, we begin with the simplest domain, i.e., path-finding on graphs (Part I), and then consider visual navigation in a 3D world (Part II) and competition in complex multi-agent games (Part III), and lastly tackle some natural language processing tasks (Part IV). Empirical evidences demonstrate that the proposed principles can generally lead to much improved generalization performances in a wide range of problems

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

Agent Bodies: An Interface Between Agent and Environment

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-23850-0_2Interfacing the agents with their environment is a classical problem when designing multiagent systems. However, the models pertaining to this interface generally choose to either embed it in the agents, or in the environment. In this position paper, we propose to highlight the role of agent bodies as primary components of the multiagent system design. We propose a tentative definition of an agent body, and discuss its responsibilities in terms of MAS components. The agent body takes from both agent and environment: low-level agent mechanisms such as perception and influences are treated locally in the agent bodies. These mechanism participate in the cognitive process, but are not driven by symbol manipulation. Furthermore, it allows to define several bodies for one mind, either to simulate different capabilities, or to interact in the different environments - physical, social- the agent is immersed in. We also draw the main challenges to apply this concept effectively.Saunier, J.; Carrascosa Casamayor, C.; Galland, S.; Kanmeugne, PS. (2015). Agent Bodies: An Interface Between Agent and Environment. En Agent Environments for Multi-Agent Systems IV. 4th International Workshop, E4MAS 2014 - 10 Years Later, Paris, France, May 6, 2014. 25-40. doi:10.1007/978-3-319-23850-0_2S2540Barella, A., Ricci, A., Boissier, O., Carrascosa, C.: MAM5: Multi-agent model for intelligent virtual environments. In: 10th European Workshop on Multi-Agent Systems (EUMAS 2012), pp. 16–30 (2012)Behe, F., Galland, S., Gaud, N., Nicolle, C., Koukam, A.: An ontology-based metamodel for multiagent-based simulations. Int. J. Simul. Model. Pract. 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Autonomous Agents and Multiagent Systems (AAMAS 2015), Istanbul, Turkey, May 2015Galland, S., Gaud, N., Demange, J., Koukam, A.: Environment model for multiagent-based simulation of 3D urban systems. In: the 7th European Workshop on Multiagent Systems (EUMAS 2009), Ayia Napa, Cyprus, December 2009 (paper 36)Gechter, F., Contet, J.M., Lamotte, O., Galland, S., Koukam, A.: Virtual intelligent vehicle urban simulator: application to vehicle platoon evaluation. Simul. Model. Practice Theor. (SIMPAT) 24, 103–114 (2012)Gibson, J.J.: The Theory of Affordances. Hilldale, USA (1977)Gouaïch, A., Michel, F., Guiraud, Y.: MIC $$^{*}$$ : a deployment environment for autonomous agents. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2004. LNCS (LNAI), vol. 3374, pp. 109–126. Springer, Heidelberg (2005)Gouaïch, A., Michel, F.: Towards a unified view of the environment (s) within multi-agent systems. Informatica (Slovenia) 29(4), 423–432 (2005)Helleboogh, A., Vizzari, G., Uhrmacher, A., Michel, F.: Modeling dynamic environments in multiagent simulation. Int. J. Auton. Agents Multiagent Syst. 14(1), 87–116 (2007)Ketenci, U.G., Bremond, R., Auberlet, J.M., Grislin, E.: Drivers with limited perception: models and applications to traffic simulation. Recherche transports sécurité, RTS (2013)Michel, F.: The IRM4S model: the influence/reaction principle for multiagent based simulation. ACM, May 2007Okuyama, F.Y., Bordini, R.H., da Rocha Costa, A.C.: ELMS: an environment description language for multi-agent simulation. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2004. LNCS (LNAI), vol. 3374, pp. 67–83. Springer, Heidelberg (2005)Platon, E., Sabouret, N., Honiden, S.: Environmental support for tag interactions. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2006. LNCS (LNAI), vol. 4389, pp. 106–123. 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Intelligence without Representation: A Historical Perspective

This paper reflects on a seminal work in the history of AI and representation: Rodney Brooks’ 1991 paper Intelligence without Representation. Brooks advocated the removal of explicit representations and engineered environments from the domain of his robotic intelligence experimentation, in favour of an evolutionary-inspired approach using layers of reactive behaviour that operated independently of each other. Brooks criticised the current progress in AI research and believed that removing complex representation from AI would help address problematic areas in modelling the mind. His belief was that we should develop artificial intelligence by being guided by evolutionary development of our own intelligence, and that his approach mirrored how our own intelligence functions. Thus the field of behaviour-based robotics emerged. This paper offers a historical analysis of Brooks’ behaviour-based robotics approach and its impact in artificial intelligence and cognitive theory at the time, as well as in modern-day approaches to AI

Adaptive Middleware for Resource-Constrained Mobile Ad Hoc and Wireless Sensor Networks

Mobile ad hoc networks: MANETs) and wireless sensor networks: WSNs) are two recently-developed technologies that uniquely function without fixed infrastructure support, and sense at scales, resolutions, and durations previously not possible. While both offer great potential in many applications, developing software for these types of networks is extremely difficult, preventing their wide-spread use. Three primary challenges are: 1) the high level of dynamics within the network in terms of changing wireless links and node hardware configurations,: 2) the wide variety of hardware present in these networks, and: 3) the extremely limited computational and energy resources available. Until now, the burden of handling these issues was put on the software application developer. This dissertation presents three novel programming models and middleware systems that address these challenges: Limone, Agilla, and Servilla. Limone reliably handles high levels of dynamics within MANETs. It does this through lightweight coordination primitives that make minimal assumptions about network connectivity. Agilla enables self-adaptive WSN applications via the integration of mobile agent and tuple space programming models, which is critical given the continuously changing network. It is the first system to successfully demonstrate the feasibility of using mobile agents and tuple spaces within WSNs. Servilla addresses the challenges that arise from WSN hardware heterogeneity using principles of Service-Oriented Computing: SOC). It is the first system to successfully implement the entire SOC model within WSNs and uniquely tailors it to the WSN domain by making it energy-aware and adaptive. The efficacies of the above three systems are demonstrated through implementation, micro-benchmarks, and the evaluation of several real-world applications including Universal Remote, Fire Detection and Tracking, Structural Health Monitoring, and Medical Patient Monitoring