Hybrid automata dicretising agents for formal modelling of robots

Some of the fundamental capabilities required by autonomous vehicles and systems for their intelligent decision making are: modelling of the environment and forming data abstractions for symbolic, logic based reasoning. The paper formulates a discrete agent framework that abstracts and controls a hybrid system that is a composition of hybrid automata modelled continuous individual processes. Theoretical foundations are laid down for a class of general model composition agents (MCAs) with an advanced subclass of rational physical agents (RPAs). We define MCAs as the most basic structures for the description of complex autonomous robotic systems. The RPA’s have logic based decision making that is obtained by an extension of the hybrid systems concepts using a set of abstractions. The theory presented helps the creation of robots with reliable performance and safe operation in their environment. The paper emphasizes the abstraction aspects of the overall hybrid system that emerges from parallel composition of sets of RPAs and MCAs

A dynamical systems approach to micro spacecraft autonomy

The drive toward reducing the size and mass of spacecraft has put new constraints on the computational resources available for control and decision making algorithms. The aim of this paper is to present alternative methods for decision making algorithms that can be introduced for micro-spacecraft. The motivation behind this work comes from dynamical systems theory. Systems of differential equations can be built to define behaviors which can be manipulated to define an action selection algorithm. These algorithms can be mathematically validated and shown to be computationally efficient, providing robust autonomous control with a modest computational overhead

The Responsibility Quantification (ResQu) Model of Human Interaction with Automation

Intelligent systems and advanced automation are involved in information collection and evaluation, in decision-making and in the implementation of chosen actions. In such systems, human responsibility becomes equivocal. Understanding human casual responsibility is particularly important when intelligent autonomous systems can harm people, as with autonomous vehicles or, most notably, with autonomous weapon systems (AWS). Using Information Theory, we develop a responsibility quantification (ResQu) model of human involvement in intelligent automated systems and demonstrate its applications on decisions regarding AWS. The analysis reveals that human comparative responsibility to outcomes is often low, even when major functions are allocated to the human. Thus, broadly stated policies of keeping humans in the loop and having meaningful human control are misleading and cannot truly direct decisions on how to involve humans in intelligent systems and advanced automation. The current model is an initial step in the complex goal to create a comprehensive responsibility model, that will enable quantification of human causal responsibility. It assumes stationarity, full knowledge regarding the characteristic of the human and automation and ignores temporal aspects. Despite these limitations, it can aid in the analysis of systems designs alternatives and policy decisions regarding human responsibility in intelligent systems and advanced automation

Synthesis about a collaborative project on “Technology Assessment of Autonomous Systems”

The project started in 2009 with the support of DAAD in Germany and CRUP in Portugal under the “Collaborative German-Portuguese University Actions” programme. One central goal is the further development of a theory of technology assessment applied to robotics and autonomous systems in general that reflects in its methodology the changing conditions of knowledge production in modern societies and the emergence of new robotic technologies and of associated disruptive changes. Relevant topics here are handling broadened future horizons and new clusters of science and technology (medicine, engineering, interfaces, industrial automation, micro-devices, security and safety), as well as new governance structures in policy decision making concerning research and development (R&D).Robotic systems, Autonomous systems, Technology assessment, Germany, Portugal

A unified framework for resource-bounded autonomous agents interacting with unknown environments

The aim of this thesis is to present a mathematical framework for conceptualizing and constructing adaptive autonomous systems under resource constraints. The first part of this thesis contains a concise presentation of the foundations of classical agency: namely the formalizations of decision making and learning. Decision making includes: (a) subjective expected utility (SEU) theory, the framework of decision making under uncertainty; (b) the maximum SEU principle to choose the optimal solution; and (c) its application to the design of autonomous systems, culminating in the Bellman optimality equations. Learning includes: (a) Bayesian probability theory, the theory for reasoning under uncertainty that extends logic; and (b) Bayes-Optimal agents, the application of Bayesian probability theory to the design of optimal adaptive agents. Then, two major problems of the maximum SEU principle are highlighted: (a) the prohibitive computational costs and (b) the need for the causal precedence of the choice of the policy. The second part of this thesis tackles the two aforementioned problems. First, an information-theoretic notion of resources in autonomous systems is established. Second, a framework for resource-bounded agency is introduced. This includes: (a) a maximum bounded SEU principle that is derived from a set of axioms of utility; (b) an axiomatic model of probabilistic causality, which is applied for the formalization of autonomous systems having uncertainty over their policy and environment; and (c) the Bayesian control rule, which is derived from the maximum bounded SEU principle and the model of causality, implementing a stochastic adaptive control law that deals with the case where autonomous agents are uncertain about their policy and environment

Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles

Control theory provides engineers with a multitude of tools to design controllers that manipulate the closed-loop behavior and stability of dynamical systems. These methods rely heavily on insights about the mathematical model governing the physical system. However, in complex systems, such as autonomous underwater vehicles performing the dual objective of path-following and collision avoidance, decision making becomes non-trivial. We propose a solution using state-of-the-art Deep Reinforcement Learning (DRL) techniques, to develop autonomous agents capable of achieving this hybrid objective without having \`a priori knowledge about the goal or the environment. Our results demonstrate the viability of DRL in path-following and avoiding collisions toward achieving human-level decision making in autonomous vehicle systems within extreme obstacle configurations

Decision support for natural resource management; models and evaluation methods

When managing natural resources or agrobusinesses, one always has to deal with autonomous processes. These autonomous processes play a core role in designing model-based decision support systems. This chapter tries to give insight into the question of which types of models might be used in which cases. It does so by formulating a rough categorization of decision problems and providing many examples. Particular attention is given to the role of statistical learning theory, which may be used to replace mathematical modeling by training with example