Influence of the number of predecessors in interaction within acceleration-based flow models

In this paper, the stability of the uniform solutions is analysed for microscopic flow models in interaction with $K\ge1$ predecessors. We calculate general conditions for the linear stability on the ring geometry and explore the results with particular pedestrian and car-following models based on relaxation processes. The uniform solutions are stable if the relaxation times are sufficiently small. The analysis is focused on the relevance of the number of predecessors in the dynamics. Unexpected non-monotonic relations between $K$ and the stability are presented.Comment: 18 pages, 14 figure

Self-directedness, integration and higher cognition

In this paper I discuss connections between self-directedness, integration and higher cognition. I present a model of self-directedness as a basis for approaching higher cognition from a situated cognition perspective. According to this model increases in sensorimotor complexity create pressure for integrative higher order control and learning processes for acquiring information about the context in which action occurs. This generates complex articulated abstractive information processing, which forms the major basis for higher cognition. I present evidence that indicates that the same integrative characteristics found in lower cognitive process such as motor adaptation are present in a range of higher cognitive process, including conceptual learning. This account helps explain situated cognition phenomena in humans because the integrative processes by which the brain adapts to control interaction are relatively agnostic concerning the source of the structure participating in the process. Thus, from the perspective of the motor control system using a tool is not fundamentally different to simply controlling an arm

Game-Theoretic Safety Assurance for Human-Centered Robotic Systems

In order for autonomous systems like robots, drones, and self-driving cars to be reliably introduced into our society, they must have the ability to actively account for safety during their operation. While safety analysis has traditionally been conducted offline for controlled environments like cages on factory floors, the much higher complexity of open, human-populated spaces like our homes, cities, and roads makes it unviable to rely on common design-time assumptions, since these may be violated once the system is deployed. Instead, the next generation of robotic technologies will need to reason about safety online, constructing high-confidence assurances informed by ongoing observations of the environment and other agents, in spite of models of them being necessarily fallible.This dissertation aims to lay down the necessary foundations to enable autonomous systems to ensure their own safety in complex, changing, and uncertain environments, by explicitly reasoning about the gap between their models and the real world. It first introduces a suite of novel robust optimal control formulations and algorithmic tools that permit tractable safety analysis in time-varying, multi-agent systems, as well as safe real-time robotic navigation in partially unknown environments; these approaches are demonstrated on large-scale unmanned air traffic simulation and physical quadrotor platforms. After this, it draws on Bayesian machine learning methods to translate model-based guarantees into high-confidence assurances, monitoring the reliability of predictive models in light of changing evidence about the physical system and surrounding agents. This principle is first applied to a general safety framework allowing the use of learning-based control (e.g. reinforcement learning) for safety-critical robotic systems such as drones, and then combined with insights from cognitive science and dynamic game theory to enable safe human-centered navigation and interaction; these techniques are showcased on physical quadrotors—flying in unmodeled wind and among human pedestrians—and simulated highway driving. The dissertation ends with a discussion of challenges and opportunities ahead, including the bridging of safety analysis and reinforcement learning and the need to ``close the loop'' around learning and adaptation in order to deploy increasingly advanced autonomous systems with confidence

Interactivist approach to representation in epigenetic agents

Interactivism is a vast and rather ambitious philosophical and theoretical system originally developed by Mark Bickhard, which covers plethora of aspects related to mind and person. Within interactivism, an agent is regarded as an action system: an autonomous, self-organizing, self-maintaining entity, which can exercise actions and sense their effects in the environment it inhabits. In this paper, we will argue that it is especially suited for treatment of the problem of representation in epigenetic agents. More precisely, we will elaborate on process-based ontology for representations, and will sketch a way of discussing about architectures for epigenetic agents in a general manner

How might climate change affect economic growth in developing countries ? a review of the growth literature with a climate lens

This paper reviews the empirical and theoretical literature on economic growth to examine how the four components of the climate change bill, namely mitigation, proactive (ex ante) adaptation, reactive (ex post) adaptation, and ultimate damages of climate change affect growth, especially in developing countries. The authors consider successivelythe Cass-Koopmans growth model and three major strands of the subsequent literature on growth: with multiple sectors, with rigidities, and with increasing returns. The paper finds that although the growth literature rarely addresses climate change per se, some issues discussed in the growth literature are directly relevant for climate change analysis. Notably, destruction of production factors, or decrease in factor productivity may strongly affect long-run equilibrium growth even in one-sector neoclassical growth models; climatic shocks have had large impacts on growth in developing countries because of rigidities; and the introducing increasing returns has a major impact on growth dynamics, in particular through induced technical change, poverty traps, or lock-ins. Among the most important gaps identified in the literature are lack of understanding of the channels by which shocks affect economic growth, lack of understanding of lock-ins, heavy reliance of numerical models assessing climate policies on neoclassical-type growth frameworks, and frequent use of an inappropriate"without climate change"counterfactual.Economic Growth,Economic Theory&Research,Climate Change,Achieving Shared Growth,Population Policies