Locomotion characteristics are often recorded within bounded spaces, a constraint which introduces geometry-specific biases and potentially complicates the inference of behavioural features from empirical observations. We describe how statistical properties of an uncorrelated random walk, namely the steady-state stopping location probability density and the empirical step probability density, are affected by enclosure in a bounded space. The random walk here is considered as a null model for an organism moving intermittently in such a space, that is, the points represent stopping locations and the step is the displacement between them. Closed-form expressions are derived for motion in one dimension and simple two-dimensional geometries, in addition to an implicit expression for arbitrary (convex) geometries. For the particular choice of no-go boundary conditions, we demonstrate that the empirical step distribution is related to the intrinsic step distribution, i.e. the one we would observe in unbounded space, via a multiplicative transformation dependent solely on the boundary geometry. This conclusion allows in practice for the compensation of boundary effects and the reconstruction of the intrinsic step distribution from empirical observations.
Modern society has become increasingly reliant on the omnipresent cyber-physical systems (CPSs), making it paramount that the contemporary autonomous and decentralized CPSs (e. g., robots, drones and self-driving cars) act reliably despite their exposure to a variety of run-time uncertainties. The sources of uncertainties could be internal, i. e., originating from the systems themselves, or external-unpredictable environments. Self-adaptive CPSs (SACPSs) modify their behavior or structure at run-time in response to the uncertainties mentioned above. The adaptation relies on gained knowledge from the observations that the SACPSs make during their operation. As a result, to build the knowledge, the need for run-time observations aggregation and reasoning emerges since the observations made by decentralized CPSs are uncertain, partial, and potentially conflicting. In response, in this paper, we propose a novel methodological approach for deriving or aggregating knowledge from uncertain observations in SACPSs utilizing the Subjective Logic. The effectiveness of the proposed approach is demonstrated through extensive evaluation on an in-house, multi-agent system from the robotics domain.
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