4,730 research outputs found
Prospects of a mathematical theory of human behavior in complex man-machine systems tasks
A hierarchy of human activities is derived by analyzing automobile driving in general terms. A structural description leads to a block diagram and a time-sharing computer analogy. The range of applicability of existing mathematical models is considered with respect to the hierarchy of human activities in actual complex tasks. Other mathematical tools so far not often applied to man machine systems are also discussed. The mathematical descriptions at least briefly considered here include utility, estimation, control, queueing, and fuzzy set theory as well as artificial intelligence techniques. Some thoughts are given as to how these methods might be integrated and how further work might be pursued
Compositional Falsification of Cyber-Physical Systems with Machine Learning Components
Cyber-physical systems (CPS), such as automotive systems, are starting to
include sophisticated machine learning (ML) components. Their correctness,
therefore, depends on properties of the inner ML modules. While learning
algorithms aim to generalize from examples, they are only as good as the
examples provided, and recent efforts have shown that they can produce
inconsistent output under small adversarial perturbations. This raises the
question: can the output from learning components can lead to a failure of the
entire CPS? In this work, we address this question by formulating it as a
problem of falsifying signal temporal logic (STL) specifications for CPS with
ML components. We propose a compositional falsification framework where a
temporal logic falsifier and a machine learning analyzer cooperate with the aim
of finding falsifying executions of the considered model. The efficacy of the
proposed technique is shown on an automatic emergency braking system model with
a perception component based on deep neural networks
Exploration and Exploitation of Victorian Science in Darwin's Reading Notebooks
Search in an environment with an uncertain distribution of resources involves
a trade-off between exploitation of past discoveries and further exploration.
This extends to information foraging, where a knowledge-seeker shifts between
reading in depth and studying new domains. To study this decision-making
process, we examine the reading choices made by one of the most celebrated
scientists of the modern era: Charles Darwin. From the full-text of books
listed in his chronologically-organized reading journals, we generate topic
models to quantify his local (text-to-text) and global (text-to-past) reading
decisions using Kullback-Liebler Divergence, a cognitively-validated,
information-theoretic measure of relative surprise. Rather than a pattern of
surprise-minimization, corresponding to a pure exploitation strategy, Darwin's
behavior shifts from early exploitation to later exploration, seeking unusually
high levels of cognitive surprise relative to previous eras. These shifts,
detected by an unsupervised Bayesian model, correlate with major intellectual
epochs of his career as identified both by qualitative scholarship and Darwin's
own self-commentary. Our methods allow us to compare his consumption of texts
with their publication order. We find Darwin's consumption more exploratory
than the culture's production, suggesting that underneath gradual societal
changes are the explorations of individual synthesis and discovery. Our
quantitative methods advance the study of cognitive search through a framework
for testing interactions between individual and collective behavior and between
short- and long-term consumption choices. This novel application of topic
modeling to characterize individual reading complements widespread studies of
collective scientific behavior.Comment: Cognition pre-print, published February 2017; 22 pages, plus 17 pages
supporting information, 7 pages reference
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