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A trace of the execution of a concurrent object-oriented program can be displayed in two-dimensions as a diagram of a non-metric finite geometry. The actions of a programs are represented by points, its objects and threads by vertical lines, its transactions by horizontal lines, its communications and resource sharing by sloping arrows, and its partial traces by rectangular figures.
Traffic simulations are generally used to forecast traffic behavior or to simulate non-player characters in computer games and virual environments. These systems are usually modeled in such a way that traffic rules are strictly followed. However, rule violations are a common part of real-life traffic and thus should be integrated into such models.
The World Wide Web (Www) offers a huge number of documents which deal with information concerning nearly any topic. Thus, search engines and meta search engines currently are the key to finding information. Search engines with crawler based indexes vary in recall and offer a very bad precision. Meta search engines try to overcome these lacks by simple methods for information extraction, information filtering and integration of heterogenous information resources. Only few search engines employ intelligent techniques in order to increase precision.
Roughness by Residuals
(2015)
Rough set theory (RST) focuses on forming posets of equivalence relations to describe sets with increasing accuracy. The connection between modal logics and RST is well known and has been extensively studied in their relation algebraic (RA) formalisation. RST has also been interpreted as a variant of intuitionistic or multi-valued logics and has even been studied in the context of logic programming.
This work presents the preliminary research towards developing an adaptive tool for fault detection and diagnosis of distributed robotic systems, using explainable machine learning methods. Autonomous robots are complex systems that require high reliability in order to operate in different environments. Even more so, when considering distributed robotic systems, the task of fault detection and diagnosis becomes exponentially difficult.
To diagnose systems, models representing the behaviour under investigation need to be developed, and with distributed robotic systems generating large amount of data, machine learning becomes an attractive method of modelling especially because of its high performance. However, with current day methods such as artificial neural networks (ANNs), the issue of explainability arises where learnt models lack the ability to give explainable reasons behind their decisions.
This paper presents current trends in methods for data collection from distributed systems, inductive logic programming (ILP); an explainable machine learning method, and fault detection and diagnosis.