Classical semantics for abstract argumentation frameworks are usually defined in terms of extensions or, more recently, labelings. That is, an argument is either regarded as accepted with respect to a labeling or not. In order to reason with a specific semantics one takes either a credulous or skeptical approach, i. e. an argument is ultimately accepted, if it is accepted in one or all labeling...

We present an approach for recognizing highlevel geo-temporal phenomena – referred as events/occurrences– from in-depth discovery of information, using geo-tagged photos, formal event models, and various context cues like weather, space, time, and people. Due to the relative availability of information, our approach automatically obtains a probabilistic measure of occurrence likelihood for the ...

in this paper, we prove the hyers-ulam stability in$beta$-homogeneous probabilistic modular spaces via fixed point method for the functional equation[f(x+ky)+f(x-ky)=f(x+y)+f(x-y)+frac{2(k+1)}{k}f(ky)-2(k+1)f(y)]for fixed integers $k$ with $kneq 0,pm1.$

Discrete-event simulation based optimization is the process of finding the optimum design of a stochastic system when the performance measure(s) could only be estimated via simulation. Randomness in simulation outputs often challenges the correct selection of the optimum. We propose an algorithm that merges Ranking and Selection procedures with a large class of random search methods for continu...

We address the problem of guiding a robot in such a way, that it can decide, based on perceived sensor data, which future actions to choose, in order to reach a goal. In order to realize this guidance, the robot has access to a (probabilistic) automaton (PA), whose nal states represent concepts, which have to be recognized in order to verify, that a goal has been achieved. The contribution of t...

A discrete model, inspired by publication activity, is introduced. It includes an increasing number of objects equipped with positive weights, which also increase with time. The random evolution of the model is driven by a weight dependent dynamics in such a way that the empirical weight distribution converges weakly with probability 1, and the limit law has a regularly varying tail. The probab...

In contrast to the usual understanding of probabilistic systems as stochastic pro-cesses, recently these systems have also been regarded as transformers of probabili-ties. In this paper, we give a natural definition of strong bisimulation for probabilisticsystems corresponding to this view that treats probability distributions as first-classcitizens. Our definition applies i...