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Neural networks are becoming a popular tool for solving many real-world problems such as object recognition and machine translation, thanks to its exceptional performance as an end-to-end solution. However, neural networks are complex black-box models, ...

Combining probabilistic choice and non-determinism is a long standing problem in denotational semantics. From a category theory perspective, the problem stems from the absence of a distributive law of the powerset monad over the distribution monad. In ...

Complex event recognition (CER) applications exhibit various types of uncertainty, ranging from incomplete and erroneous data streams to imperfect complex event patterns. We review CER techniques that handle, to some extent, uncertainty. We examine ...

Abstraction refinement techniques in probabilistic model checking are prominent approaches for verification of very large or infinite-state probabilistic concurrent systems. At the core of the refinement step lies the implicit or explicit analysis of a ...

We study finite automata running over infinite binary trees. A run of such an automaton over an input tree is a tree labeled by control states of the automaton: the labeling is built in a top-down fashion and should be consistent with the transitions of ...

For deployed systems, software fault detection can be challenging. Generally, faulty behaviors are detected based on execution logs, which may contain a large volume of execution traces, making analysis extremely difficult. This paper investigates and ...

SysML activity diagrams are OMG/INCOSE standard models for specifying and analyzing systems' behaviors. In this paper, we propose an abstraction approach for this type of diagrams that helps to mitigate the state-explosion problem in probabilistic model ...

Working principle of the probabilistic automaton is based on the state transition probability [4], [6]. Construction of the automaton depends on its purpose. We assume that all transitions probabilities are constant[3]. In situations where low levels of ...

Probabilistic ω-automata are variants of nondeterministic automata over infinite words where all choices are resolved by probabilistic distributions. Acceptance of a run for an infinite input word can be defined using traditional acceptance criteria for ...

Process mining uses event logs to learn and reason about business process models. Existing algorithms for mining the control-flow of processes in general do not take into account the probabilistic nature of the underlying process, which affects the ...

By using music written in a certain style as training data, parameters can be calculated for Markov chains and hidden Markov models to capture the musical style of the training data as mathematical models.

We investigate and compare the descriptional power of unary probabilistic and nondeterministic automata (pfa's and nfa's, respectively). We show the existence of a family of languages hard for pfa's in the following sense: For any positive integer d, ...

We present a fully automated technique for compositional verification of probabilistic systems. Our approach builds upon a recently proposed assume-guarantee framework for probabilistic automata, in which assumptions and guarantees are probabilistic ...

The nondeterministic quantum finite automaton (NQFA) is the only known case wherea one-way quantum finite automaton (QFA) model has been shown to be strictly superiorin terms of language recognition power to its probabilistic counterpart. We give ...

In this article, we introduce the model of finite state probabilistic monitors (FPM), which are finite state automata on infinite strings that have probabilistic transitions and an absorbing reject state. FPMs are a natural automata model that can be ...

This paper constructs an operant conditioning learning system based on fuzzy and probabilistic automata, which used for on-line self-learning of fuzzy rules. The learning system can learn its rules on line by interaction with environment, and achieve ...

This paper constructs a learning probabilistic automata (PA) model with response of operant conditioning (OC) behavior, which used for simulating skinner-pigeon experiment. The PA model with OC is a form of animal learning in that it allows an agent to ...

We consider the Probabilistic I/O Automata framework, for which we address the verification of reachability properties in case the rates (also called delay parameters) are unspecified. We show that the problem of finding (or even approximating) the ...

We introduce a notion of finite testing, based on statistical hypothesis tests, via a variant of the well-known trace machine. Under this scenario, two processes are deemed observationally equivalent if they cannot be distinguished by any finite test. ...

Probabilistic automata (PAs) constitute a general framework for modeling and analyzing discrete event systems that exhibit both nondeterministic and probabilistic behavior, such as distributed algorithms and network protocols. The behavior of PAs is ...