The verification of probabilistic timed automata involves finding schedulers that optimise their nondeterministic choices with respect to the probability of a property. In practice, approaches based on model checking fail due to state-space explosion, while simulation-based techniques like statistical model checking are not applicable due to the nondeterminism. We present a new lightweight on-t...

In this paper we present a new symbolic approach to hybrid systems Hybrid systems are systems con taining both continuous and discrete changing quantities We model hybrid systems using hybrid automata Hybrid automata extend the classical notion of nite state machines by combining di erential equations to model the dynamic behavior of systems with a nite control In contrast to other approaches w...

We propose a model for synaptic plasticity according to the Spike Timing Dependent Plasticity (STDP) theory using Linear Hybrid Automata (LHA). We first present a compositional LHA model in which each component corresponds to some process in STDP. We then abstract this model into a monolithic LHA model in order to enable formal analysis using hybrid model checking. We discuss how the availabili...

We propose algorithms that use Markov chain techniques to generate acyclic automata uniformly at random. We first consider deterministic, accessible and acyclic automata, then focus on the class of minimal acyclic automata. In each case we explain how to define random local transformations that describe an ergodic and symmetric Markov chain: the distribution of the automaton obtained after T ra...

We start by introducing a notion of hybrid automata with a clear identiication of the signature component. Hybrid automata intermix discrete and continuous behavior, extending non-deterministic automata with attributes that evolve continuously with time according to evolution laws. Adopting a categorial approach to aggregating and interconnecting hybrid automata we look at a suitable deenition ...

We use linear hybrid automata to deene linear approximations of the phase portraits of nonlinear hybrid systems. The approximating automata can be analyzed automatically using the symbolic model checker HyTech. We demonstrate the technique through the study of predator-prey systems, where we compute population bounds for both species. We also identify a class of nonlinear hybrid automata for wh...

In this paper we develop data mining techniques to predict 3D contact potentials among protein residues (or amino acids) based on the hierarchical nucleationpropagation model of protein folding. We apply a hybrid approach, using a Hidden Markov Model to extract folding initiation sites, and then apply association mining to discover contact potentials. The new hybrid approach achieves accuracy r...

multi agent markov decision processes (mmdps), as the generalization of markov decision processes to the multi agent case, have long been used for modeling multi agent system and are used as a suitable framework for multi agent reinforcement learning. in this paper, a generalized learning automata based algorithm for finding optimal policies in mmdp is proposed. in the proposed algorithm, mmdp ...

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...

This paper discusses the use of hybrid automata to specify and verify embedded distributed systems that consist of both discrete and continuous components The basis of the evaluation is an automotive control system which controls the height of an automobile by pneumatic suspension It has been proposed by BMW AG as a case study taken from a current industrial development Essential parts of the s...