Abstract. Insight in the global structure of a state space is of great help in the analysis of the underlying process. We advocate the use of visualization for this purpose and present a new method to visualize the structure of very large state spaces. The method uses a clustering method to obtain

We present a novel framework for exploring very large state spaces of concurrent reactive systems. Our framework exploits applicationindependent heuristics using genetic algorithms to guide a state-space search towards error states. We have implemented this framework in conjunction with Veri

that adding new state variables inevitably results in much larger (often exponential) increases in the size of the state space, which can make solving superficially small problems impossible. A large state space makes optimal SDP solutions computationally expensive to compute because optimal SDP techniques

at the state-transition level. However, and especially when analysing industrial-scale systems, workstation memory and compute power is often overwhelmed by the sheer number of states. This chapter explores an array of techniques for analysing stochastic performance models with large state spaces. We

We present a novel framework for exploring very large state spaces of concurrent reactive systems. Our framework exploits application-independent heuristics using genetic algorithms to guide a state-space search towards error states. We have implemented this framework in conjunction with Veri

For Hidden Markov Models (HMMs) with fully connected transition models, the three fundamental problems of evaluating the likelihood of an observation sequence, estimating an optimal state sequence for the observations, and learning the model parameters, all have quadratic time complexity

This paper describes symbolic techniques for the construction, representation and analysis of large, probabilistic systems. Symbolic approaches derive their eciency by exploiting high-level structure and regularity in the models to which they are applied, increasing the size of the state spaces

of short-term versus long-term gains. We study a simple MDP in which the agent must constantly decide between exploratory jumps and local reward mining in state space. The number of policies to choose from grows exponentially with the size of the state space, N . We view the expected returns as defining

Bayesian networks (BNs) are a compact representation of a joint probability distribution. In this paper we show how they can be used for modeling other agents in the environment. More precisely we will have special attention to the problem of large state spaces and incomplete information

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