Real-Reward Testing for Probabilistic Processes (Extended Abstract)

@inproceedings{Deng2011RealRewardTF,
  title={Real-Reward Testing for Probabilistic Processes (Extended Abstract)},
  author={Yuxin Deng and Rob J. van Glabbeek and Matthew C. B. Hennessy and Carroll Morgan},
  booktitle={QAPL},
  year={2011}
}
We introduce a notion of real-valued reward testing for probabilistic processes by extending the traditional nonnegative-reward testing with negative rewards. In this richer testing framework, the may and must preorders turn out to be inverses. We show that for convergent processes with finitely many states and transitions, but not in the presence of divergenc e, the real-reward must-testing preorder coincides with the nonnegative-reward must-testing preorder. To prove this coincidence we… 
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References

SHOWING 1-10 OF 18 REFERENCES

Scalar Outcomes Suffice for Finitary Probabilistic Testing

It is proved that vectors of real-valued outcomes are unnecessary when processes are finitary, that is finitely branching and finite-state: single scalar outcomes are just as powerful.

Testing Probabilistic and Nondeterministic Processes

Testing and Refinement for Nondeterministic and Probabilistic Processes

The main contribution is a notion of reward testing, and a denotational characterization of a testing preorder that corresponds to a natural probabilistic extension of the trace model.

The Linear Time - Branching Time Spectrum II: The Semantics of Sequential Systems with Silent Moves

This paper provides a parameterized deenition of a such a preorder, such that most such pre-orders and equivalences found in the literature are obtained by a suitable instantiation of the parameters.

The Linear Time - Branching Time Spectrum II

This paper studies semantic equivalences and preorders for sequential systems with silent moves, restricting attention to the ones that abstract from successful termination, stochastic and real-time

Testing Finitary Probabilistic Processes

Weak transitions between probabilistic processes are developed, elaborate their topological properties, and express divergence in terms of partial distributions, to develop may- and must-testing preorders for recursive CSP processes with divergence.

Characterising Testing Preorders for Finite Probabilistic Processes

This paper characterises the may- and must preorders in terms of simulation, and the must preorder in terms-of-failure simulation, also gives a characterisation of both preorders using a modal logic and axiomatises both pre orders over a probabilistic version of CSP.

Testing Probabilistic Automata

This work studies testing preorders for probabilistic automata in terms of relations that are based on inclusion of trace and failure distributions, i.e., probability distributions over failures and traces that can arise in a Probabilistic computation.

A probabilistic PDL

  • D. Kozen
  • Mathematics
    J. Comput. Syst. Sci.
  • 1985
A probabilistic analog PPDL of Propositional Dynamic Logic is given and a small model property is proved and a polynomial space decision procedure for formulas involving well-structured programs is given.

Markov Decision Processes: Discrete Stochastic Dynamic Programming

  • M. Puterman
  • Computer Science
    Wiley Series in Probability and Statistics
  • 1994
Markov Decision Processes covers recent research advances in such areas as countable state space models with average reward criterion, constrained models, and models with risk sensitive optimality criteria, and explores several topics that have received little or no attention in other books.