23,786 research outputs found
Efficient state reduction methods for PLA-based sequential circuits
Experiences with heuristics for the state reduction of finite-state machines are presented and two new heuristic algorithms described in detail. Results on machines from the literature and from the MCNC benchmark set are shown. The area of the PLA implementation of the combinational component and the design time are used as figures of merit. The comparison of such parameters, when the state reduction step is included in the design process and when it is not, suggests that fast state-reduction heuristics should be implemented within FSM automatic synthesis systems
Multi-Objective Approaches to Markov Decision Processes with Uncertain Transition Parameters
Markov decision processes (MDPs) are a popular model for performance analysis
and optimization of stochastic systems. The parameters of stochastic behavior
of MDPs are estimates from empirical observations of a system; their values are
not known precisely. Different types of MDPs with uncertain, imprecise or
bounded transition rates or probabilities and rewards exist in the literature.
Commonly, analysis of models with uncertainties amounts to searching for the
most robust policy which means that the goal is to generate a policy with the
greatest lower bound on performance (or, symmetrically, the lowest upper bound
on costs). However, hedging against an unlikely worst case may lead to losses
in other situations. In general, one is interested in policies that behave well
in all situations which results in a multi-objective view on decision making.
In this paper, we consider policies for the expected discounted reward
measure of MDPs with uncertain parameters. In particular, the approach is
defined for bounded-parameter MDPs (BMDPs) [8]. In this setting the worst, best
and average case performances of a policy are analyzed simultaneously, which
yields a multi-scenario multi-objective optimization problem. The paper
presents and evaluates approaches to compute the pure Pareto optimal policies
in the value vector space.Comment: 9 pages, 5 figures, preprint for VALUETOOLS 201
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