Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In an earlier paper, the authors introduced the notion of safety control of stochastic discrete event systems (DESs), modeled as controlled Markov chains. Safety was specified as an upper bound on the components of the state probability distribution, and the class of irreducible and aperiodic Markov chains re analyzed relative to this safety criterion. Under the assumption of complete state observations: 1) the authors identified the set of all state-feedback controllers that enforce the safety specification, for all safe initial probability distributions and 2) for any given state-feedback controller, the authors constructed the maximal invariant safe set (MISS). , the authors extend the work in several ways: 1) safety is specified in terms of both upper and lor bounds; 2)  consider a larger class of Markov chains that includes reducible and periodic chains; 3)  present a more general iterative algorithm for computing the MISS, which is quite flexible in its initialization; 4)  obtain an explicit upper bound for the number of iterations needed for the algorithm to terminate. Note to Practitioners-The paper studies "safety" control of stochastic systems modeled as Markov chains. Safety is defined as a requirement that the probability distribution in each state remain bounded beten an upper and a lor bound. For example, a financial investment policy should be such that the probability of ever being bankrupt is bounded below by a positive number. Prior works on control of Markov chains have addressed optimality but not safety. A condition is obtained under which a controlled Markov chain is guaranteed to be safe at all times. For those chains that do not satisfy such a condition, a maximal subset of the safe set of distributions is computed so that if the chain is initialized with a distribution in that maximal subset, it remains safe all the times. A condition is obtained under which such a maximal set is nonempty. The computation of such a maximal set is iterative and  provide a condition under which the computation terminates in a finite number of iterations. Manufacturing system examples are included to illustrate the results.
['Ari Arapostathis', 'Ratnesh Kumar', 'Shun-Pin Hsu']
I am writing a research paper on a idea Control of Markov chains with safety bounds
[{"from": "human", "value": "I am writing a research paper on a idea Control of Markov chains with safety bounds"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In an earlier paper, the authors introduced the notion of safety control of stochastic discrete event systems (DESs), modeled as controlled Markov chains. Safety was specified as an upper bound on the components of the state probability distribution, and the class of irreducible and aperiodic Markov chains re analyzed relative to this safety criterion. Under the assumption of complete state observations: 1) the authors identified the set of all state-feedback controllers that enforce the safety specification, for all safe initial probability distributions and 2) for any given state-feedback controller, the authors constructed the maximal invariant safe set (MISS). , the authors extend the work in several ways: 1) safety is specified in terms of both upper and lor bounds; 2)  consider a larger class of Markov chains that includes reducible and periodic chains; 3)  present a more general iterative algorithm for computing the MISS, which is quite flexible in its initialization; 4)  obtain an explicit upper bound for the number of iterations needed for the algorithm to terminate. Note to Practitioners-The paper studies \"safety\" control of stochastic systems modeled as Markov chains. Safety is defined as a requirement that the probability distribution in each state remain bounded beten an upper and a lor bound. For example, a financial investment policy should be such that the probability of ever being bankrupt is bounded below by a positive number. Prior works on control of Markov chains have addressed optimality but not safety. A condition is obtained under which a controlled Markov chain is guaranteed to be safe at all times. For those chains that do not satisfy such a condition, a maximal subset of the safe set of distributions is computed so that if the chain is initialized with a distribution in that maximal subset, it remains safe all the times. A condition is obtained under which such a maximal set is nonempty. The computation of such a maximal set is iterative and  provide a condition under which the computation terminates in a finite number of iterations. Manufacturing system examples are included to illustrate the results."}]