Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  consider termination of probabilistic programs with real-valued variables. The questions concerned are: 1. qualitative ones that ask (i) whether the program terminates with probability 1 (almost-sure termination) and (ii) whether the expected termination time is finite (finite termination); 2. quantitative ones that ask (i) to approximate the expected termination time (expectation problem) and (ii) to compute a bound B such that the probability to terminate after B steps decreases exponentially (concentration problem). To solve these questions,  utilize the notion of ranking supermartingales which is a porful approach for proving termination of probabilistic programs. In detail,  focus on algorithmic synthesis of linear ranking-supermartingales over affine probabilistic programs (APP's) with both angelic and demonic non-determinism. An important subclass of APP's is LRAPP which is defined as the class of all APP's over which a linear ranking-supermartingale exists. Our main contributions are as follows. Firstly,  show that the membership problem of LRAPP (i) can be decided in polynomial time for APP's with at most demonic non-determinism, and (ii) is NP-hard and in PSPACE for APP's with angelic non-determinism; moreover, the NP-hardness result holds already for APP's without probability and demonic non-determinism. Secondly,  show that the concentration problem over LRAPP can be solved in the same complexity as for the membership problem of LRAPP. Finally,  show that the expectation problem over LRAPP can be solved in 2EXPTIME and is PSPACE-hard even for APP's without probability and non-determinism (i.e., deterministic programs). Our experimental results demonstrate the effectiveness of our approach to ansr the qualitative and quantitative questions over APP's with at most demonic non-determinism.
['Krishnendu Chatterjee', 'Hongfei Fu', 'Petr Novotný', 'Rouzbeh Hasheminezhad']
I am writing a research paper on a idea Algorithmic analysis of qualitative and quantitative termination problems for affine probabilistic programs
[{"from": "human", "value": "I am writing a research paper on a idea Algorithmic analysis of qualitative and quantitative termination problems for affine probabilistic programs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  consider termination of probabilistic programs with real-valued variables. The questions concerned are: 1. qualitative ones that ask (i) whether the program terminates with probability 1 (almost-sure termination) and (ii) whether the expected termination time is finite (finite termination); 2. quantitative ones that ask (i) to approximate the expected termination time (expectation problem) and (ii) to compute a bound B such that the probability to terminate after B steps decreases exponentially (concentration problem). To solve these questions,  utilize the notion of ranking supermartingales which is a porful approach for proving termination of probabilistic programs. In detail,  focus on algorithmic synthesis of linear ranking-supermartingales over affine probabilistic programs (APP's) with both angelic and demonic non-determinism. An important subclass of APP's is LRAPP which is defined as the class of all APP's over which a linear ranking-supermartingale exists. Our main contributions are as follows. Firstly,  show that the membership problem of LRAPP (i) can be decided in polynomial time for APP's with at most demonic non-determinism, and (ii) is NP-hard and in PSPACE for APP's with angelic non-determinism; moreover, the NP-hardness result holds already for APP's without probability and demonic non-determinism. Secondly,  show that the concentration problem over LRAPP can be solved in the same complexity as for the membership problem of LRAPP. Finally,  show that the expectation problem over LRAPP can be solved in 2EXPTIME and is PSPACE-hard even for APP's without probability and non-determinism (i.e., deterministic programs). Our experimental results demonstrate the effectiveness of our approach to ansr the qualitative and quantitative questions over APP's with at most demonic non-determinism."}]