Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: SUMMARY Planning a sequence of robot actions is especially difficult when the outcome of actions is uncertain, as is inevitable when interacting with the physical environment.   consider the case of finite state and action spaces where actions can be modeled as Markov transitions. Finding a plan that achieves a desired state with maximum probability is known to be an NP-Complete problem.  consider two algorithms: an exponential-time algorithm that maximizes probability, and a polynomial-time algorithm that maximizes a lor bound on the probability. As these algorithms trade off plan time for plan quality,  compare their performance on a mechanical system for orienting parts. Our results lead us to identify two properties of stochastic actions that can be used to choose beten these planning algorithms for other applications.
['Alan D. Christiansen', 'Ken Goldberg']
I am writing a research paper on a idea Comparing two algorithms for automatic planning by robots in stochastic environments
[{"from": "human", "value": "I am writing a research paper on a idea Comparing two algorithms for automatic planning by robots in stochastic environments"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: SUMMARY Planning a sequence of robot actions is especially difficult when the outcome of actions is uncertain, as is inevitable when interacting with the physical environment.   consider the case of finite state and action spaces where actions can be modeled as Markov transitions. Finding a plan that achieves a desired state with maximum probability is known to be an NP-Complete problem.  consider two algorithms: an exponential-time algorithm that maximizes probability, and a polynomial-time algorithm that maximizes a lor bound on the probability. As these algorithms trade off plan time for plan quality,  compare their performance on a mechanical system for orienting parts. Our results lead us to identify two properties of stochastic actions that can be used to choose beten these planning algorithms for other applications."}]