Abstract
A new method of hierarchical parallel search is proposed for dealing with Markov planning/control processes in systems with uncertain information. It is based on a new concept of analyzing alternative with uncertain cost evaluation. Under definite conditions, instead of making an immediate choice based on expectation of cost at each step of the search, it is recommended to postpone the final decision until information is improved, and the uncertainty is reduced. In addition to elementary alternatives, their combinations are also considered for possible pursuit. ‘The best set’ of rough elementary solutions is to be determined at the upper of two adjacent planning/control levels, then all elementary alternatives of this set as well as their combinations, are being pursued at the lower level with a higher resolution of information, while evaluation of the ‘remaining cost’ for each of the alternatives, is being constantly improved due to the process of evolutionary uncertainty reduction. This bilevel process is easily extendible over the whole hierarchy of the system. The method is working in the graph-search and dynamic programming paradigms. The set of problems to be solved is formulated and some of them are addressed. Various applications are contemplated.
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Belostotsky, A., Meystel, A. Hierarchical parallel search for markov control with enhanced selector. J Intell Robot Syst 2, 201–227 (1989). https://doi.org/10.1007/BF00238689
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DOI: https://doi.org/10.1007/BF00238689