一种双向求解平行结构类问题的方法

平行结构类问题是一类适于分布式求解的人工智能问题．已有的大多数求解方法均采用预期或目标来指导自底向上的问题求解．但这些预期或目标是以局部问题求解状态为基础的，指导性较弱．尽管有的方法（如改进的ＤＶＭＴ结构）允许高层了解，但未给出明确的求解算法．本文提出一种双向求解平行结构类问题的方法，首先根据全局问题求解状态生成预期，指导自底向上的求解，然后根据新产生的假设来验证和修改预期，并用新的预期重新指导求解．该方法不仅提高了预期的指导性，而且使问题求解更为灵活．

AN INTEGRATED APPROACH TO FLAT-STRUCTURED PROBLEMS

Flat-structured Problems (FP) are an important class of Cooperative Distributed Problem Solving applications which include speech understanding, vehicle monitoring, transport dispatching and so on. So far, a number of approaches to FPs have been developed such as those in Hearsay-II and DVMT. Most of these approaches use predictions or goals to guide bottom-up problem solving. However, most predictions and goals in these approaches are based on local view of problem solving states. Although the improved architecture of DVMT allowed a high-level view, no explicit algorithm was given. This paper gives an integrated approach to FPs which makes top-down predictions from global problem solving states, guides bottom-up solving by predictions, verifies and modifies predictions by newly-created hypotheses, and guide bottom--up solving once again. This approach not only enhances the directing role of predictions obtained from global problem solving states, but also makes problem solving flexible due to the prediction verification mechanism.