A Knowledge-Based Framework for Effective Probabilistic Control Strategies in Signal Understanding

We describe a problem solving framework for a knowledge based approach to signal understanding. The risk of erroneous analysis makes advisable the use of well-experimented probabilistic control methods. Such methods have been used in the past in task-specific applications, such as speech. Here they are generalized to the case of a deduction system, thus becoming applicable to a wider class of problems still maintaining their effectiveness. That results in a blackboard based framework where every knowledge source is abstracted as a set of operators, that allow integration of different deductive processes independently evolved, either forward or backward. Such framework allows the use of admissible control strategies proposed in the literature.

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