Signal-to-symbol transformation: Reasoning in the HASP/SIAP program

In the past fifteen years, artificial intelligence scientists have built several signal interpretation, or understanding, programs. These programs have combined "low" level signal processing algorithms with knowledge representation and reasoning techniques used in knowledge-based, or expert, systems. [4] They have shown how the use of task domain knowledge combined with symbolic manipulation techniques can be of use in making signal understanding systems more effective and efficient. HASP/SIAP is one such program that tries to interpret the meaning of passively collected sonar data. In this paper we explore some of the AI techniques that contribute in the "understanding" process. We also describe the organization of HASP/SIAP system as an example of a programming framework that show promise for applications in a class of similar problems.