A prime application area for expert system technology is that of automatic target recognition. Existing target recognizers by design are not capable of exploiting all of the information contained in a scene during their classification process (contextual cueing of targets for example). This paper describes a Tactical Expert System (TESS) research project being developed at the Georgia Tech Research Institute. Developed on a Symbolics Lisp machine, TESS is a prototype expert system for interpreting the tactical implications of infrared scenes. This paper highlights the basic target recognition features of TESS and describes several areas of activity research being performed to enhance the TESS system.

Tess = The Tactical Expert System

KIMS is an acronym for a K nowledge-Based I mage M anagement S ystem developed in the Robotics and Artificial Intelligence Laboratory ( RAIL ) at North Carolina A&T State University. KIMS model architecture consists of rules which are developed through statistical experimentation with thresholding and quality control chart algorithms. The control architecture of KIMS is driven by the pattern of these rules. KIMS can analyze features of an X-ray image of a manufactured product such as printed circuit board in a real-time mode and make decisions on whether there are defect symptoms in the product. In this paper we present the current performance of KIMS in product inspection decision.

The performance of KIMS in image recognition tasks

The performance of autonomous vehicle systems is currently limited by their inability to accurately analyze their surrounding environment In order to function in a dynamic real world environment, an autonomous vehicle system must be capable of interpreting terrain based upon predetermined mission goals This paper describes a concept of knowledge-based terrain analysis currently being developed to support the information needs of an autonomous helicopter system The terrain analysis system consists of five integrated processing stages Each process is discussed in detail and supported by a number of mission oriented examples Int roduction One of the most unique system concepts being explored today is that of the development of totally autonomous land, sea, and air vehicles [1 - 3] For military applications, autonomous vehicles provide a mechanism for removing humans from modern day battlefields while not impacting the tactical capabilities of the battle force Civilian applications for such a device entail the investigation of hazardous areas (eg nuclear reactors and toxic waste dumps), search over large areas (eg downed aircraft or lost campers) and monitoring of large facilities (eg robot security police) As military funding for these systems is currently at an alltime high, a variety of autonomous system concepts are continually being presented in the technical literature The primary limitation of an autonomous vehicle still lies in its inability to accurately analyze surrounding terrain information in order to generate tactical plans of action This paper describes activities being performed in the Artificial Intelligence Branch of the Georgia Tech Research Institute on knowledge-based terrain analysis, particularly in support of the autonomous helicopter programSystem OverviewThe knowledge-based tactical terrain analysis system consists of five separate processing phases: [1] image segmentation, [2] region classification, [3] visual model, [4] route planning, [5] threat location and coverage Each processing phase is briefly discussed in the following subsections with actual image results given where appropriate

Tracy Bruce

Papers

Knowledge-Based Tactical Terrain Analysis