The development of a reliable Automatic Target Recognition (ATE) system is considered a
very critical and challenging problem. Existing ATE Systems have inherent limitations
in terms of recognition performance and the ability to learn and adapt. Artificial
Intelligence Techniques have the potential to improve the performance of ATh Systems.
In this paper, we presented a novel Knowledge-Engineering tool, termed, the Automatic
Reasoning Process (ARP) , that can be used to automatically develop and maintain a
Knowledge-Base (K-B) for the ATR Systems. In its learning mode, the ARP utilizes
Learning samples to automatically develop the ATR K-B, which consists of minimum size
sets of necessary and sufficient conditions for each target class. In its operational
mode, the ARP infers the target class from sensor data using the ATh K-B System. The
ARP also has the capability to reason under uncertainty, and can support both statistical
and model-based approaches for ATR development. The capabilities of the ARP are
compared and contrasted to those of another Knowledge-Engineering tool, termed, the
Automatic Rule Induction (ARI) which is based on maximizing the mutual information. The
AR? has been implemented in LISP on a VAX-GPX workstation.