Adaptive Planning For Threat Response

Iris Cox Hayslip; Jeffrey P. Rosenking

doi:10.1117/12.969352

21 March 1989 Adaptive Planning For Threat Response

Iris Cox Hayslip, Jeffrey P. Rosenking

Proceedings Volume 1095, Applications of Artificial Intelligence VII; (1989) https://doi.org/10.1117/12.969352
Event: SPIE 1989 Technical Symposium on Aerospace Sensing, 1989, Orlando, FL, United States

Abstract

In this paper we present a design for a multilevel planning system which addresses some of the real time aspects of planning for threat response. Our approach utilizes multiple knowledge representations, and a coupled system of AI and conventional models. The motivation behind this design is to maximize the amount and depth of knowledge which can be utilized depending upon the amount of time which is available to plan. This research is part of the continued development of Grumman's Rapid Expert Assessment to Counter Threats (REACT) system; designed to aid pilots in air combat decision making. REACT consists of cooperating expert systems which communicate and are controlled by the use of a blackboard architecture. This paper concentrates on the REACT module which deals with fast response planning for combat maneuvering at low altitude over hilly terrain. REACT research has led to many interesting and potentially useful results applicable to general autonomous vehicle control architectures. In particular work on integrating the capability in REACT to reason about the tactical use of terrain has suggested guidelines for knowledge base design and data management, system and language specifications, and planner architectures pertinent to real-time coupled systems. We also describe the associated implementation progress, where the experimental planner is being integrated into the multi-language modular system.

Citation Download Citation

Iris Cox Hayslip and Jeffrey P. Rosenking "Adaptive Planning For Threat Response", Proc. SPIE 1095, Applications of Artificial Intelligence VII, (21 March 1989); https://doi.org/10.1117/12.969352

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