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Proceedings Paper

Intent and error recognition as part of a knowledge-based cockpit assistant
Author(s): Michael Strohal; Reiner Onken
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Paper Abstract

With the Crew Assistant Military Aircraft (CAMA) a knowledge- based cockpit assistant system for future military transport aircraft is developed and tested to enhance situation awareness. Human-centered automation was the central principal for the development of CAMA, an approach to achieve advanced man-machine interaction, mainly by enhancing situation awareness. The CAMA-module Pilot Intent and Error Recognition (PIER) evaluates the pilot's activities and mission events in order to interpret and understand the pilot's actions in the context of the flight situation. Expected crew actions based on the flight plan are compared with the actual behavior shown by the crew. If discrepancies are detected the PIER module tries to figure out, whether the deviation was caused erroneously or by a sensible intent. By monitoring pilot actions as well as the mission context, the system is able to compare the pilot's action with a set of behavioral hypotheses. In case of an intentional deviation from the flight plan, the module checks, whether the behavior matches to the given set of behavior patterns of the pilot. Intent recognition can increase man-machine synergy by anticipating a need for assistance pertinent to the pilot's intent without having a pilot request. The interpretation of all possible situations with respect to intent recognition in terms of a reasoning process is based on a set of decision rules. To cope with the need of inferencing under uncertainty a fuzzy-logic approach is used. A weakness of the fuzzy-logic approach lies in the possibly ill-defined boundaries of the fuzzy sets. Self-Organizing Maps (SOM) as introduced and elaborated on by T. Kohonen are applied to improve the fuzzy set data and rule base complying with observed pilot behavior. Hierarchical cluster analysis is used to locate clusters of similar patterns in the maps. As introduced by Pedrycz, every feature is evaluated using fuzzy sets for each designated cluster. This approach allows to generate fuzzy sets and rules by use of a user-friendly and easily adjustable environment of development tools for data interpretation.

Paper Details

Date Published: 25 March 1998
PDF: 13 pages
Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); doi: 10.1117/12.304818
Show Author Affiliations
Michael Strohal, Univ. der Bundeswehr Muenchen (Germany)
Reiner Onken, Univ. der Bundeswehr Muenchen (Germany)

Published in SPIE Proceedings Vol. 3390:
Applications and Science of Computational Intelligence
Steven K. Rogers; David B. Fogel; James C. Bezdek; Bruno Bosacchi, Editor(s)

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