Formal methods of automated reasoning for situational estimation

James Llinas

doi:10.1117/12.276120

16 June 1997 Formal methods of automated reasoning for situational estimation

James Llinas

Proceedings Volume 3067, Sensor Fusion: Architectures, Algorithms, and Applications; (1997) https://doi.org/10.1117/12.276120
Event: AeroSense '97, 1997, Orlando, FL, United States

Abstract

Most research and prototype development of automated methods for situational estimation in the data fusion community have applied heuristic approaches coupled to techniques for uncertainty management. Reasoning theorists would label these methods as those of the parametric reasoning class. Such methods are reasonable when the so-called 'closed world' assumption can be confidently applied (ability to full pre- specify expected conditions) which might have been reasonable in the 'Soviet Era' but would appear fragile/brittle for current-day application. Motivated in part by these considerations and by the need to consider much more cost- effective knowledge-based-system development in an era of declining budgets, this paper offers some discussion on the applicability of more formal methods of reasoning for KBS. It is concluded that strictly formal methods for real-world applications require yet further theoretical development but that movement toward formalization is possible.

Citation Download Citation

James Llinas "Formal methods of automated reasoning for situational estimation", Proc. SPIE 3067, Sensor Fusion: Architectures, Algorithms, and Applications, (16 June 1997); https://doi.org/10.1117/12.276120

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available