Automated learning by tactical decision systems in air combat

Lirov, Yuval; Rodin, Ervin Y.; Ghosh, Bijoy K.

doi:10.1016/0898-1221(89)90132-6

Cited by 6 publications

(8 citation statements)

References 5 publications

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“…display to pilot the information collected from these sensors, and even further as how to use the computers to process the information to aid the pilot in tactical decisions. The primarily purpose in Rodin et al; 225,226 Lirov et al; 227,228 and Rodin 229 consequently was to develop a so-called Tactical Decision Aid Expert System (TDAES).…”

Section: The Lookahead Tree Methodsmentioning

confidence: 99%

“…; 225,226 Lirov et al. ; 227,228 and Rodin. 229 This approach primarily dealt with the medium-range fight, multiple players were also considered in the combat.…”

Section: Knowledge-encoded Schemesmentioning

confidence: 99%

“…; 225,226 Lirov et al. ; 227,228 and Rodin 229 consequently was to develop a so-called Tactical Decision Aid Expert System (TDAES).…”

Section: Knowledge-encoded Schemesmentioning

confidence: 99%

“…In later development of the TDAES line, the authors discretized the differential games to a game tree, and add self-learning features in the algorithm. 228 The authors followed similar ideas as used in the AML, LT, or the later ADP, ID, etc. A discrete game tree was constructed via considering all possible control actions into the future.…”

Section: Knowledge-encoded Schemesmentioning

confidence: 99%

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Guidance and control for own aircraft in the autonomous air combat: A historical review and future prospects

Dong

Liu

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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The Autonomous Air Combat technique has been a lasting research topic for decades. However, no complete solutions seem to have appeared because of the highly dynamic and complex nature of the Autonomous Air Combat problem. In devising the Autonomous Air Combat solutions, we follow similar methodologies in the robotics community, and divide the overall scheme into two folds: the perception of other (enemy/friendly) aircraft, and the guidance/control for own aircraft. While the perception in the first fold serves as a foundation, this paper is mainly focused on the second one. Based on our survey, a review of own aircraft guidance/control in the (primarily one-to-one) Autonomous Air Combat solutions is presented. We divide different Autonomous Air Combat solutions into three groups, i.e. mathematics-based, knowledge-encoded, and learning-driven. In each group, we present the representative methods first; problem definition, solution, and a brief overview of the historical development are illustrated. We also comment on both weakness and strengths for each group/method. We point out certain technical paths/challenges that need to be addressed in the future Autonomous Air Combat development, i.e. to abstract and emulate the human pilot experiences, and to develop the online learning capabilites. Inspired by the state-of-art techniques in other similar fields (robotics, autonomous driving), we also propose potential solutions, i.e. traditional approaches enhanced by the novel data-driven technique. Via this paper, we hope to deliver an in-depth analysis of past experiences and potential challenges/solutions for the Autonomous Air Combat technique. We also advocate referring to the approaches/techniques that are utilized in other similar fields in devising the Autonomous Air Combat solutions.

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Section: The Lookahead Tree Methodsmentioning

confidence: 99%

“…; 225,226 Lirov et al. ; 227,228 and Rodin. 229 This approach primarily dealt with the medium-range fight, multiple players were also considered in the combat.…”

Section: Knowledge-encoded Schemesmentioning

confidence: 99%

“…; 225,226 Lirov et al. ; 227,228 and Rodin 229 consequently was to develop a so-called Tactical Decision Aid Expert System (TDAES).…”

Section: Knowledge-encoded Schemesmentioning

confidence: 99%

Section: Knowledge-encoded Schemesmentioning

confidence: 99%

See 2 more Smart Citations

Guidance and control for own aircraft in the autonomous air combat: A historical review and future prospects

Dong

Liu

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…Medical applications such as lymphography, prognosis of breast cancer recurrence, location of primary tumour and thyroid problem diagnosis have been reported [5,24,25]. Other applications include investment appraisal [26], forensic classification of glass fragment evidence [27], extraction of decision rules for analysis of test data for the space shuttle main engine [28], experimental generation of decision rules for the conceptual design of steel members under bending [10], soil classification [29], stock control [30], software resource analysis [31], assessing credit card applications [32], military decision making [33], dynamic system identification [34,35], engine fault diagnosis [36] and identification of the mass-spectra of complex materials [37,38,39].…”

Section: Applications Of Inductive Learningmentioning

confidence: 99%

A Review of Rules Family of Algorithms

Aksoy

2008

MCA

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In recent years, there has been a growing amount of research on inductive learning. Out of this research a number of promising algorithms have surfaced. In the paper after a brief description of knowledge acquisition, induction and inductive learning; RULES family of inductive learning algorithms, their strengths as well as weaknesses are explained and discussed. The applications of inductive learning and particularly the applications of RULES family of algorithms are overviewed.

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