Using a Multi-Agent Evidential Reasoning Network as the Objective Function for an Evolutionary Algorithm
A culturally diverse group of people are now participating in military multinational coalition operations (e.g., combined air operations center, traning exercises such as Red Flag at Nellis AFB, NATO AWACS), as well as in extreme environments. Human biases and routines, capabilities, and limitations strongly influence overall system performance, whether during operations or simulations using models of humans. Many missions and environments challenge human capabilities (eg., combat stress, waiting, fatigue from long duty hours or tour of duty). This paper presents a team selection algorithm based on an evolutionary algorithm. The main difference between this and the standard EA is tha a new form of objective function is used that incorporates the beliefs and uncertainties of the data. Preliminary results show that this selection algorithm will be very beneficial for very large data sets with multiple constraints and uncertainties. This algorithm will be utilized in a military unit selection tool.
R. S. Woodley et al., "Using a Multi-Agent Evidential Reasoning Network as the Objective Function for an Evolutionary Algorithm," Proceedings of SPIE - The International Society for Optical Engineering, vol. 6563, SPIE, Apr 2007.
The definitive version is available at https://doi.org/10.1117/12.718395
Evolutionary and Bio-inspired Computation: Theory and Applications (2007: Apr. 12-13, Orlando, FL)
Electrical and Computer Engineering
Keywords and Phrases
Evidential reasoning network; Evolutionary algorithm; Multi-agent; Subjective logic
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Article - Conference proceedings
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