Metareasoning for multi-criteria decision making using complex information sources

Justine Caylor; Jeffrey W. Herrmann; Chou Hung; Adrienne Raglin; John Richardson

doi:10.1117/12.2619418

6 June 2022 Metareasoning for multi-criteria decision making using complex information sources

Justine Caylor, Jeffrey W. Herrmann, Chou Hung, Adrienne Raglin, John Richardson

Proceedings Volume 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV; 121130Y (2022) https://doi.org/10.1117/12.2619418
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

The information that decision-makers use for command and control has uncertainty. Previous work has described different types of uncertainties, and the methods for using information to evaluate and rank alternative courses of action vary based on the type of uncertainty that occurs. Thus, when developing ways to generate automated decisions to support Soldier tactical planning, including multi-criteria decision making (MCDM) with different types (“modalities”) of uncertain information, no single method or algorithm will be optimal for all situations. Metareasoning is reasoning about reasoning, which is a type of self-adaptation, and it has been closely studied in AI and in logic due to its relevance to autonomous decision making; it is also of interest in cognitive science under the rubric of executive reasoning. A software agent or autonomous system can use metareasoning to monitor and control the procedures that it uses to process sensor information, evaluate potential courses of action, and plan its actions. This concept paper presents a metareasoning framework that can enhance artificial reasoning about uncertain information in the context of generating and ranking alternative courses of action. In this framework, the decision support agents will use rules to select the MCDM algorithms that are most appropriate for the type of information and the uncertainty modalities that are present. The rules may be curated by experts or generated from machine learning algorithms. We expect that using metareasoning will improve the ability to make complicated decisions with uncertain information.

Conference Presentation

Citation Download Citation

Justine Caylor, Jeffrey W. Herrmann, Chou Hung, Adrienne Raglin, and John Richardson "Metareasoning for multi-criteria decision making using complex information sources", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 121130Y (6 June 2022); https://doi.org/10.1117/12.2619418

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