Armchair missions to Mars: using case-based reasoning and fuzzy logic to simulate a time series model of astronaut crews

Abstract

Computer simulation of long missions in space can provide experience and predictions without the expense and risk of actual flights. Simulations are most helpful if they can model the behavior of key psychological factors of the crew over time, rather than simply predicting overall mission success. Because of the lack of experience with interplanetary trips and the problems of generalizing and adapting data on analog missions, it is not possible to formulate a set of formal rules adequate for building an expert system. Rather, a case-based reasoning approach to constructing a time series model is pursued. Even for this approach, however, the case base must be supplemented by adaptation rules. These rules of thumb are gleaned from the social science literature on small group interactions under extreme conditions of isolation and confinement. The non-quantitative nature of these rules lends itself to formulation and computation using fuzzy logic. The application domain presents several technical issues for traditional case-based reasoning: there is no natural hierarchy of parameters to use in optimizing installation and retrieval of cases, and there are large variations in behavior among similar missions. These problems are addressed by custom algorithms to keep the computations tractable and plausible. Thus, the harnessing of case-based reasoning for this practical application requires the crafting of a custom, hybrid system.