Case-based reasoning for mission planning, control, and decision-making

Case-based reasoning (CBR) is a powerful reasoning paradigm for many application domains like planning, diagnosis, classification, and decision making. Recognizing solutions of past instances which are similar to the problem in hand is the central concept of CBR. Accordingly, the main research issues in CBR are efficient indexing, retrieval, and evaluation of cases. Generalization of indices has been a major concern as it directly influences the size of casebases and the ability to recognize the right candidate cases. This dissertation work presents a novel indexing scheme--using fuzzy sets to represent case indices and fuzzy aggregation operators to evaluate case matches. The proposed scheme, REFIC (REasoning from Fuzzy Indexed Cases), provides a flexible and transparent scheme to generalize case indices leading to smaller casebases. A hierarchical aggregation of different index matches is suggested for case evaluation. Also, for continuous variable domains, it is proposed to combine the solutions of a small subset of best matching cases as opposed to the conventional approach of selecting and modifying a single best one. These schemes are demonstrated by implementing a case-based navigation planner for autonomous underwater vehicles (AUVs). This navigation planner comprises of an annotated map database, a case-based path planner, and a hybrid fuzzy-CBR based reactive navigation module. The annotated map database provides a general framework for modeling the navigational environment. Annotations attached to objects and geometrical query handling are two main features of this database. Using this system as a spatial casebase, an off-line path planning system for AUV missions is designed. The obstacle avoidance module employs CBR to dynamically select promising directions of movement and to activate a subset of navigational behaviors. This reactive navigation scheme has been found to be very robust under noisy sensor data and complex obstacle distribution patterns.