Design of a large-scale expert system using fuzzy logic for uncertainty reasoning and its application to vision-based mobile robot navigation.

摘要

There exist in the literature today many contributions dealing with the incorporation of fuzzy logic in expert systems. But, unfortunately, much of what has been proposed can only be applied to small-scale expert systems, that is when the number of rules is in the dozens as opposed to in the hundreds. The more traditional (non-fuzzy) expert systems are able to cope with large numbers of rules by using Rete networks for maintaining matches of all the rules and all the facts. (A Rete network obviates the need to match the rules with the facts on every cycle of the inference engine.) In this dissertation, we present a more general Rete network that is particularly suitable for reasoning with fuzzy logic. The generalized Rete network consists of a cascade of three networks: the Pattern Network, the Join Network, and the Evidence Aggregation Network. The first two layers are modified versions of similar layers for the traditional Rete networks, and the last, the aggregation layer, is a new concept that allows fuzzy evidence to be aggregated when fuzzy inferences are made about the same fuzzy variable by different rules. Although the reasoning architecture we have implemented is general, it will be tested specifically in the context of vision-guided mobile robot navigation in indoor environments.