Optimizing Expert Systems: Heuristics for Efficiently Generating Low-Cost Information Acquisition Strategies

Abstract

We study the sequential information acquisition problem for rule-based expert systems as follows: find the information acquisition strategy that minimizes the expected cost to operate the system while maintaining the same output decisions. This problem arises for rule-based expert systems when the cost or time to collect inputs is significant and the inputs are not known until the system operates. We develop several “optimistic” heuristics to generate information acquisition strategies and study their properties. The heuristics provide choices concerning precision (i.e., how optimistic) versus computational effort. The heuristics are embedded into an informed search algorithm (based on AO*) that produces an optimal strategy and a greedy search algorithm. The search strategies are designed for situations in which rules can overlap and the cost of collecting an input may depend on the set of inputs previously collected. We study the properties of these approaches and simulate their performance on a variety of synthetic expert systems. Our results indicate that the heuristics are very precise, leading to near optimal results for greedy search and moderate search effort for optimal search.