Inference-Guiding for Intelligent Agents

In many applications of intelligent agents, initially given facts are not sufficient to reach a decision, and more data are needed. In that case. Inference-guiding is needed to identify the missing information and lead inference to a conclusion. This paper presents a new inferenceguiding strategy that selects the key pieces of missing information in such a way that the total cost of acquiring additional information for reaching a conclusion is the lowest. The computational experiments show that the new strategy is more effective and economical than the inference-guiding strategies currently available for the intelligent systems.