Generating Explanations for Goal‐Based Decision Making*
介绍了一个名为VOTE的自动决策程序，它能用英语和法语生成自然语言解释，模拟美国众议院议员在国会投票中的决策，并建议将该模型应用于商业战略规划、投资和营销。
ABSTRACT As information systems make the transition from decision support to decision making, there will be a concomitant need for the programs to explain or justify their actions. Without such explanations, humans will not readily cede authority to a machine. This paper describes an automated decision‐making program, VOTE, which generates natural language explanations for its decisions in both English and French. The program domain is Congressional roll call voting. VOTE simulates voting decisions of specific members of the United States House of Representatives on given bills. VOTE's natural language generation facility is also used by the underlying databases to interpret the knowledge representations. These underlying knowledge representations are described and applications of this decision making model to the general business domains of strategic planning, investment, and marketing are suggested.