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With the growth of the economy and society, enterprises, especially in theFinTech industry, have increasing demands of outbound calls for customers suchas debt collection, marketing, anti-fraud calls, and so on. But a large amountof repetitive and mechanical work occupies most of the time of human agents, sothe cost of equipment and labor for enterprises is increasing accordingly. Atthe same time, with the development of artificial intelligence technology inthe past few decades, it has become quite common for companies to use newtechnologies such as Big Data and artificial intelligence to empower outboundcall businesses. The intelligent outbound robot is a typical application of theartificial intelligence technology in the field of outbound call businesses. Itis mainly used to communicate with customers in order to accomplish a certaintarget. It has the characteristics of low cost, high reuse, and easycompliance, which has attracted more attention from the industry.  At present, there are two kinds of intelligent outbound robots in theindustry but both of them still leave large room for improvement. One kind ofthem is based on a finite state machine relying on the configuration of jumpconditions and corresponding nodes based on manual experience. This kind ofintelligent outbound robot is also called a flow-based robot. For example, theschematic diagram of the working model of a flow-based robot for debtcollection is shown in Fig.\ref{fig:label}. In each round, the robot will replyto the user with the words corresponding to each node.

Toward An Optimal Selection of Dialogue Strategies: A Target-Driven Approach for Intelligent Outbound Robots