Many companies have been using chatbots to provide automatic, scalable, and personalized customer care services. You may think building a chatbot to this end is an easy task, just defining a sequence of messages according to the standard conversation flow, right? No, there is a lot more to it than that! In order to deliver a pleasant experience to end-users (or simply users) and to keep them engaged, the Conversational Designers need to put in quite a bit of effort. Usually, they deep-dive into diverse articles and studies to better understand the business.

While machine learning originated as a subfield of artificial intelligence--the area of computer science dedicated to creating humanlike intelligence in computers--it's expanded beyond the boundaries of A.I. into data science and expert systems. But machine learning is fundamentally different from much of what we think of as programming. When we think of a computer program (or the algorithm a program implements), we generally think of a human engineer giving a set of instructions to a computer, telling it how to handle certain inputs that will generate certain outputs. The state maintained by the program changes over time--a Web browser keeps track of which pages it's displaying and responds to user input by (ideally) reacting in a determinate and predictable fashion--but the logic of the program is essentially described by the code written by the human. Machine learning, in many of its forms, is about building programs that themselves build programs.

While highly constrained language can be used for robot control, robots that can operate as fully autonomous subordinate agents communicating via rich language remain an open challenge. Toward this end, we developed an autonomous system that supports natural, continuous interaction with the operator through language before, during, and after mission execution. The operator communicates instructions to the system through natural language and is given feedback on how each instruction was understood as the system constructs a logical representation of its orders. While the plan is executed, the operator is updated on relevant progress via language and images and can change the robot's orders. Unlike many other integrated systems of this type, the language interface is built using robust, general purpose parsing and semantics systems that do not rely on domain-specific grammars. This system demonstrates a new level of continuous natural language interaction and a novel approach to using general-purpose language and planning components instead of hand-building for the domain. Language-enabled autonomous systems of this type represent important progress toward the goal of integrating robots as effective members of human teams.