In this paper, we present UbuntuWorld 1.0 LTS - a platform for developing automated technical support agents in the Ubuntu operating system. Specifically, we propose to use the Bash terminal as a simulator of the Ubuntu environment for a learning-based agent and demonstrate the usefulness of adopting reinforcement learning (RL) techniques for basic problem solving and troubleshooting in this environment. We provide a plug-and-play interface to the simulator as a python package where different types of agents can be plugged in and evaluated, and provide pathways for integrating data from online support forums like AskUbuntu into an automated agent's learning process. Finally, we show that the use of this data significantly improves the agent's learning efficiency. We believe that this platform can be adopted as a real-world test bed for research on automated technical support.
On the learning side, 's work on using reinforcement learning (RL) to map natural language instructions to sequences of executable actions @cite_10 explores a similar problem setting in a Windows OS domain. However, that work focuses on the application of RL techniques to the language processing problem, and on mapping text instructions to executable actions. In contrast, our work focuses on learning task-oriented models for solving the e2eGCS problem. Thus the most relevant prior explorations into this area are complementary to our work in different ways; while the softbot work lays the groundwork for our architecture, 's work provides a report on using learning on a related but different problem.
@cite_10
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In this paper, we present a reinforcement learning approach for mapping natural language instructions to sequences of executable actions. We assume access to a reward function that defines the quality of the executed actions. During training, the learner repeatedly constructs action sequences for a set of documents, executes those actions, and observes the resulting reward. We use a policy gradient algorithm to estimate the parameters of a log-linear model for action selection. We apply our method to interpret instructions in two domains --- Windows troubleshooting guides and game tutorials. Our results demonstrate that this method can rival supervised learning techniques while requiring few or no annotated training examples.