The state of the art in automated conversational agents for enterprise (e.g. for customer support) require a lengthy design process with experts in the loop who have to figure out and specify complex conversation patterns. This demonstration looks at a prototype interface that aims to bring down the expertise required to design such agents as well as the time taken to do so. Specifically, we will focus on how a metawriter can assist the domain-writer during the design process and how complex conversation patterns can be derived from simplifying abstractions at the interface level.

Temporal logics are useful for providing concise descriptions of system behavior, and have been successfully used as a language for goal definitions in task planning. Prior works on inferring temporal logic specifications have focused on "summarizing" the input dataset - i.e., finding specifications that are satisfied by all plan traces belonging to the given set. In this paper, we examine the problem of inferring specifications that describe temporal differences between two sets of plan traces. We formalize the concept of providing such contrastive explanations, then present BayesLTL - a Bayesian probabilistic model for inferring contrastive explanations as linear temporal logic (LTL) specifications. We demonstrate the robustness and scalability of our model for inferring accurate specifications from noisy data and across various benchmark planning domains.