By Raju Vegesna The past decade has been a true testament to the success of many inventive technologies. The biggest wave we witnessed was the advent of artificial intelligence and machine learning. NLP & Conversational AI In the early 2010s, consumer natural language processing (NLP) allowed us to talk to our phones and control smart-home appliances reliably. Many people expected NLP to explode in other domains, but it never really materialized, because of either poor implementations or a focus on other types of development. Over the next decade, NLP will be put to use in complex software to lower the barrier to entry.

We consider voting situations in which some candidates may turn out to be unavailable. When determining availability is costly (e.g., in terms of money, time, or computation), voting prior to determining candidate availability and testing the winner's availability after the vote may be beneficial. However, since few voting rules are robust to candidate deletion, winner determination requires a number of such availability tests. We outline a model for analyzing such problems, defining robust winners relative to potential candidate unavailability. We assess the complexity of computing robust winners for several voting rules. Assuming a distribution over availability, and costs for availability tests/queries, we describe algorithms for computing optimal query policies, which minimize the expected cost of determining true winners.