The data tides are changing. Between the influx of regulations, Apple's new privacy controls, and greater concern around privacy issues, it's clear enterprises won't be able to collect and leverage data as they have been for much longer. Streamlytics, a Miami-based data provider founded in 2018, believes letting users sell their data could be part of the solution. The company has already collected more than 75 million data points this way and says it aims to "democratize" data by giving users more control and then selling the user-supplied data to enterprises -- from media conglomerates to consumer goods companies. Streamlytics is particularly focused on working with Black users in the U.S. and on getting underrepresented data into AI training models.

Modern sales is more about long-term customer relationships than big, one-and-done deals. This has always been true -- lower customer churn decreases marketing costs -- but it's especially true today, as everything from designer clothing to SaaS products moves toward a subscription model. "The best way to be successful in sales is to know yourself, know your customer and know how you create strong relationships with other people," Samantha Harrington opined in Forbes. "Once you've built that relationship, shown you care, and earned their trust, you are on the road to making [a prospect] a customer," Lee Ann Obringe wrote in HowStuffWorks. Machine-learning algorithms can master repetitive, predictable, and, in a word, mechanical tasks -- but despite the future foretold by Her, artificial intelligence hasn't yet learned to empathize or make jokes.

My research represents an approach to integrating planning and execution in time-sensitive environments. The primary focus is on a problem called continual on-line planning. New goals arrive stochastically during execution, the agent issues actions for execution one at a time, and the environment is otherwise deterministic. My dissertation will address this setting in three stages: optimizing total goal achievement time, handling on-line goal arrival during planning or execution, and adapting to changes in state also during planning or execution. My current approach to this problem is based on incremental heuristic search. The two central issues are the decision of which partial plans to elaborate during search and the decision of when to issue an action for execution. I have proposed an extension of Russell and Wefald's decision-theoretic A* algorithm that is not limited by assumptions of an admissible heuristic like DTA*. This algorithm, Decision Theoretic On-line Continual Search (DTOCS), handles the complexities of the on-line setting by balancing deliberative planning and real-time response.

This paper presents an approach to integrating planning and execution in time-sensitive environments. We present a simple setting in which to consider the issue, that we call continual on-line planning. New goals arrive stochastically during execution, the agent issues actions for execution one at a time, and the environment is otherwise deterministic. We take the objective to be a form of time-dependent partial satisfaction planning reminiscent of discounted MDPs: goals offer reward that decays over time, actions incur fixed costs, and the agent attempts to maximize net utility. We argue that this setting highlights the central challenge of time-aware planning while excluding the complexity of non-deterministic actions. Our approach to this problem is based on real-time heuristic search. We view the two central issues as the decision of which partial plans to elaborate during search and the decision of when to issue an action for execution. We propose an extension of Russell and Wefald's decision-theoretic A* algorithm that can cope with our inadmissible heuristic. Our algorithm, DTOCS, handles the complexities of the on-line setting by balancing deliberative planning and real-time response.