Human-AI Complex Task Planning

Abstract

The process of complex task planning is ubiquitous and arises in a variety of compelling applications. A few leading examples include designing a personalized course plan or trip plan, designing music playlists/work sessions in web applications, or even planning routes of naval assets to collaboratively discover an unknown destination. For all of these aforementioned applications, creating a plan requires satisfying a basic construct, i.e., composing a sequence of sub-tasks (or items) that optimizes several criteria and satisfies constraints. For instance, in course planning, sub-tasks or items are core and elective courses, and degree requirements capture their complex dependencies as constraints. In trip planning, sub-tasks are points of interests (POIs) and constraints represent time and monetary budget, or user-specified requirements. Needless to say, task plans are to be individualized and designed considering uncertainty. When done manually, the process is human-intensive and tedious, and unlikely to scale. The goal of my research is to present computational frameworks that synthesize the capabilities of human and AI algorithms to enable task planning at scale while satisfying multiple objectives and complex constraints.I present a set of computational frameworks for automated task planning as a sequence generation problem that requires minimal inputs from the end users and produces personalized task plans in an uncertain environment while satisfying multiple objectives and complex constraints. At the core, I propose a set of multi-objective optimization problems with constraints, solving which will generate task plans as a sequence of sub-tasks that are highly dependent and optimize the underlying problems. From the algorithmic standpoint, I design novel algorithms by adapting Reinforcement Learning (RL) and discrete optimization-based techniques with theoretical guarantees. I also study data engineering and data management opportunities to design scalable algorithms. Finally, I provide large-scale synthetic and real-world experiments, as well as deployment challenges in the real-world environment.