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Global investments in artificial intelligence (AI) and robotics are on the rise, with the results to impact global economies, security, safety, and human well-being. The most heralded advances in this space are more often about the technologies that are capable of disrupting business-as-usual than they are about innovation that advances or supports a global workforce. The Future of Work at the Human-Technology Frontier is one of NSF’s 10 Big Ideas for research advancement. This panel discussion focuses on the barriers and opportunities for a future of human and AI/robot teaming, with people at the center of complex systems that provide social, ethical, and economic value. 

As more and more people are expected to work with complex AI-systems, it becomes more important than ever that such systems provide intuitive explanations for their decisions. A prerequisite for holding such explanatory dialogue is the ability of the systems to present their proposed decisions to the user in an easy-to-understand form. Unfortunately, such dialogues could become hard to facilitate in real-world problems where the system may be planning for multiple eventualities in stochastic environments. This means for the system to be effective, it needs to be able to present the policy at a high-level of abstraction and delve into details as required. Towards this end, we investigate the utility of temporal abstractions derived through analytically computed landmarks and their relative ordering to build a summarization of policies for Stochastic Shortest Path Problems. We formalize the concept of policy landmarks and show how it can be used to provide a high level overview of a given policy. Additionally, we establish the connections between the type of hierarchy we generate and previous works in temporal abstractions, specifically MaxQ hierarchies. Our approach is evaluated through user studies as well as empirical metrics that establish that people tend to choose landmarks facts as subgoals to summarize policies and demonstrates the performance of our approach on standard benchmarks. 

The prevalence and success of AI applications have been tempered by concerns about the controllability of AI systems about AI's impact on the future of work. These concerns reflect two aspects of a central question: how would humans work with AI systems? While research on AI safety focuses on designing AI systems that allow humans to safely instruct and control AI systems, research on AI and the future of work focuses on the impact of AI on humans who may be unable to do so. This Blue Sky Ideas paper proposes a unifying set of declarative principles that enable a more uniform evaluation of arbitrary AI systems along multiple dimensions of the extent to which they are suitable for use by specific classes of human operators. It leverages recent AI research and the unique strengths of the field to develop human-centric principles for AI systems that address the concerns noted above. 

In order to solve complex, long-horizon tasks, intelligent robots need to carry out high-level, abstract planning and reasoning in conjunction with motion planning. However, abstract models are typically lossy and plans or policies computed using them can be unexecutable. These problems are exacerbated in stochastic situations where the robot needs to reason about, and plan for multiple contingencies. We present a new approach for integrated task and motion planning in stochastic settings. In contrast to prior work in this direction, we show that our approach can effectively compute integrated task and motion policies whose branching structures encoding agent behaviors handling multiple execution-time contingencies. We prove that our algorithm is probabilistically complete and can compute feasible solution policies in an anytime fashion so that the probability of encountering an unresolved contingency decreases over time. Empirical results on a set of challenging problems show the utility and scope of our methods.