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I share reflections at several levels on directions with moving beyond the closed-world aspects of AI methods and prototypes to pursue a deeper understanding of open-world intelligence. I start with highlights of the rich intellectual history of the pursuit of computational mechanisms underlying thought and intelligent behavior, touching on core contributions of Alonzo Church and Alan Turing, John von Neumann, Herbert Simon, John McCarthy, and others. I discuss the goal of creating computational agents that can prosper, despite inescapable incompleteness in sensing, reasoning, and representation--incompleteness that comes when relatively simple reasoners are immersed in complex, dynamic universes. After discussing the limitations of closed-world reasoning, I present several key challenges with moving ahead. In logic-based reasoners, open-world refers to the assumption that the truth value of a statement is independent of whether or not it is known by any single observer or agent to be true. I use open world more broadly to refer to models and machinery that incorporate implicit or explicit machinery for representing and grappling with assumed incompleteness in representations and inferences, not just in truth values. Such incompleteness is common and is to be assumed when an agent is sensing, reflecting, and acting in a complex universe. Following a discussion of technical challenges and opportunities with open-world AI, I allude to the open world outside the closed worlds of our laboratories, where AI is pressed into real service, working with realistic streams of problem instances, and with people and organizations. Finally, I discuss the endeavor of AI research, and collaboration and coordination among researchers in the open world.
Presented at the 2008 AAAI Conference, Chicago, Illinois, July 2008.
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