Solving Time-Critical Decision-Making Problems with Predictable Computational Demands

In this work we present an approach to solving time-critical decision-making problems by taking advantage of domain structure to expand the amount of time available for processing difficult combinatorial tasks. Our approach uses predictable variability in computational demands to allocate on-line deliberation time and exploits problem regularity and stochastic models of environmental dynamics to restrict attention to small subsets of the state space. This approach demonstrates how slow, high-level systems (e.g., for planning and scheduling) might interact with faster, more reactive systems (e.g., for real-time execution and monitoring) and enables us to generate timely solutions to difficult combinatorial planning and scheduling problems such as air traffic control. Introduction We are interested in the design of systems that make the best use of the time available for decision-making by explicitly accounting for the costs and benefits of computational delays. Applications such as ai..