Deliberation Scheduling for Time-Critical Sequential Decision Making

TL;DR

This paper presents a method for time-critical decision making that allocates computational resources among various routines, using models for pre-execution and real-time decision processes, supported by algorithms and empirical results.

Contribution

It introduces a framework for deliberation scheduling in time-critical decision making, including models and algorithms for both precursor and recurrent scenarios.

Findings

Algorithms for precursor and recurrent models developed.

Empirical investigations demonstrate effectiveness.

Models capture decision making under time constraints.

Abstract

We describe a method for time-critical decision making involving sequential tasks and stochastic processes. The method employs several iterative refinement routines for solving different aspects of the decision making problem. This paper concentrates on the meta-level control problem of deliberation scheduling, allocating computational resources to these routines. We provide different models corresponding to optimization problems that capture the different circumstances and computational strategies for decision making under time constraints. We consider precursor models in which all decision making is performed prior to execution and recurrent models in which decision making is performed in parallel with execution, accounting for the states observed during execution and anticipating future states. We describe algorithms for precursor and recurrent models and provide the results of our empirical investigations to date.