Predictive Heuristics for Decision-Making in Real-World Environments

Abstract

In this paper we consider the issue of endowing an AGI system with decision-making capabilities for operation in real-world environments or those of comparable complexity. While action-selection is a critical function of any AGI system operating in the real-world, very few applicable theories or methodologies exist to support such functionality, when all necessary factors are taken into account. Decision theory and standard search techniques require several debilitating simplifications, including determinism, discrete state spaces, exhaustive evaluation of all possible future actions and a coarse grained representation of time. Due to the stochastic and continuous nature of real-world environments and inherent time-constraints, direct application of decision-making methodologies from traditional decision theory and search is not a viable option. We present predictive heuristics as a way to bridge the gap between the simplifications of decision theory and search, and the complexity of real-world environments.