Informed expectations to guide GDA agents in partially observable environments

Goal Driven Autonomy (GDA) is an agent model for reasoning about goals while acting in a dynamic environment. Since anomalous events may cause an agent's current goal to become invalid, GDA agents monitor the environment for such anomalies. When domains are both partially observable and dynamic, agents must reason about sensing and planning actions. Previous GDA work evaluated agents in domains that were partially observable, but does not address sensing actions with associated costs. Furthermore, partial observability still enabled generation of a grounded plan to reach the goal. We study agents where observability is more limited: the agent cannot generate a grounded plan because it does not know which future actions will be available until it explores more of the environment. We present a formalism of the problem that includes sensing costs, a GDA algorithm using this formalism, an examination of four methods of expectations under this formalism, and an implementation of the algorithm and empirical study.