Autonomous planning under uncertainty: reset-identification strategies

The author investigates the problem of planning state-identification strategies. These strategies help an agent to reduce its uncertainty about the state of the environment through active exploration of the environment. Specifically, the author emphasizes the gathering of information from the environment where the agent utilizes the (entropic) actions of its control operations to provide maximum information gain. An interesting class of state-identification strategies is the class of those that use no sensing capabilities, i.e. sensorless strategies. Necessary and sufficient conditions for the existence of such strategies are presented. These conditions are exploited to develop an algorithm for planning sensorless state-identification strategies