We consider the fundamental problem of monitoring (i.e. tracking) the belief state in a dynamic system, when the model is only approximately correct and when the initial belief state might be unknown. In this general setting where the model is (perhaps only slightly) mis-specified, monitoring (and consequently planning) may be impossible as errors might accumulate over time. We provide a new characterization, the value of observation, which allows us to bound the error accumulation. The value of observation is a parameter that governs how much information the observation provides. For instance, in Partially Observable MDPs when it is 1 the POMDP is an MDP while for an unobservable Markov Decision Process the parameter is 0. Thus, the new parameter characterizes a spectrum from MDPs to unobservable MDPs depending on the amount of information conveyed in the observations.
|Number of pages
|IJCAI International Joint Conference on Artificial Intelligence
|Published - 2007
|20th International Joint Conference on Artificial Intelligence, IJCAI 2007 - Hyderabad, India
Duration: 6 Jan 2007 → 12 Jan 2007