Authors: Ofer Dagan, Tyler Becker, Zachary N. Sunberg
Abstract: When human operators of cyber-physical systems encounter surprising behavior,
they often consider multiple hypotheses that might explain it. In some cases,
taking information-gathering actions such as additional measurements or control
inputs given to the system can help resolve uncertainty and determine the most
accurate hypothesis. The task of optimizing these actions can be formulated as
a belief-space Markov decision process that we call a hypothesis-driven belief
MDP. Unfortunately, this problem suffers from the curse of history similar to a
partially observable Markov decision process (POMDP). To plan in continuous
domains, an agent needs to reason over countlessly many possible
action-observation histories, each resulting in a different belief over the
unknown state. The problem is exacerbated in the hypothesis-driven context
because each action-observation pair spawns a different belief for each
hypothesis, leading to additional branching. This paper considers the case in
which each hypothesis corresponds to a different dynamic model in an underlying
POMDP. We present a new belief MDP formulation that: (i) enables reasoning over
multiple hypotheses, (ii) balances the goals of determining the (most likely)
correct hypothesis and performing well in the underlying POMDP, and (iii) can
be solved with sparse tree search.
Source: http://arxiv.org/abs/2411.14404v1
