Deceptive Kernel Function on Observations of Discrete POMDP
  This paper studies the deception applied on agent in a partially observable
Markov decision process. We introduce deceptive kernel function (the kernel)
applied to agent's observations in a discrete POMDP. Based on value iteration,
value function approximation and POMCP three characteristic algorithms used by
agent, we analyze its belief being misled by falsified observations as the
kernel's outputs and anticipate its probable threat on agent's reward and
potentially other performance. We validate our expectation and explore more
detrimental effects of the deception by experimenting on two POMDP problems.
The result shows that the kernel applied on agent's observation can affect its
belief and substantially lower its resulting rewards; meantime certain
implementation of the kernel could induce other abnormal behaviors by the
agent.
