2 research outputs found
Optimal Deceptive and Reference Policies for Supervisory Control
The use of deceptive strategies is important for an agent that attempts not
to reveal his intentions in an adversarial environment. We consider a setting
in which a supervisor provides a reference policy and expects an agent to
follow the reference policy and perform a task. The agent may instead follow a
different, deceptive policy to achieve a different task. We model the
environment and the behavior of the agent with a Markov decision process,
represent the tasks of the agent and the supervisor with linear temporal logic
formulae, and study the synthesis of optimal deceptive policies for such
agents. We also study the synthesis of optimal reference policies that prevents
deceptive strategies of the agent and achieves the supervisor's task with high
probability. We show that the synthesis of deceptive policies has a convex
optimization problem formulation, while the synthesis of reference policies
requires solving a nonconvex optimization problem.Comment: 20 page