.png){kind=icon}
.png){kind=icon}
Return
Deception in Supervisory Control
.png){kind=icon}
OA
AI
DOI：10.1109/TAC.2021.3057991.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 reachability specifications, and study the synthesis of optimal deceptive policies for such agents. We also study the synthesis of optimal reference policies that prevent deceptive strategies of the agent and achieve the supervisor's task with high probability. We show that the synthesis of optimal deceptive policies has a convex optimization problem formulation, while the synthesis of optimal reference policies requires solving a nonconvex optimization problem. We also show that the synthesis of optimal reference policies is NP-hard.
Keywords:
Task analysis
Optimization
Probabilistic logic
Markov processes
Hidden Markov models
Supervisory control
Convex functions
Computational complexity
deception
Markov decision processes (MDPs)
supervisory control
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W
Organization
View more organizations