# Deceptive Kernel Function on Observations of Discrete POMDP

arXiv: https://arxiv.org/abs/2008.05585 · HTML: https://mlsanity.com/paper/2008.05585/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2020.

## Built on by (most cited first, 1 of 1)

- [Symbiotic Game and Foundation Models for Cyber Deception Operations in Strategic Cyber Warfare](https://mlsanity.com/paper/2403.10570/) (2024)

## Abstract (arXiv)

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.