CogSense: A Cognitively Inspired Framework for Perception Adaptation

TL;DR

CogSense is a cognitively inspired framework that detects perception errors and adapts perception parameters using probabilistic logic, validated through a contrast-based method that reduces false detections.

Contribution

The paper introduces CogSense, a novel perception adaptation system inspired by mammalian cognition, employing probabilistic signal temporal logic for error detection and correction.

Findings

Effective perception error detection using heterogeneous probe functions.

Successful validation of contrast-based perception adaptation method.

Reduction in false positives and negatives in perception tasks.

Abstract

This paper proposes the CogSense system, which is inspired by sense-making cognition and perception in the mammalian brain to perform perception error detection and perception parameter adaptation using probabilistic signal temporal logic. As a specific application, a contrast-based perception adaption method is presented and validated. The proposed method evaluates perception errors using heterogeneous probe functions computed from the detected objects and subsequently solves a contrast optimization problem to correct perception errors. The CogSense probe functions utilize the characteristics of geometry, dynamics, and detected blob image quality of the objects to develop axioms in a probabilistic signal temporal logic framework. By evaluating these axioms, we can formally verify whether the detections are valid or erroneous. Further, using the CogSense axioms, we generate the probabilistic signal temporal logic-based constraints to finally solve the contrast-based optimization problem to reduce false positives and false negatives.