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Machine Self-confidence in Autonomous Systems via Meta-analysis of Decision Processes

  • Brett IsraelsenEmail author
  • Nisar Ahmed
  • Eric Frew
  • Dale Lawrence
  • Brian Argrow
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 965)

Abstract

Algorithmic assurances assist human users in trusting advanced autonomous systems appropriately. This work explores one approach to creating assurances in which systems self-assess their decision-making capabilities, resulting in a ‘self-confidence’ measure. We present a framework for self-confidence assessment and reporting using meta-analysis factors, and then develop a new factor pertaining to ‘solver quality’ in the context of solving Markov decision processes (MDPs), which are widely used in autonomous systems. A novel method for computing solver quality self-confidence is derived, drawing inspiration from empirical hardness models. Numerical examples show our approach has desirable properties for enabling an MDP-based agent to self-assess its performance for a given task under different conditions. Experimental results for a simulated autonomous vehicle navigation problem show significantly improved delegated task performance outcomes in conditions where self-confidence reports are provided to users.

Keywords

Human-Machine systems Artificial intelligence Self-assessment 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Brett Israelsen
    • 1
    Email author
  • Nisar Ahmed
    • 1
  • Eric Frew
    • 1
  • Dale Lawrence
    • 1
  • Brian Argrow
    • 1
  1. 1.University of Colorado BoulderBoulderUSA

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