Adjudication of Symbolic & Connectionist Arguments in Autonomous Driving AI

6 pages•Published: April 27, 2020

Michael Giancola, Selmer Bringsjord, Naveen Sundar Govindarajulu and John Licato

Abstract

This paper discusses the tragic accident in which the first pedestrian was killed by an autonomous car: due to several grave errors in its design, it failed to recognize the pedestrian and stop in time to avoid a collision. We start by discussing the accident in some detail, enlightened by the recent publication of a report from the National Transportation Safety Board (NTSB) re. the accident. We then discuss the shortcomings of current autonomous- car technology, and advocate an approach in which several AI agents generate arguments in support of some action, and an adjudicator AI determines which course of action to take. Input to the agents can come from both symbolic reasoning and connectionist-style inference. Either way, underlying each argument and the adjudication process is a proof/argument in the language of a multi-operator modal calculus, which renders transparent both the mechanisms of the AI and accountability when accidents happen.

Keyphrases: argumentation, hybrid, uncertainty multi operator logic

In: Gregoire Danoy, Jun Pang and Geoff Sutcliffe (editors). GCAI 2020. 6th Global Conference on Artificial Intelligence (GCAI 2020), vol 72, pages 28-33.

Links:	https://easychair.org/publications/paper/Vtl4
	https://doi.org/10.29007/k647

BibTeX entry

@inproceedings{GCAI2020:Adjudication_Symbolic_Connectionist_Arguments,
  author    = {Michael Giancola and Selmer Bringsjord and Naveen Sundar Govindarajulu and John Licato},
  title     = {Adjudication of Symbolic & Connectionist Arguments in Autonomous Driving AI},
  booktitle = {GCAI 2020. 6th Global Conference on Artificial Intelligence (GCAI 2020)},
  editor    = {Gregoire Danoy and Jun Pang and Geoff Sutcliffe},
  series    = {EPiC Series in Computing},
  volume    = {72},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/Vtl4},
  doi       = {10.29007/k647},
  pages     = {28-33},
  year      = {2020}}

Download PDF Open PDF in browser