Abstract
The focus of this chapter is autistic learning and cognition. We explore the difficullties humans and machines share in learning the external world and focus on how it happens in case of autism. Firstly, the framework of active learning is introduced which is the basis of our model for autistic adaptation. We start with a hypersensitivity of an autistic learning system and explain how it leads to repetitive patterns, stereotypy and ignorance behavior. We then introduce hybrid deductive, inductive and abductive reasoning system Jasmine and reproduce the scenarios of autistic learning.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anshakov OM, Finn VK, Skvortsov DP (1989) On axiomatization of many-valued logics associated with formalization of plausible reasoning. Stud Logica 42(4):423–447
Bacchus F, Halpern JY, Levesque H (1999) Reasoning about noisy sensors and effectors in the situation calculus. Artif Intell 111(1–2)
Bogdashina O (2005) Communication Issues in Autism and Asperger Syndrome: do we speak the same language? Jessica Kingsley Publishers, London
Bos K, Zeanah CH, Fox NA, Drury SS, McLaughlin KA, Nelson CA (2011) Psychiatric outcomes in young children with a history of institutionalization. Harv Rev Psychiatry 19(1):15–23
Dasgupta S (2005) Coarse sample complexity bounds for active learning. In NIPS
Dasgupta S, Hsu D, Monteleoni C (2007) A general agnostic active learning algorithm. Technical Report CS2007-0898 Department of Computer Science and Engineering University of California, San Diego
Galitsky B (2007) Handling representation changes by autistic reasoning. AAAI fall symposium – technical report FS-07-03, pp 9–16
Galitsky B, Shpitsberg I (2015) Evaluating assistance to Individuals with autism in reasoning about mental world. Artificial intelligence applied to assistive technologies and smart environments: papers from the 2015 AAAI workshop
Galitsky B, Spitsberg I (2006) How one can learn programming while teaching reasoning to children with autism AAAI Spring Symposia Stanford CA
Ghera M, Marshall P, Fox N, Zeanah C, Nelson CA, Smyke AT (2009) The effects of foster care intervention on socially deprived institutionalized children’s attention and positive affect: results from the BEIP study. J Child Psychol Psychiatry 50:246–253
Kennedy Krieger Institute (2009) Difference in the way children with autism learn new behaviors described. ScienceDaily, July 10. Retrieved February 25, 2016 from www.sciencedaily.com/releases/2009/07/090706113647.htm
Lakemeyer G (1999) On sensing in GOLOG. In: Levesque HJ, Pirri F (eds) Logical foundations for cognitive agents. Springer, Berlin
Levesque HL, Pagnucco M (2000) Legolog: inexpensive experiments in cognitive robotics. In: 938 proceedings of the second international cognitive robotics workshop, Berlin, Germany, August 939 pp 21–22
Levesque HJ, Reiter R, Lesperance Y, Lin F, Scherl RB (1997) GOLOG: a logic programming language for dynamic domains. J Log Program 31:59–84
Mill JS (1843) A system of logic 1843. Also available from University Press of the Pacific, Honolulu, 2002
Mundy P, Crowson M (1997) Joint attention and early social communication. J Autism Dev Disord 27:653–676
Olsson, F (2008) Bootstrapping named entity annotation by means of active machine learning – a method for creating corpora. Ph.D. dissertation., Department of Swedish, University of Gothenburg
Pedersen T (2015) Children with autism Don’t adjust sniffing time for bad smells. Psych Central. Retrieved on December 8, 2015, from http://psychcentral.com/news/2015/07/03/children-with-autism-dont-adjust-sniffing-time-for-bad-smells/86412.html
Rapp JT, Vollmer TR, St Peter C, Dozier CL, Cotnoir NM (2004) Analysis of response allocation in individuals with multiple forms of stereotyped behavior. J Appl Behav Anal 37(4):481–501
Reiter R (1993) Proving properties of states in the situational calculus. J Artif Intell 64:337–351
Sandman CA, Kemp AS (2011) Opioid antagonists may reverse endogenous opiate. Dependence in the treatment of self-injurious behavior. Pharmaceuticals (Basel) 4(2):366–381
Shanahan M (1997) Solving the frame problem. MIT Press, Cambridge, MA
Shpitsberg I (2005) Sensory system correction for Choldrten with autism (Кoppeкция ocoбeннocтeй paзвития ceнcopныx cиcтeм у дeтeй c cиндpoмoм paннeгo дeтcкoгo aутизмa». Aльмaнax ИКП PAO – M.) in Russian
Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features, conference on computer vision and pattern recognition
Yu K, Bi J, Tresp V (2006) Active learning via transductive experimental design. In Proceedings of the International Conference on Machine Learning (ICML). ACM Press, pp 1081–1087
Zhu X (2005) Semi-supervised learning literature survey. Computer Sciences Technical Report 1530, University of Wisconsin-Madison
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Galitsky, B., Shpitsberg, I. (2016). Autistic Learning and Cognition. In: Computational Autism. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-39972-0_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-39972-0_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-39971-3
Online ISBN: 978-3-319-39972-0
eBook Packages: Computer ScienceComputer Science (R0)