Abstract
Many cognitive processes can be represented as a graph that is visual and computational. Graph search used to be a classic AI method. Here we present a dynamic graph, called “Mental Map” with a set of timestamps, nodes, edges, attributes, and operators for extracting experts’ knowledge and incorporating other AI models such as Association Rule Learning and Decision Tree. Mental Map is written in Javascript and it can run on any platform that has a web browser. Three case studies are presented: suspicious behavior detection, email phishing, and malware detection in embedded systems. The tool can be used interactively and automatically. Through human-machine collaborative learning, Mental Map provides more explainability and flexibility than prevailing semantic webs and machine learning algorithms.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Winston, P.H.: Artificial Intelligence. 2nd edn. Addison-Wesley Company (1984)
EPAM, Wikipedia, captured on 17 January 2021. https://en.wikipedia.org/wiki/EPAM
Kost, R.: VOM – Visual Ontology Modeler. http:// thematix.com/tools/vom/ (2013)
MouselabWEB. http://www.mouselabweb.org/
Google Knowledge Graph. https://googleblog.blogspot.no/2012/05/introducing-knowledge-graph-things-not.html
van der Maaten, L.J.P., Hinton, G.E.: Visualizing non-metric similarities in multiple maps. Mach. Learn. 87(1), 33–55 (2012)
Wikipedia, Association Rule Learning. https://en.wikipedia.org/wiki/Decision_tree_learning. Captured on 17 January 2021
Wikipedia, Decision Tree Learning, https://en.wikipedia.org/wiki/Decision_tree_learning captured on January 17, 2021
Anderson, J.R., Lebiere, C.: The Atomic Components of Thought. Lawrence Erlbaum Associates, Mahwah (1998)
Kahneman, D.: Thinking, Fast and Slow. NY, NY: Farrar, Straus and Giroux. (2011)
Kriglstein, S.: OWL ontology visualization: graphical representations of properties on the instance level. In Proceedings of the 14th International Conference on Information Visualisation (IV 2010), pp. 92–97. IEEE (2010)
Laird, J.E., et al.: Interactive task learning. IEEE Intell. Syst. 32(4), 6–21 (2017)
Lebiere, C., Jentsch, F., Ososky, S.: Cognitive models of decision-making processes for human-robot interaction. In: Proceedings of the HCI International Conference (HCII-2013), Las Vegas, NV (2013)
Acknowledgment
The author would like to thank Samuel Pearl, Neta Ezer, Justin King, and Martin Otto for insightful discussions, Jose A. Morales and Michael Strougen for their malware detection case studies, and Ben Graham for programming. This project is in part supported by Northrop Grumman Corporation, Siemens, and the NIST PSCR program.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Cai, Y. (2021). Human-Machine Learning with Mental Map. In: Ayaz, H., Asgher, U., Paletta, L. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2021. Lecture Notes in Networks and Systems, vol 259. Springer, Cham. https://doi.org/10.1007/978-3-030-80285-1_60
Download citation
DOI: https://doi.org/10.1007/978-3-030-80285-1_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80284-4
Online ISBN: 978-3-030-80285-1
eBook Packages: EngineeringEngineering (R0)