Abstract
One area of artificial intelligence system design, development, and especially testing is the notion of knowledge management. Long-term memory within an Artificial Intelligent System (AIS) can be thought of as the system’s Knowledge Base (KB) or knowledge ontology (Newell et al., Preliminary description of general problem-solving program-i (gps-i). Carnegie Institute of Technology, Pittsburgh, PA, 1957). What does the AIS know, what has it learned, and how does that knowledge change over time as the system continues to learn, reason, and adapt. How are memories modified or reinterpreted as new information is gained through experience as the system interacts with its environment? How knowledge is managed throughout the AIS radically changes how it learns, what it learns, and how it assimilates knowledge. This must be considered early on, even during the overall conceptual modeling of the AIS. Ontologies are an important part of conceptual modeling. They provide substantial structural information and are typically the key elements in any large-scale integration effort (Davis et al., Molecular Psychiatry 6:13–34, 2001). We investigate and present several notions from the formal practice of ontology and adapt them for use in intelligence processing and fusion systems. The aim is to provide a solid logical framework that will allow a formal taxonomy to be analyzed. We will provide examples of formal ontological analysis applied to system of systems fault ontologies and an example of technical publications taxonomy.
The purpose here is to discuss some research and development issues with respect to ontologies and taxonomies for Information Systems (Carbone, A framework for enhancing transdisciplinary research knowledge. Tech University Press, Lubbock, TX, 2010). We look at what issues exist for ontology-based knowledge management systems in a large, diverse, heterogeneous processing environment (Brillouin, Science and information theory. Dover, Mineola, NY, 2004).
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References
Newell, J., Shaw, C., & Simon, H. (1957). Preliminary description of general problem-solving program-i (gps-i). Pittsburgh, PA: WP7, Carnegie Institute of Technology.
LaBar, K. (2006). Cognitive neuroscience of emotional memory. Nature Reviews Neuroscience, 7, 54–64.
Damasio, A. (1994). Descarte’s error: Emotion, reason, and the human brain. New York: Gosset/Putnam.
Crowder, J. (2003). Machine learning: intuition (concept) learning in hybrid genetic/fuzzy/neural systems. NSA Technical Paper CON_0013_2003_009.
LeDoux, J. (1996). The emotional brain. New York: Simon and Schuster.
LeDoux, J. (2000). Emotion circuits in the brain. Annual Review of Neuroscience, 23, 155–184.
Crowder, J. (2002). Adaptive learning algorithms for functional constraints on an evolving neural network system. NSA Technical Paper CON_0013_2002_003.
Zadeh, L. (2004). A note on web intelligence, world knowledge and fuzzy logic. Data & Knowledge Engineering, 50, 291–304.
LeDoux, J. (2002). Synaptic self: How our brains become who we are. New York: Viking.
Crowder, J. (2010). Operative information software agents (OISA) for intelligence processing. In AIAA Aerospace@Infotech 2010 Conference.
Crowder, J., & Friess, S. (2010). Artificial neural diagnostics and prognostics: Self-soothing in cognitive systems. In Proceedings of the International Conference on Artificial Intelligence, ICAI’10.
Dourish, J. (2004). What we talk about when we talk about context. Personal and Ubiquitous Computing, 8, 19–30.
Levine, P. (1997). Walking the tiger: Healing trauma. Berkeley, CA: North Atlantic Books.
Rowley, J., & Hartley, R. (2008). Organizing knowledge: An introduction to managing access to information. Farnham: Ashgate.
Kosko, G. (2004). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24, 65–75.
Marsella, S., & Gratch, J. (2002). A step towards irrationality: Using emotion to change belief. In 1st International Joint Conference on Autonomous Agents and Multi-Agent Systems, Bologna, Italy.
Liao, S., He, J., & Tang, T. (2004). A framework for context information management. Journal of Information Science, 30, 528–539.
Gupta, K., & Govindarajan, V. (2000). Knowledge flows within multinational corporations. Strategic Management Journal, 21, 473–496.
Miller, E., Freedman, D., & August, W. J. (2002). The prefrontal cortex: Categories, concepts and cognition. Philosophical Transactions of the Royal Society B Biological Sciences, 357(1424), 1123–1136.
Pinto, M. (2006). A grounded theory on abstracts quality: Weighting variables and attributes. Scientometrics, 69, 213–226.
Suh, N. (2006). Application of axiomatic design to engineering collaboration and negotiation. In 4th International Conference on Axiomatic Design, Firenze.
Ejigu, D., Scuturici, M., & Brunie, L. (2008). Hybrid approach to collaborative context-aware service platform for pervasive computing. Journal of Computers, 3, 40.
DeYoung, C., Hirsh, J., Shane, M., Papademetris, X., Rajeevan, N., & Gray, J. (2010). Testing predictions from personality neuroscience. Psychological Science, 21(6), 820–828.
Gruber, T. (2008). Collective knowledge systems: Where the social web meets the semantic web. Web Semantics: Science, Services and Agents on the World Wide Web, 6, 4–13.
Carbone, J. N. (2010). A framework for enhancing transdisciplinary research knowledge. Lubbock, TX: Tech University Press.
Eichenbaum, H. (2002). The cognitive neuroscience of memory. New York: Oxford University Press.
Hong, J., & Landay, J. (2001). An infrastructure approach to context-aware computing. Human-Computer Interaction, 16, 287–303.
Howard, N., & Qusaibaty, A. (2004). Network-centric information policy. In Second International Conference on Informatics and Systems.
Anderson, J. (2004). Cognitive psychology and its implications. New York: Worth.
Newell, A. (2003). Unified theories of cognition. Cambridge, MA: Harvard University Press.
Crowder, J., & Carbone, J. (2011). The great migration: Information to knowledge using cognition-based frameworks. New York: Springer Science.
Crowder, J., & Friess, S. (2010). Artificial neural emotions and emotional memory. In International Conference on Artificial Intelligence, ICAI’10.
Crowder, J. (2010). Flexible object architectures for hybrid neural processing systems. In International Conference on Artificial Intelligence, ICAI’10.
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Crowder, J.A., Carbone, J., Friess, S. (2020). Ontology-Based Knowledge Management for Artificial Intelligent Systems. In: Artificial Psychology. Springer, Cham. https://doi.org/10.1007/978-3-030-17081-3_9
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DOI: https://doi.org/10.1007/978-3-030-17081-3_9
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