Abstract
Fishgram, a novel algorithm for recognizing frequent or otherwise interesting sub-hypergraphs in large, heterogeneous hypergraphs, is presented. The algorithm’s implementation the OpenCog integrative AGI framework is described, and concrete examples are given showing the patterns it recognizes in OpenCog’s hypergraph knowledge store when the OpenCog system is used to control a virtual agent in a game world. It is argued that Fishgram is well suited to fill a critical niche in OpenCog and potentially other integrative AGI architectures: scalable recognition of relatively simple patterns in heterogeneous, potentially rapidly-changing data.
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References
Goertzel, B., Pitt, J., Cai, Z., Wigmore, J., Huang, D., Geisweiller, N., Lian, R., Yu, G.: Integrative general intelligence for controlling game ai in a minecraft-like environment. In: Proc. of BICA 2011 (2011)
Goertzel, B., Pinto, H., Pennachin, C., Goertzel, I.F.: Using dependency parsing and probabilistic inference to extract relationships between genes, proteins and malignancies implicit among multiple biomedical research abstracts. In: Proc. of Bio-NLP 2006 (2006)
Goertzel, B., Pennachin, C., et al.: An integrative methodology for teaching embodied non-linguistic agents, applied to virtual animals in second life. In: Proc. of the First Conf. on AGI. IOS Press (2008)
Goertzel, B.: The Hidden Pattern. Brown Walker (2006)
Tulving, E., Craik, R.: The Oxford Handbook of Memory. Oxford U. Press (2005)
Washio, T., Motoda, H.: State of the art of graph-based data mining. SIGKDD Explorations 5, 59–68 (2003)
Keyvanpour, M., Azizani, F.: Classification of approaches and challenges of frequent subgraphs mining in biological networks. Int. J. Adv. Eng. Sci. and Tech. 4 (2012)
Yan, X., Han, J.: gspan: Graph-based substructure pattern mining. In: ICDM 2002 (2002)
Quinlan, J.R.: Learning logical definitions from relations. Machine Learning 5 (1990)
Bell, A.J.: The co-information lattice. In: Proc. ICA 2003 (2003)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. 20th Int. Conf. Very Large Data Bases (1994)
Williams, P.L., Beer, R.D.: Nonnegative decomposition of multivariate information. CoRR abs/1004.2515 (2010)
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O’Neill, J. et al. (2012). Pattern Mining for General Intelligence: The FISHGRAM Algorithm for Frequent and Interesting Subhypergraph Mining. In: Bach, J., Goertzel, B., Iklé, M. (eds) Artificial General Intelligence. AGI 2012. Lecture Notes in Computer Science(), vol 7716. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35506-6_20
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DOI: https://doi.org/10.1007/978-3-642-35506-6_20
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