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Cited by 101Reinforcement Learning in RoboticsAI-based Problem Solving and PlanningMachine Learning and Algorithms

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Intelligence Through Interaction: Towards a Unified Theory for Learning

Open access
Tan, Ah-Hwee, Carpenter, Gail A., Grossberg, Stephen·2007·Lecture Notes in Computer Science
DOI 10.1007/978-3-540-72383-7_128
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Crossref · 10.1007/978-3-540-72383-7_128

Cites

1 of 1 references verified0 citation errors · 0 not checkable

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  • Fusion ART extends Adaptive Resonance Theory by integrating multiple information channels through complementary learning, allowing simultaneous processing of heterogeneous data types within a unified resonance-based system.

    “Fusion ART (Tan, Carpenter, and Grossberg 2007; Tan et al. 2019) extends this framework by integrating multiple information channels through complementary learning, allowing simultaneous processing of heterogeneous data types within a unified resonance-based system.”

    From ARTEM: Enhancing Large Language Model Agents with Spatial-Temporal Episodic Memory (2026) · cites this paper as Tan, Carpenter, and Grossberg 2007

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 22

  1. Anderson, M.L.: Embodied Cognition: A Field Guide. Artificial Intelligence 149, 91–130 (2003) · DOI
  2. Carpenter, G.A., Grossberg, S. (eds.): Pattern Recognition by Self-Organizing Neural Networks. MIT Press, Cambridge (1991)
  3. Carpenter, G.A., Grossberg, S., Rosen, D.B.: Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System. Neural Networks 4, 759–771 (1991) · DOI
  4. Carpenter, G.A., Grossberg, S.: Adaptive Resonance Theory. In: Arbib, M.A. (ed.) The Handbook of Brain Theory and Neural Networks, pp. 87–90. MIT Press, Cambridge (2003)
  5. Duda, R.O., Hart, P.E., Stock, D.G. (eds.): Pattern Classification (Section 10.11.2), 2nd edn. John Wiley, New York (2001)
  6. Gordan, D., Subramanian, D.: A Cognitive Model of Learning to Navigate. In: Proceedings, Nineteenth Annual Conference of the Cognitive Science Society, pp. 271–276 (1997)
  7. Grossberg, S.: Adaptive Pattern Recognition and Universal Recoding, I: Parallel Development and Coding of Neural Feature Detectors. Biological Cybernetics 23, 121–134 (1976) · DOI
  8. Grossberg, S.: Adaptive Pattern Recognition and Universal Recoding, II: Feedback, Expectation, Olfaction, and Illusion. Biological Cybernetics 23, 187–202 (1976) · DOI
  9. Grossberg, S.: How Does the Cerebral Cortex Work? Development, Learning, Attention, and 3d Vision by Laminar Circuits of Visual cortex. Behavioral and Cognitive Neuroscience Reviews 2, 47–76 (2003) · DOI
  10. He, J., Tan, A.-H., Tan, C.-L.: On Machine Learning Methods for Chinese Documents Classification. Applied Intelligence 18(3), 311–322 (2003) · DOI
  11. Levine, D.S. (ed.): Introduction to Neural and Cognitive Modeling. Lawrence Erlbaum Associates, Mahwah (2000)
  12. Raizada, R., Grossberg, S.: Towards a Theory of the Laminar Architecture of Cerebral Cortes: Computational Clues from the Visual System. Cerebral Cortex 13, 200–213 (2003) · DOI
  13. Sun, R., Merrill, E., Peterson, T.: From Implicit Skills to Explicit Knowledge: A Bottom-up Model of Skill Learning. Cognitive Science 25(2), 203–244 (2001) · DOI
  14. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
  15. Tan, A.H.: Adaptive Resonance Associative Map. Neural Networks 8(3), 437–446 (1995) · DOI
  16. Tan, A.H.: Cascade ARTMAP: Integrating Neural Computation and Symboli Knowledge Processing. IEEE Transactions on Neural Networks 8(2), 237–250 (1997) · DOI
  17. Tan, A.-H., Ong, H.-L., Pan, H., Ng, J., Li, Q.-X.: Towards Personalized Web Intelligence. Knowledge and Information Systems 6(5), 595–616 (2004) · DOI
  18. Tan, A.-H., Soon, H.-S.: Predictive Adaptive Resonance Theory and Knowledge Discovery in Database. In: Terano, T., Chen, A.L.P. (eds.) PAKDD 2000. LNCS, vol. 1805, pp. 173–176. Springer, Heidelberg (2000) · DOI
  19. Tan, A.H.: FALCON: A Fusion Architecture for Learning, Cognition, and Navigation. In: Proceedings, International Joint Conference on Neural Networks, pp. 3297–3302 (2004)
  20. Tan, A.H.: Self-organizing Neural Architecture for Reinforcement Learning. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3971, pp. 470–475. Springer, Heidelberg (2006) · DOI
  21. Tan, A.H., Pan, H.: Predictive Neural Networks for Gene Expression Data Analysis. Neural Networks 18(3), 297–306 (2005) · DOI
  22. Tan, A.H., Lu, N., Xiao, D.: Integrating Temporal Difference Methods and Selforganizing Neural Networks for Reinforcement Learning with Delayed Evaluative feedback. IEEE Transactions on Neural Networks, to appear

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Metadata

Authors
Tan, A.-H., Carpenter, G. A., & Grossberg, S.
Affiliations
  • Nanyang Technological University (SG)
  • Boston University (US)
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer Berlin Heidelberg
Published
2007
Pages
1094-1103
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-540-72383-7_128
OpenAlex
W2166631986
Cited by
101
References
1
Citations by year
2025: 5 · 2024: 3 · 2023: 5 · 2022: 2 · 2021: 5 · 2020: 4 · 2019: 11 · 2018: 4 · 2017: 7 · 2016: 5 · 2015: 6 · 2014: 10 · 2013: 2 · 2012: 14
FWCI
3.25
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion, other-oa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
Keywords
embodied AI, autonomous agents, incremental knowledge acquisition, learning architecture

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