Evolutionary hypernetwork classifiers for protein-proteininteraction sentence filtering
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolving hypernetwork models of binary time series for forecasting price movements on stock markets
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Evolutionary hypernetworks for learning to generate music from examples
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Bio-inspired computing: constituents and challenges
International Journal of Bio-Inspired Computation
MMG: a learning game platform for understanding and predicting human recall memory
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Visual query expansion via incremental hypernetwork models of image and text
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
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Recent interest in human-level intelligence suggests a rethink of the role of machine learning in computational intelligence. We argue that "without cognitive learning the goal of achieving human-level synthetic intelligence is far from completion. Here we review the principles underlying human learning and memory, and identify three of them, i.e., continuity, glocality, and compositionality, as the most fundamental to human-level machine learning. We then propose the recently-developed hypernetwork model as a candidate architecture for cognitive learning and memory. Hypernetworks are a random hypergraph structure higher-order probabilistic relations of data by an evolutionary self-organizing process based on molecular self- assembly. The chemically-based massive interaction for information organization and processing in the molecular hypernetworks, referred to as hyperinteractionism, is contrasted "with the symbolist, connectionist, and dynamicist approaches to mind and intelligence. We demonstrate the generative learning capability of the hypernetworks to simulate linguistic recall memory, visual imagery, and language-vision crossmodal translation based on a video corpus of movies and dramas in a multimodal memory game environment. We also offer prospects for the hyperinteractionistic molecular mind approach to a unified theory of cognitive learning.