Motivated Learning from Interesting Events: Adaptive, Multitask Learning Agents for Complex Environments

By Kathryn Elizabeth Merrick, Mary Lou Maher. 2009

37 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

Active learning of inverse models with intrinsically motivated goal exploration in robots (2013, positive): “This is the reason why new active learning heuristics have been proposed in developmental robotics, such as those based on the psychological concept of intrinsic motivations [54, 46, 55] which relate to mechanisms that drive a learning agent to perform different activities for their own sake, withou”
What is Intrinsic Motivation? A Typology of Computational Approaches (2007, background): “…particular models of intrinsic motivation have already been developed in the literature (e.g., Barto and Simsek, 2005; Bonarini et al., 2006; Huang and Weng, 2002; Kaplan and Oudeyer, 2003; Marshall et al., 2004; Merrick and Maher, 2008; Oudeyer et al., 2005, 2007; Schmidhuber, 1991; Thrun, 1995).”
Self-organization of early vocal development in infants and machines: the role of intrinsic motivation (2014, background): “Indeed, transposed in curiosity-driven learning machines [14, 15, 18, 19, 20, 21, 22] and robots [11, 12], they were shown to yield highly efficient learning of inverse models in high-dimensional redundant sensorimotor spaces [12, 23], those spaces sharing many mathematical properties with vocal spa”
Intrinsically Motivated Learning in Natural and Artificial Systems (2013, background): “The same is true for the robots used to test the intelligent adaptive curiosity algorithm of Oudeyer and colleagues (2007) and for almost all other systems whose intrinsic motivations are based on knowledge (e.g., Huang and Weng 2002; Lee et al. 2009; Marshall et al. 2004; Merrick and Maher 2009).”
Learning under Concept Drift: an Overview (2010, background): “Adaptive knowledge transfer has been exploited in multitask learning [107] and learning from multiple sources [31, 103].”
The interaction of maturational constraints and intrinsic motivations in active motor development (2011, background): “While numerous heuristics are not robust to drive exploration in spaces where some parts cannot be learned or where the noise is inhomogeneous [7], some of them (e.g. decrease in prediction errors) have been shown to be efficient in such spaces [3], [8]–[10] Nevertheless, all of these methods begin…”