Fully Autonomous Real-Time Autoencoder-Augmented Hebbian Learning through
                        the Collection of Novel Experiences

Stanley, Kenneth O.; Pugh, Justin K.; Bowren, Joshua

doi:10.1162/978-0-262-33936-0-ch064

Proceedings of the Artificial Life Conference 2016 2016

DOI: 10.1162/978-0-262-33936-0-ch064

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Fully Autonomous Real-Time Autoencoder-Augmented Hebbian Learning through the Collection of Novel Experiences

Kenneth O. Stanley

Justin K. Pugh

Joshua Bowren

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Cited by 2 publications

References 24 publications

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Enhanced Optimization with Composite Objectives and Novelty Selection

Shahrzad¹,

Fink²,

Miikkulainen³

2018

The 2018 Conference on Artificial Life

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An important benefit of multi-objective search is that it maintains a diverse population of candidates, which helps in deceptive problems in particular. Not all diversity is useful, however: candidates that optimize only one objective while ignoring others are rarely helpful. This paper proposes a solution: The original objectives are replaced by their linear combinations, thus focusing the search on the most useful tradeoffs between objectives. To compensate for the loss of diversity, this transformation is accompanied by a selection mechanism that favors novelty. In the highly deceptive problem of discovering minimal sorting networks, this approach finds better solutions, and finds them faster and more consistently than standard methods. It is therefore a promising approach to solving deceptive problems through multi-objective optimization.

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Enhanced Optimization with Composite Objectives and Novelty Selection

Shahrzad¹,

Fink²,

Miikkulainen³

2018

The 2018 Conference on Artificial Life

View full text Add to dashboard Cite

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Ortus: An emotion--driven approach to (artificial) biological intelligence

McDonald

Grimes

Breen

2017

Proceedings of the 14th European Conference on Artificial Life ECAL 2017

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Ortus is a simple virtual organism that also serves as an initial framework for investigating and developing biologicallybased artificial intelligence. Born from a goal to create complex virtual intelligence and an initial attempt to model C. elegans, Ortus implements a number of mechanisms observed in organic nervous systems, and attempts to fill in unknowns based upon plausible biological implementations and psychological observations. Implemented mechanisms include excitatory and inhibitory chemical synapses, bidirectional gap junctions, and Hebbian learning with its Stentian extension. We present an initial experiment that showcases Ortus' fundamental principles; specifically, a cyclic respiratory circuit, and emotionally-driven associative learning with respect to an input stimulus. Finally, we discuss the implications and future directions for Ortus and similar systems.

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Fully Autonomous Real-Time Autoencoder-Augmented Hebbian Learning through the Collection of Novel Experiences

Cited by 2 publications

References 24 publications

Enhanced Optimization with Composite Objectives and Novelty Selection

Enhanced Optimization with Composite Objectives and Novelty Selection

Ortus: An emotion--driven approach to (artificial) biological intelligence

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