This paper presents a technique called evolving self-supervised neural networks - neural networks that can teach themselves, intrinsically motivated, without external supervision or reward. The proposed method presents some sort-of paradigm shift, and differs greatly from both traditional gradient-based learning and evolutionary algorithms in that it combines the metaphor of evolution and learning, more specifically self-learning, together, rather than treating these phenomena alternatively. I simulate a multi-agent system in which neural networks are used to control autonomous foraging agents with little domain knowledge. Experimental results show that only evolved self-supervised agents can demonstrate some sort of intelligent behaviour, but not evolution or self-learning alone. Indications for future work on evolving intelligence are also presented.
Generally learning in neural networks can be thought of as part of neural plasticity. There have been some other ideas, like evolving local learning rules to update the weights @cite_13 , evolution of neuromodulation which facilitates the information transfer between neurons in hopes of creating meta-learning @cite_14 . Please refer to @cite_23 for more recent studies on evolving plastic neural networks. In short, most of the work use and neural networks in single-agent environment, having no mutual interaction as they solve their own problems, having no effect on other's performance.
@cite_14 @cite_13 @cite_23
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This paper presents a computational theory on the roles of the ascending neuromodulatory systems from the viewpoint that they mediate the global signals that regulate the distributed learning mechanisms in the brain. Based on the review of experimental data and theoretical models, it is proposed that dopamine signals the error in reward prediction, serotonin controls the time scale of reward prediction, noradrenaline controls the randomness in action selection, and acetylcholine controls the speed of memory update. The possible interactions between those neuromodulators and the environment are predicted on the basis of computational theory of met alearning. This paper presents a new approach to neural modeling based on the idea of using an automated method to optimize the parameters of a synaptic learning rule The synaptic modi cation rule is considered as a parametric function This function has local inputs and is the same in many neurons We can use standard optimization methods to select appropriate parameters for a given type of task We also present a theoretical analysis permitting to study the generalization property of such parametric learning rules By generalization we mean the possibility for the learning rule to learn to solve new tasks Experiments were performed on three types of problems a biologically inspired circuit for conditioning in Aplysia Boolean functions linearly separable as well as non linearly separable and classi cation tasks The neural network architecture as well as the form and initial parameter values of the synaptic learning function can be designed using a priori knowledge Abstract Biological neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifelong learning. The interplay of these elements leads to the emergence of biological intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) employ simulated evolution in-silico to breed plastic neural networks with the aim to autonomously design and create learning systems. EPANN experiments evolve networks that include both innate properties and the ability to change and learn in response to experiences in different environments and problem domains. EPANNs’ aims include autonomously creating learning systems, bootstrapping learning from scratch, recovering performance in unseen conditions, testing the computational advantages of particular neural components, and deriving hypotheses on the emergence of biological learning. Thus, EPANNs may include a large variety of different neuron types and dynamics, network architectures, plasticity rules, and other factors. While EPANNs have seen considerable progress over the last two decades, current scientific and technological advances in artificial neural networks are setting the conditions for radically new approaches and results. Exploiting the increased availability of computational resources and of simulation environments, the often challenging task of hand-designing learning neural networks could be replaced by more autonomous and creative processes. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and possible developments are presented.