We compare a novel Knowledge-based Reinforcement Learning (KB-RL) approach with the traditional Neural Network (NN) method in solving a classical task of the Artificial Intelligence (AI) field. Neural networks became very prominent in recent years and, combined with Reinforcement Learning, proved to be very effective for one of the frontier challenges in AI - playing the game of Go. Our experiment shows that a KB-RL system is able to outperform a NN in a task typical for NN, such as optimizing a regression problem. Furthermore, KB-RL offers a range of advantages in comparison to the traditional Machine Learning methods. Particularly, there is no need for a large dataset to start and succeed with this approach, its learning process takes considerably less effort, and its decisions are fully controllable, explicit and predictable.
As the result of NNs' popularity, little attention has been paid to other AI approaches, such as symbolism, evolutionarism, or Bayesian statistics @cite_0 . More recently, though, new studies emerge that show successful results in applying alternative approaches to AI tasks. For example, Denis G Willson at el. show that their evolutionary algorithm can outperform the deep neural network approach in playing Atari games @cite_2 . More studies are targeting General AI as, for instance, the CYC project @cite_19 . Some researchers advocate that the combination of different techniques into one powerful AI system is the way to go @cite_8 .
@cite_0 @cite_19 @cite_8 @cite_2
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Research in neuroevolution---that is, evolving artificial neural networks (ANNs) through evolutionary algorithms---is inspired by the evolution of biological brains, which can contain trillions of connections. Yet while neuroevolution has produced successful results, the scale of natural brains remains far beyond reach. This article presents a method called hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) that aims to narrow this gap. HyperNEAT employs an indirect encoding called connective compositional pattern-producing networks (CPPNs) that can produce connectivity patterns with symmetries and repeating motifs by interpreting spatial patterns generated within a hypercube as connectivity patterns in a lower-dimensional space. This approach can exploit the geometry of the task by mapping its regularities onto the topology of the network, thereby shifting problem difficulty away from dimensionality to the underlying problem structure. Furthermore, connective CPPNs can represent the same connectivity pattern at any resolution, allowing ANNs to scale to new numbers of inputs and outputs without further evolution. HyperNEAT is demonstrated through visual discrimination and food-gathering tasks, including successful visual discrimination networks containing over eight million connections. The main conclusion is that the ability to explore the space of regular connectivity patterns opens up a new class of complex high-dimensional tasks to neuroevolution. Many different methods for combining human expertise with machine learning in general, and evolutionary computation in particular, are possible. Which of these methods work best, and do they outperform human design and machine design alone? In order to answer this question, a human-subject experiment for comparing human-assisted machine learning methods was conducted. Three different approaches, i.e. advice, shaping, and demonstration, were employed to assist a powerful machine learning technique (neuroevolution) on a collection of agent training tasks, and contrasted with both a completely manual approach (scripting) and a completely hands-off one (neuroevolution alone). The results show that, (1) human-assisted evolution outperforms a manual scripting approach, (2) unassisted evolution performs consistently well across domains, and (3) different methods of assisting neuroevolution outperform unassisted evolution on different tasks. If done right, human-assisted neuroevolution can therefore be a powerful technique for constructing intelligent agents. Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone. Our goal is to minimize human participation, so we employ evolutionary algorithms to discover such networks automatically. Despite significant computational requirements, we show that it is now possible to evolve models with accuracies within the range of those published in the last year. Specifically, we employ simple evolutionary techniques at unprecedented scales to discover models for the CIFAR-10 and CIFAR-100 datasets, starting from trivial initial conditions and reaching accuracies of 94.6 (95.6 for ensemble) and 77.0 , respectively. To do this, we use novel and intuitive mutation operators that navigate large search spaces; we stress that no human participation is required once evolution starts and that the output is a fully-trained model. Throughout this work, we place special emphasis on the repeatability of results, the variability in the outcomes and the computational requirements. Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone. Our goal is to minimize human participation, so we employ evolutionary algorithms to discover such networks automatically. Despite significant computational requirements, we show that it is now possible to evolve models with accuracies within the range of those published in the last year. Specifically, we employ simple evolutionary techniques at unprecedented scales to discover models for the CIFAR-10 and CIFAR-100 datasets, starting from trivial initial conditions and reaching accuracies of 94.6 (95.6 for ensemble) and 77.0 , respectively. To do this, we use novel and intuitive mutation operators that navigate large search spaces; we stress that no human participation is required once evolution starts and that the output is a fully-trained model. Throughout this work, we place special emphasis on the repeatability of results, the variability in the outcomes and the computational requirements.