Forcing neurocontrollers to exploit sensory symmetry through hard-wired modularity in the game of Cellz

Several attempts have been made in the past to construct encoding schemes that allow modularity to emerge in evolving systems, but success is limited. We believe that in order to create successful and scalable encodings for emerging modularity, we first need to explore the benefits of different types of modularity by hard-wiring these into evolvable systems. In this paper we explore different ways of exploiting sensory symmetry inherent in the agent in the simple game Cellz by evolving symmetrically identical modules. It is concluded that significant increases in both speed of evolution and final fitness can be achieved relative to monolithic controllers. Furthermore, we show that a simple function approximation task that exhibits sensory symmetry can be used as a quick approximate measure of the utility of an encoding scheme for the more complex game-playing task

Evolutionary Design of Artificial Neural Networks Using a Descriptive Encoding Language

Automated design of artificial neural networks by evolutionary algorithms (neuroevolution) has generated much recent research both because successful approaches will facilitate wide-spread use of intelligent systems based on neural networks, and because it will shed light on our understanding of how "real" neural networks may have evolved. The main challenge in neuroevolution is that the search space of neural network architectures and their corresponding optimal weights can be high-dimensional and disparate, and therefore evolution may not discover an optimal network even if it exists. In this dissertation, I present a high-level encoding language that can be used to restrict the general search space of neural networks, and implement a problem-independent design system based on this encoding language. I show that this encoding scheme works effectively in 1) describing the search space in which evolution occurs; 2) specifying the initial configuration and evolutionary parameters; and 3) generating the final neural networks resulting from the evolutionary process in a human-readable manner. Evolved networks for ``n-partition problems'' demonstrate that this approach can evolve high-performance network architectures, and show by example that a small parsimony factor in the fitness measure can lead to the emergence of modular networks. Further, this approach is shown to work for encoding recurrent neural networks for a temporal sequence generation problem, and the trade-offs between various recurrent network architectures are systematically compared via multi-objective optimization. Finally, it is shown that this system can be extended to address reinforcement learning problems by evolving architectures and connection weights in a hierarchical manner. Experimental results support the conclusion that hierarchical evolutionary approaches integrated in a system having a high-level descriptive encoding language can be useful in designing modular networks, including those that have recurrent connectivity

Duplication of modules facilitates the evolution of functional specialization

The evolution of simulated robots with three different architectures is studied. We compared a non-modular feed forward network, a hardwired modular and a duplication-based modular motor control network. We conclude that both modular architectures outperform the non-modular architecture, both in terms of rate of adaptation as well as the level of adaptation achieved. The main difference between the hardwired and duplication-based modular architectures is that in the latter the modules reached a much higher degree of functional specialization of their motor control units with regard to high level behavioral functions. The hardwired architectures reach the same level of performance, but have a more distributed assignment of functional tasks to the motor control units. We conclude that the mechanism through which functional specialization is achieved is similar to the mechanism proposed for the evolution of duplicated genes. It is found that the duplication of multifunctional modules first leads to a change in the regulation of the module, leading to a differentiation of the functional context in which the module is used. Then the module adapts to the new functional context. After this second step the system is locked into a functionally specialized state. We suggest that functional specialization may be an evolutionary absorption state

Analyzing Modular CNN Architectures for Joint Depth Prediction and Semantic Segmentation

This paper addresses the task of designing a modular neural network architecture that jointly solves different tasks. As an example we use the tasks of depth estimation and semantic segmentation given a single RGB image. The main focus of this work is to analyze the cross-modality influence between depth and semantic prediction maps on their joint refinement. While most previous works solely focus on measuring improvements in accuracy, we propose a way to quantify the cross-modality influence. We show that there is a relationship between final accuracy and cross-modality influence, although not a simple linear one. Hence a larger cross-modality influence does not necessarily translate into an improved accuracy. We find that a beneficial balance between the cross-modality influences can be achieved by network architecture and conjecture that this relationship can be utilized to understand different network design choices. Towards this end we propose a Convolutional Neural Network (CNN) architecture that fuses the state of the state-of-the-art results for depth estimation and semantic labeling. By balancing the cross-modality influences between depth and semantic prediction, we achieve improved results for both tasks using the NYU-Depth v2 benchmark.Comment: Accepted to ICRA 201