90,603 research outputs found
Bioclimatism in Architecture : an evolutionary perspective
peer reviewedFundamentals of vernacular architecture have been used in bioclimatic architecture which has gradually become the inspiration of various movements in contemporary architecture. The study points out that the development of bioclimatism in architecture has followed the pattern of a natural evolutionary process in which ânatural selectionâ is likely motivated by several factors, including resources and environment problems, and driven by different mechanisms including novel building design concepts and methods, new standards and codes, discoveries in building science and construction costs. This study is an effort aimed to clarify the evolution process of the bioclimatic approach in architecture over time and its influences on contemporary movements in architecture. The paper shows also that the evolutionary theory generated new scientific tools able to improve building design thanks to simulation-based optimization methods applied to building performances. Finally, this study investigates new motivations in the era of climate change whose effects are expected to introduce more challenges as well as more trends towards a sustainable built environment through the new concept of Eco-adaptive architecture
The evolutionary origins of volition
It appears to be a straightforward implication of distributed cognition principles that there is no integrated executive control system (e.g. Brooks 1991, Clark 1997). If distributed cognition is taken as a credible paradigm for cognitive science this in turn presents a challenge to volition because the concept of volition assumes integrated information processing and action control. For instance the process of forming a goal should integrate information about the available action options. If the goal is acted upon these processes should control motor behavior. If there were no executive system then it would seem that processes of action selection and performance couldnât be functionally integrated in the right way. The apparently centralized decision and action control processes of volition would be an illusion arising from the competitive and cooperative interaction of many relatively simple cognitive systems. Here I will make a case that this conclusion is not well-founded. Prima facie it is not clear that distributed organization can achieve coherent functional activity when there are many complex interacting systems, there is high potential for interference between systems, and there is a need for focus. Resolving conflict and providing focus are key reasons why executive systems have been proposed (Baddeley 1986, Norman and Shallice 1986, Posner and Raichle 1994). This chapter develops an extended theoretical argument based on this idea, according to which selective pressures operating in the evolution of cognition favor high order control organization with a âhighest-orderâ control system that performs executive functions
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integration of recent Machine Learning methods (based in particular on Deep
Learning and Recurrent Neural Networks) with more traditional ones (e.g.
Monte-Carlo tree search, goal babbling exploration or addressable memory
systems). Regarding embodiment, we note that the traditional benchmark tasks
(e.g. visual classification or board games) are becoming obsolete as
state-of-the-art learning algorithms approach or even surpass human performance
in most of them, having recently encouraged the development of first-person 3D
game platforms embedding realistic physics. Building upon this analysis, we
first propose an embodied cognitive architecture integrating heterogenous
sub-fields of Artificial Intelligence into a unified framework. We demonstrate
the utility of our approach by showing how major contributions of the field can
be expressed within the proposed framework. We then claim that benchmarking
environments need to reproduce ecologically-valid conditions for bootstrapping
the acquisition of increasingly complex cognitive skills through the concept of
a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017
conference (Lisbon, Portugal
Exploiting Evolution for an Adaptive Drift-Robust Classifier in Chemical Sensing
Gas chemical sensors are strongly affected by drift, i.e., changes in sensors' response with time, that may turn statistical models commonly used for classification completely useless after a period of time. This paper presents a new classifier that embeds an adaptive stage able to reduce drift effects. The proposed system exploits a state-of-the-art evolutionary strategy to iteratively tweak the coefficients of a linear transformation able to transparently transform raw measures in order to mitigate the negative effects of the drift. The system operates continuously. The optimal correction strategy is learnt without a-priori models or other hypothesis on the behavior of physical-chemical sensors. Experimental results demonstrate the efficacy of the approach on a real problem
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
Different evolutionary paths to complexity for small and large populations of digital organisms
A major aim of evolutionary biology is to explain the respective roles of
adaptive versus non-adaptive changes in the evolution of complexity. While
selection is certainly responsible for the spread and maintenance of complex
phenotypes, this does not automatically imply that strong selection enhances
the chance for the emergence of novel traits, that is, the origination of
complexity. Population size is one parameter that alters the relative
importance of adaptive and non-adaptive processes: as population size
decreases, selection weakens and genetic drift grows in importance. Because of
this relationship, many theories invoke a role for population size in the
evolution of complexity. Such theories are difficult to test empirically
because of the time required for the evolution of complexity in biological
populations. Here, we used digital experimental evolution to test whether large
or small asexual populations tend to evolve greater complexity. We find that
both small and large---but not intermediate-sized---populations are favored to
evolve larger genomes, which provides the opportunity for subsequent increases
in phenotypic complexity. However, small and large populations followed
different evolutionary paths towards these novel traits. Small populations
evolved larger genomes by fixing slightly deleterious insertions, while large
populations fixed rare beneficial insertions that increased genome size. These
results demonstrate that genetic drift can lead to the evolution of complexity
in small populations and that purifying selection is not powerful enough to
prevent the evolution of complexity in large populations.Comment: 22 pages, 5 figures, 7 Supporting Figures and 1 Supporting Tabl
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