1,016 research outputs found
Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes
I argue that data becomes temporarily interesting by itself to some
self-improving, but computationally limited, subjective observer once he learns
to predict or compress the data in a better way, thus making it subjectively
simpler and more beautiful. Curiosity is the desire to create or discover more
non-random, non-arbitrary, regular data that is novel and surprising not in the
traditional sense of Boltzmann and Shannon but in the sense that it allows for
compression progress because its regularity was not yet known. This drive
maximizes interestingness, the first derivative of subjective beauty or
compressibility, that is, the steepness of the learning curve. It motivates
exploring infants, pure mathematicians, composers, artists, dancers, comedians,
yourself, and (since 1990) artificial systems.Comment: 35 pages, 3 figures, based on KES 2008 keynote and ALT 2007 / DS 2007
joint invited lectur
Deep Reinforcement Learning for Artificial Upwelling Energy Management
The potential of artificial upwelling (AU) as a means of lifting
nutrient-rich bottom water to the surface, stimulating seaweed growth, and
consequently enhancing ocean carbon sequestration, has been gaining increasing
attention in recent years. This has led to the development of the first
solar-powered and air-lifted AU system (AUS) in China. However, efficient
scheduling of air injection systems remains a crucial challenge in operating
AUS, as it holds the potential to significantly improve system efficiency.
Conventional approaches based on rules or models are often impractical due to
the complex and heterogeneous nature of the marine environment and its
associated disturbances. To address this challenge, we propose a novel energy
management approach that utilizes deep reinforcement learning (DRL) algorithm
to develop efficient strategies for operating AUS. Through extensive
simulations, we evaluate the performance of our algorithm and demonstrate its
superior effectiveness over traditional rule-based approaches and other DRL
algorithms in reducing energy wastage while ensuring the stable and efficient
operation of AUS. Our findings suggest that a DRL-based approach offers a
promising way for improving the efficiency of AUS and enhancing the
sustainability of seaweed cultivation and carbon sequestration in the ocean.Comment: 31 pages, 13 figure
Language Modeling Is Compression
It has long been established that predictive models can be transformed into
lossless compressors and vice versa. Incidentally, in recent years, the machine
learning community has focused on training increasingly large and powerful
self-supervised (language) models. Since these large language models exhibit
impressive predictive capabilities, they are well-positioned to be strong
compressors. In this work, we advocate for viewing the prediction problem
through the lens of compression and evaluate the compression capabilities of
large (foundation) models. We show that large language models are powerful
general-purpose predictors and that the compression viewpoint provides novel
insights into scaling laws, tokenization, and in-context learning. For example,
Chinchilla 70B, while trained primarily on text, compresses ImageNet patches to
43.4% and LibriSpeech samples to 16.4% of their raw size, beating
domain-specific compressors like PNG (58.5%) or FLAC (30.3%), respectively.
Finally, we show that the prediction-compression equivalence allows us to use
any compressor (like gzip) to build a conditional generative model
06051 Abstracts Collection -- Kolmogorov Complexity and Applications
From 29.01.06 to 03.02.06, the Dagstuhl Seminar 06051 ``Kolmogorov Complexity and Applications\u27\u27 was held in the International Conference and Research Center (IBFI),
Schloss Dagstuhl. During the seminar, several participants presented
their current research, and ongoing work and open problems were
discussed. Abstracts of the presentations given during the seminar
as well as abstracts of seminar results and ideas are put together
in this paper. The first section describes the seminar topics and
goals in general. Links to extended abstracts or full papers are
provided, if available
Advanced Warehouse Energy Storage System Control Using Deep Supervised and Reinforcement Learning
The world is undergoing a shift from fossil fuels to renewable energy sources due to the threat of global warming, which has led to a substantial increase in complex buildingintegrated energy systems. These systems increasingly feature local renewable energy production and energy storage systems that require intelligent control algorithms. Traditional approaches, such as rule-based algorithms, are dependent upon timeconsuming human expert design and maintenance to control the energy systems efficiently. Although machine learning has gained increasing amounts of research attention in recent years, its application to energy cost optimization in warehouses still remains in a relatively early stage. Suggested newer approaches are often too complex to implement efficiently, very computationally expensive, or lacking in performance. This Ph.D. thesis explores, designs, and verifies the use of deep learning and reinforcement learning approaches to solve the bottleneck of human expert resource dependency with respect to efficient control of complex building-integrated energy systems. A technologically advanced smart warehouse for food storage and distribution is utilized as acase study. The warehouse has a commercially available Intelligent Energy ManagementSystem (IEMS).publishedVersio
Decomposition of a Cooling Plant for Energy Efficiency Optimization Using OptTopo
The operation of industrial supply technology is a broad field for optimization. Industrial cooling plants are often (a) composed of several components, (b) linked using network technology, (c) physically interconnected, and (d) complex regarding the effect of set-points and operating points in every entity. This leads to the possibility of overall optimization. An example containing a cooling tower, water circulations, and chillers entails a non-linear optimization problem with five dimensions. The decomposition of such a system allows the modeling of separate subsystems which can be structured according to the physical topology. An established method for energy performance indicators (EnPI) helps to formulate an optimization problem in a coherent way. The novel optimization algorithm OptTopo strives for efficient set-points by traversing a graph representation of the overall system. The advantages are (a) the ability to combine models of several types (e.g., neural networks and polynomials) and (b) an constant runtime independent from the number of operation points requested because new optimization needs just to be performed in case of plant model changes. An experimental implementation of the algorithm is validated using a simscape simulation. For a batch of five requests, OptTopo needs 61 (Formula presented.) while the solvers Cobyla, SDPEN, and COUENNE need 0.3 min, 1.4 min, and 3.1 min, respectively. OptTopo achieves an efficiency improvement similar to that of established solvers. This paper demonstrates the general feasibility of the concept and fortifies further improvements to reduce computing time
On-line PID tuning for engine idle-speed control using continuous action reinforcement learning automata
PID systems are widely used to apply control without the need to obtain a dynamic model. However, the
performance of controllers designed using standard on-line tuning methods, such as Ziegler-Nichols, can often be
significantly improved. In this paper the tuning process is automated through the use of continuous action
reinforcement learning automata (CARLA). These are used to simultaneously tune the parameters of a three term
controller on-line to minimise a performance objective. Here the method is demonstrated in the context of engine
idle speed control; the algorithm is first applied in simulation on a nominal engine model, and this is followed by
a practical study using a Ford Zetec engine in a test cell. The CARLA provides marked performance benefits
over a comparable Ziegler-Nichols tuned controller in this application
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