POWERPLAY: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem

Most of computer science focuses on automatically solving given computational problems. I focus on automatically inventing or discovering problems in a way inspired by the playful behavior of animals and humans, to train a more and more general problem solver from scratch in an unsupervised fashion. Consider the infinite set of all computable descriptions of tasks with possibly computable solutions. The novel algorithmic framework POWERPLAY (2011) continually searches the space of possible pairs of new tasks and modifications of the current problem solver, until it finds a more powerful problem solver that provably solves all previously learned tasks plus the new one, while the unmodified predecessor does not. Wow-effects are achieved by continually making previously learned skills more efficient such that they require less time and space. New skills may (partially) re-use previously learned skills. POWERPLAY's search orders candidate pairs of tasks and solver modifications by their conditional computational (time & space) complexity, given the stored experience so far. The new task and its corresponding task-solving skill are those first found and validated. The computational costs of validating new tasks need not grow with task repertoire size. POWERPLAY's ongoing search for novelty keeps breaking the generalization abilities of its present solver. This is related to Goedel's sequence of increasingly powerful formal theories based on adding formerly unprovable statements to the axioms without affecting previously provable theorems. The continually increasing repertoire of problem solving procedures can be exploited by a parallel search for solutions to additional externally posed tasks. POWERPLAY may be viewed as a greedy but practical implementation of basic principles of creativity. A first experimental analysis can be found in separate papers [53,54].Comment: 21 pages, additional connections to previous work, references to first experiments with POWERPLA

Learning the Designer's Preferences to Drive Evolution

This paper presents the Designer Preference Model, a data-driven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the user's design style to better assess the tool's procedurally generated content with respect to that user's preferences. Through this approach, we aim for increasing the user's agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking. We describe the details of this novel solution, as well as its implementation in the MI-CC tool the Evolutionary Dungeon Designer. We present and discuss our findings out of the initial tests carried out, spotting the open challenges for this combined line of research that integrates MI-CC with Procedural Content Generation through Machine Learning.Comment: 16 pages, Accepted and to appear in proceedings of the 23rd European Conference on the Applications of Evolutionary and bio-inspired Computation, EvoApplications 202

An Evolutionary Algorithm to Optimize Log/Restore Operations within Optimistic Simulation Platforms

In this work we address state recoverability in advanced optimistic simulation systems by proposing an evolutionary algorithm to optimize at run-time the parameters associated with state log/restore activities. Optimization takes place by adaptively selecting for each simulation object both (i) the best suited log mode (incremental vs non-incremental) and (ii) the corresponding optimal value of the log interval. Our performance optimization approach allows to indirectly cope with hidden effects (e.g., locality) as well as cross-object effects due to the variation of log/restore parameters for different simulation objects (e.g., rollback thrashing). Both of them are not captured by literature solutions based on analytical models of the overhead associated with log/restore tasks. More in detail, our evolutionary algorithm dynamically adjusts the log/restore parameters of distinct simulation objects as a whole, towards a well suited configuration. In such a way, we prevent negative effects on performance due to the biasing of the optimization towards individual simulation objects, which may cause reduced gains (or even decrease) in performance just due to the aforementioned hidden and/or cross-object phenomena. We also present an application-transparent implementation of the evolutionary algorithm within the ROme OpTimistic Simulator (ROOT-Sim), namely an open source, general purpose simulation environment designed according to the optimistic synchronization paradigm

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

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

Improving machine dynamics via geometry optimization

The central thesis of this paper is that the dynamic performance of machinery can be improved dramatically in certain cases through a systematic and meticulous evolutionary algorithm search through the space of all structural geometries permitted by manufacturing, cost and functional constraints. This is a cheap and elegant approach in scenarios where employing active control elements is impractical for reasons of cost and complexity. From an optimization perspective the challenge lies in the efficient, yet thorough global exploration of the multi-dimensional and multi-modal design spaces often yielded by such problems. Morevoer, the designs are often defined by a mixture of continuous and discrete variables - a task that evolutionary algorithms appear to be ideally suited for. In this article we discuss the specific case of the optimization of crop spraying machinery for improved uniformity of spray deposition, subject to structural weight and manufacturing constraints. Using a mixed variable evolutionary algorithm allowed us to optimize both shape and topology. Through this process we have managed to reduce the maximum roll angle of the sprayer by an order of magnitude , whilst allowing only relatively inexpensive changes to the baseline design. Further (though less dramatic) improvements were shown to be possible when we relaxed the cost constraint. We applied the same approach to the inverse problem of reducing the mass while maintaining an acceptable roll angle - a 2% improvement proved possible in this cas

Learning in evolutionary environments

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Automatic surrogate model type selection during the optimization of expensive black-box problems

The use of Surrogate Based Optimization (SBO) has become commonplace for optimizing expensive black-box simulation codes. A popular SBO method is the Efficient Global Optimization (EGO) approach. However, the performance of SBO methods critically depends on the quality of the guiding surrogate. In EGO the surrogate type is usually fixed to Kriging even though this may not be optimal for all problems. In this paper the authors propose to extend the well-known EGO method with an automatic surrogate model type selection framework that is able to dynamically select the best model type (including hybrid ensembles) depending on the data available so far. Hence, the expected improvement criterion will always be based on the best approximation available at each step of the optimization process. The approach is demonstrated on a structural optimization problem, i.e., reducing the stress on a truss-like structure. Results show that the proposed algorithm consequently finds better optimums than traditional kriging-based infill optimization