Automatically Discovering Properties That Specify the Latent Behavior of UML Models

Goldsby, Heather J.; Cheng, Betty H. C.

doi:10.1007/978-3-642-16145-2_22

Cited by 20 publications

(8 citation statements)

References 22 publications

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“…The benefits of divergent search were further confirmed through similar experiments by Mouret and Doncieux (2009) and Mouret and Doncieux (2012), who use the synonymous term behavioral diversity. These initial studies were followed by a wave of interest in divergent search algorithms as researchers explored their potential for discovery and open-endedness (Risi et al, 2009(Risi et al, , 2011Soltoggio and Jones, 2009;Doucette and Heywood, 2010;Goldsby and Cheng, 2010;Graening et al, 2010;Krcah, 2010;Kistemaker and Whiteson, 2011;Woolley and Stanley, 2011;Liapis et al, 2013a,b;Martinez et al, 2013;Morse et al, 2013;Naredo and Trujillo, 2013;Risi and Stanley, 2013). However, a clear missing ingredient from pure novelty or behavioral diversity techniques is a complementary notion of objective quality.…”

Section: Early Divergent Search Algorithmsmentioning

confidence: 99%

Quality Diversity: A New Frontier for Evolutionary Computation

2016

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While evolutionary computation and evolutionary robotics take inspiration from nature, they have long focused mainly on problems of performance optimization. Yet, evolution in nature can be interpreted as more nuanced than a process of simple optimization. In particular, natural evolution is a divergent search that optimizes locally within each niche as it simultaneously diversifies. This tendency to discover both quality and diversity at the same time differs from many of the conventional algorithms of machine learning, and also thereby suggests a different foundation for inferring the approach of greatest potential for evolutionary algorithms. In fact, several recent evolutionary algorithms called quality diversity (QD) algorithms (e.g., novelty search with local competition and MAP-Elites) have drawn inspiration from this more nuanced view, aiming to fill a space of possibilities with the best possible example of each type of achievable behavior. The result is a new class of algorithms that return an archive of diverse, high-quality behaviors in a single run. The aim in this paper is to study the application of QD algorithms in challenging environments (in particular complex mazes) to establish their best practices for ambitious domains in the future. In addition to providing insight into cases when QD succeeds and fails, a new approach is investigated that hybridizes multiple views of behaviors (called behavior characterizations) in the same run, which succeeds in overcoming some of the challenges associated with searching for QD with respect to a behavior characterization that is not necessarily sufficient for generating both quality and diversity at the same time.

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Section: Early Divergent Search Algorithmsmentioning

confidence: 99%

Quality Diversity: A New Frontier for Evolutionary Computation

2016

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“…Heather et al [47] generate linear temporal logic properties that specify the latent behaviour of an existing UML diagram. The key component of their approach is an evolutionary-computation called MARPLE.…”

Section: Specification Of Security Requirementsmentioning

confidence: 99%

Specification, verification, and quantification of security in model-based systems

Ouchani

Debbabi

2015

Computing

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Modern systems are more and more complex and security has become a key component in the success of software and systems development. The main challenge encountered in industry as well as in academia is to develop secure products, prove their security correctness, measure their resilience to attacks, and check if vulnerabilities exist. In this paper, we review the state-of-the-art related to security specification, verification, and quantification for software and systems that are modeled by using UML or SysML language. The reviewed work fall into the field of secure software and systems engineering that aims at fulfilling the security as an afterthought in the development of secure systems.

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“…The generality of results from novelty search [60,59,57,87,58,37,31,76,50,95,55] is provocative and may hint at the importance of non-adaptive forces to the creativity of natural evolution. At minimum it provides a concrete example of how non-adaptive forces can consistently produce artifacts with the superficial appearance of adaptation.…”

Section: Novelty Searchmentioning

confidence: 99%

Investigating Biological Assumptions through Radical Reimplementation

Lehman

Stanley

2015

Artificial Life

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An important goal in both artificial life and biology is uncovering the most general principles underlying life, which might catalyze both our understanding of life and engineering life-like machines. While many such general principles have been hypothesized, conclusively testing them is difficult because life on Earth provides only a singular example from which to infer. To circumvent this limitation, this paper formalizes an approach called radical reimplementation. The idea is to investigate an abstract biological hypothesis by intentionally reimplementing its main principles to diverge maximally from existing natural examples. If the reimplementation successfully exhibits properties resembling biology it may better support the underlying hypothesis than an alternative example inspired more directly by nature. The approach thereby provides a principled alternative to a common tradition of defending and minimizing deviations from nature in artificial life. This work reviews examples that can be interpreted through the lens of radical reimplementation to yield potential insights into biology despite having purposefully unnatural experimental setups. In this way, radical reimplementation can help renew the relevance of computational systems for investigating biological theory and can act as a practical philosophical tool to help separate the fundamental features of terrestrial biology from the epiphenomenal.

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Automatically Discovering Properties That Specify the Latent Behavior of UML Models

Cited by 20 publications

References 22 publications

Quality Diversity: A New Frontier for Evolutionary Computation

Quality Diversity: A New Frontier for Evolutionary Computation

Specification, verification, and quantification of security in model-based systems

Investigating Biological Assumptions through Radical Reimplementation

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