696 research outputs found
Emergent diversity in an open-ended evolving virtual community
Understanding the dynamics of biodiversity has
become an important line of research in theoretical ecology and,
in particular, conservation biology. However, studying the evolution
of ecological communities under traditional modeling approaches
based on differential calculus requires speciesʼ characteristics to be
predefined, which limits the generality of the results. An alternative
but less standardized methodology relies on intensive computer
simulation of evolving communities made of simple, explicitly
described individuals. We study here the formation, evolution, and
diversity dynamics of a community of virtual plants with a novel
individual-centered model involving three different scales: the
genetic, the developmental, and the physiological scales. It constitutes
an original attempt at combining development, evolution, and
population dynamics (based on multi-agent interactions) into one
comprehensive, yet simple model. In this world, we observe that our
simulated plants evolve increasingly elaborate canopies, which are
capable of intercepting ever greater amounts of light. Generated
morphologies vary from the simplest one-branch structure of
promoter plants to a complex arborization of several hundred
thousand branches in highly evolved variants. On the population
scale, the heterogeneous spatial structuration of the plant community
at each generation depends solely on the evolution of its component
plants. Using this virtual data, the morphologies and the dynamics
of diversity production were analyzed by various statistical methods,
based on genotypic and phenotypic distance metrics. The results
demonstrate that diversity can spontaneously emerge in a community
of mutually interacting individuals under the influence of specific
environmental conditions.This research
was partially supported by a grant for the GENEX project (P09-TIC-5123) from the Consejería de
Innovación y Ciencia de Andalucía. J.D.F. was supported by a FPU grant from the Spanish Ministerio
de Educación. R.D. wishes to thank the Région Ile-de-France for supporting his research position at
the Complex Systems Institute, Paris Ile-de-France
Adapting models of visual aesthetics for personalized content creation
This paper introduces a search-based approach to
personalized content generation with respect to visual aesthetics.
The approach is based on a two-step adaptation procedure
where (1) the evaluation function that characterizes the content
is adjusted to match the visual aesthetics of users and (2) the
content itself is optimized based on the personalized evaluation
function. To test the efficacy of the approach we design fitness
functions based on universal properties of visual perception,
inspired by psychological and neurobiological research. Using
these visual properties we generate aesthetically pleasing 2D
game spaceships via neuroevolutionary constrained optimization
and evaluate the impact of the designed visual properties on the
generated spaceships. The offline generated spaceships are used
as the initial population of an interactive evolution experiment in
which players are asked to choose spaceships according to their
visual taste: the impact of the various visual properties is adjusted
based on player preferences and new content is generated online
based on the updated computational model of visual aesthetics
of the player. Results are presented which show the potential of
the approach in generating content which is based on subjective
criteria of visual aesthetics.Thanks to all the participants of the interactive evolution
experiement. The research was supported, in part, by the
FP7 ICT project SIREN (project no: 258453) and by the
Danish Research Agency, Ministry of Science, Technology
and Innovation project AGameComIn; project number: 274-
09-0083.peer-reviewe
Evolving Intelligent Multimodal Gameplay Agents and Decision Makers with Neuroevolution
�Super Mario Bros� is a difficult platforming game that requires the use of multiple behavioral modes to complete different gameplay elements such as: collecting coins, dodging enemies and getting to the end of the level. Methods for creating intelligent game playing agents have previously used human designed behavior policy for each gameplay state or by combining gameplay goals into a single task to be learned. This thesis assesses the development and method of training machines to promote multiple modes of behavior within neural network controllers. These controllers utilize the concept of evolution through multi-objective optimization for the test bench platform game system �MarioAI�. Artificial neural networks were evolved to exhibit complex and multimodal behavior using multiple sub objectives of the game; and thus overcome the non-linear, noisy, and fractured game environment. Experiments were conducted with the purpose of creating multiple Pareto-optimal solutions of quality with differing behavioral aspects. These solutions were then discerned by a Decision Maker Neural Network Ensemble that had been evolved to pick the best solution according to game level. This Decision Maker Ensemble proved to be able to learn on minimal information and provide the highest overall game score. The results of this thesis show that it�s possible to train agents on sub objectives to teach multiple forms of complex behavior that can then be abstractly chosen by an evolved Decision Maker to provide a better outcome than agents that were trained specifically towards that single solution.Electrical Engineerin
Evolutionary Genetics of Tetrodotoxin (TTX) Resistance in Snakes: Tracking a Feeding Adaptation from Populations Through Clades
Understanding the nature of adaptive evolution has been the recent focus of research detailing the genetic basis of adaptation and theoretical work describing the mechanics of adaptive evolution. Nevertheless, key questions regarding the process of adaptive evolution remain. Ultimately, a detailed description of the ecological context, evolutionary history, and genetic basis of adaptations is required to advance our understanding of adaptive evolution. To address some of the contemporary issues surrounding adaptive evolution, I examine phenotypic and genotypic changes in a snake feeding adaptation. Adaptations can arise through fixation of novel mutations or recruitment of existing variation. Some populations of the garter snakes Thamnophis sirtalis, T. couchii, and T. atratus possess elevated resistance to tetrodotoxin (TTX), the lethal toxin of their newt prey. I show that TTX resistance has evolved independently through amino acid changes at critical sites in a voltage-gated sodium channel protein (Nav1.4) targeted by TTX. Thus, adaptive evolution has occurred multiple times in garter snakes via de novo acquisition of beneficial mutations. Detailing the genetic basis of adaptive variation in natural populations is the first step towards understanding the tempo and mode of adaptive evolution. I evaluate the contribution of Nav1.4 alleles to TTX resistance in two garter snake species from central coastal California. Allelic variation in Nav1.4 explains 29% and 98% of the variation in TTX resistance in T. atratus and T. sirtalis, respectively, demonstrating that Nav1.4 is a major effect locus. The simple genetic architecture of TTX resistance in garter snakes may significantly impact the dynamics of trait change and coevolution. Patterns of convergent evolution are cited as some of the most compelling examples of the strength of natural selection in shaping organismal diversity. Yet repeated patterns may tell us as much about the constraints that restrict evolution as about the importance of natural selection. I present data on convergent molecular adaptations in parallel arms races between diverse snakes and amphibians from across the globe. Six snake species that prey on TTX bearing amphibians have independently acquired amino acid changes in Nav1.4. The derived mutations are clustered in two portions of the gene, often involving the same sites and substitutions. While a number of amino acid changes can make Nav1.4 insensitive to TTX, most of these negatively impact or abolish the ion-conducting function of the protein. Thus, intramolecular pleiotropy likely prevents most replacements from becoming fixed and imposes limits on protein evolution
Enhancing Exploration and Safety in Deep Reinforcement Learning
A Deep Reinforcement Learning (DRL) agent tries to learn a policy maximizing a long-term objective by trials and errors in large state spaces. However, this learning paradigm requires a non-trivial amount of interactions in the environment to achieve good performance. Moreover, critical applications, such as robotics, typically involve safety criteria to consider while designing novel DRL solutions. Hence, devising safe learning approaches with efficient exploration is crucial to avoid getting stuck in local optima, failing to learn properly, or causing damages to the surrounding environment. This thesis focuses on developing Deep Reinforcement Learning algorithms to foster efficient exploration and safer behaviors in simulation and real domains of interest, ranging from robotics to multi-agent systems. To this end, we rely both on standard benchmarks, such as SafetyGym, and robotic tasks widely adopted in the literature (e.g., manipulation, navigation). This variety of problems is crucial to assess the statistical significance of our empirical studies and the generalization skills of our approaches. We initially benchmark the sample efficiency versus performance trade-off between value-based and policy-gradient algorithms. This part highlights the benefits of using non-standard simulation environments (i.e., Unity), which also facilitates the development of further optimization for DRL. We also discuss the limitations of standard evaluation metrics (e.g., return) in characterizing the actual behaviors of a policy, proposing the use of Formal Verification (FV) as a practical methodology to evaluate behaviors over desired specifications. The second part introduces Evolutionary Algorithms (EAs) as a gradient-free complimentary optimization strategy. In detail, we combine population-based and gradient-based DRL to diversify exploration and improve performance both in single and multi-agent applications. For the latter, we discuss how prior Multi-Agent (Deep) Reinforcement Learning (MARL) approaches hinder exploration, proposing an architecture that favors cooperation without affecting exploration
The evolution of diversity in the structure and function of artificial organisms
Life on Earth has been shaped by evolutionary processes into a marvelous diversity of form and function, at all levels from melecules to ecosystems. It can be expected that no single conceptual framework ca encompass all the aspects of the evolution of diversity. This thesis explores this question from three different points of view: the role of developmental processes, the role of evolutionary dynamics, and the interplay between the body's control system
Using MapReduce Streaming for Distributed Life Simulation on the Cloud
Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp
\u3cem\u3eTechnological Tethereds\u3c/em\u3e: Potential Impact of Untrustworthy Artificial Intelligence in Criminal Justice Risk Assessment Instruments
Issues of racial inequality and violence are front and center today, as are issues surrounding artificial intelligence (“AI”). This Article, written by a law professor who is also a computer scientist, takes a deep dive into understanding how and why hacked and rogue AI creates unlawful and unfair outcomes, particularly for persons of color.
Black Americans are disproportionally featured in criminal justice, and their stories are obfuscated. The seemingly endless back-to-back murders of George Floyd, Breonna Taylor, Ahmaud Arbery, and heartbreakingly countless others have finally shaken the United States from its slumbering journey towards intentional criminal justice reform. Myths about Black crime and criminals are embedded in the data collected by AI and do not tell the truth about race and crime. However, the number of Black people harmed by hacked and rogue AI will dwarf all historical records, and the gravity of harm is incomprehensible.
The lack of technical transparency and legal accountability leaves wrongfully convicted defendants without legal remedies if they are unlawfully detained based on a cyberattack, faulty or hacked data, or rogue AI. Scholars and engineers acknowledge that the artificial intelligence that is giving recommendations to law enforcement, prosecutors, judges, and parole boards lacks the common sense of an eighteen-month-old child. This Article reviews the ways AI is used in the legal system and the courts’ response to this use. It outlines the design schemes of proprietary risk assessment instruments used in the criminal justice system, outlines potential legal theories for victims, and provides recommendations for legal and technical remedies to victims of hacked data in criminal justice risk assessment instruments. It concludes that, with proper oversight, AI can increase fairness in the criminal justice system, but without this oversight, AI-based products will further exacerbate the extinguishment of liberty interests enshrined in the Constitution.
According to anti-lynching advocate, Ida B. Wells-Barnett, “The way to right wrongs is to turn the light of truth upon them.” Thus, transparency is vital to safeguarding equity through AI design and must be the first step. The Article seeks ways to provide that transparency, for the benefit of all America, but particularly persons of color who are far more likely to be impacted by AI deficiencies. It also suggests legal reforms that will help plaintiffs recover when AI goes rogue
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