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The evolution of multicellular eukaryotes expresses two sorts of adaptations: local adaptations like fur or feathers, which characterize species in particular environments, and universal adaptations like microbiomes or sexual reproduction, which characterize most multicellulars in any environment. We reason that the mechanisms driving the universal adaptations of multicellulars should themselves be universal, and propose a mechanism based on properties of matter and systems: energy, entropy, and interaction. Energy from the sun, earth and beyond creates new arrangements and interactions. Metabolic networks channel some of this energy to form cooperating, interactive arrangements. Entropy, used here as a term for all forces that dismantle ordered structures (rather than as a physical quantity), acts as a selective force. Entropy selects for arrangements that resist it long enough to replicate, and dismantles those that do not. Interactions, energy-charged and dynamic, restrain entropy and enable survival and propagation of integrated living systems. This fosters survival-of-the-fitted - those entities that resist entropic destruction - and not only of the fittest - the entities with the greatest reproductive success. The \"unit\" of evolution is not a discrete entity, such as a gene, individual, or species; what evolves are collections of related interactions at multiple scales. Survival-of-the-fitted explains universal adaptations, including resident microbiomes, sexual reproduction, continuous diversification, programmed turnover, seemingly wasteful phenotypes, altruism, co-evolving environmental niches, and advancing complexity. Indeed survival-of-the-fittest may be a particular case of the survival-of-the-fitted mechanism, promoting local adaptations that express reproductive advantages in addition to resisting entropy. Survival-of-the-fitted accounts for phenomena that have been attributed to neutral evolution: in the face of entropy, there is no neutrality; all variations are challenged by ubiquitous energy and entropy, retaining those that are \"fit enough\". We propose experiments to test predictions of the survival-of-the-fitted theory, and discuss implications for the wellbeing of humans and the biosphere.

Commonly recognized effects of sexual reproduction include increased diversity, improved adaptability, enabling of DNA repair, constrained accumulation of deleterious mutations, and species genotype homogenization. Additionally, there are studies that show how sexual reproduction slows down certain evolutionary responses, offering advantages in population cumulative growth and stability over time and other metrics. Here, we contribute an observation of another distinct effect of sexual reproduction, focusing on retaining a species’s key traits. In an initial mathematical analysis and simulation, we show that in an environment where copying is prone to error, quantitative polygenic traits that are shared within a parents’ generation are transmitted to future generations under sexual reproduction with less deviation than under asexual reproduction. Furthermore, the model shows that this retention of common traits (abbr. RoCT), is driven by the very nature of mixing of parental traits, and occurs even before adding effects like trait-specific reproductive advantages, DNA repair, or the raising of reproductive barriers. Since survival of ecosystems depends on the ability of individuals to replace the networked interactions and interdependencies associated with failing, dying, or absent members of the same species, RoCT helps sustain species and ecosystems.

SignificanceAutonomous systems are replacing humans in a variety of tasks, and in the years to come, such systems will become central and crucial to human life. They will include vehicles of all kinds, medical and industrial robots, agricultural and manufacturing facilities, traffic management systems, and much more. While many organizations strive to develop the next generation of trustworthy, cost-effective autonomous systems, a major gap exists between the challenges in developing these and the state of the art. There is a crucial need for a common scientific and engineering foundation for developing these systems, which we term “autonomics.” We believe that such a foundation will dramatically accelerate the deployment and acceptance of high-quality autonomous systems, for the benefit of human society. The potential benefits of autonomous systems are obvious. However, there are still major issues to be dealt with before developing such systems becomes a commonplace engineering practice, with accepted and trustworthy deliverables. We argue that a solid, evolving, publicly available, community-controlled foundation for developing next-generation autonomous systems is a must, and term the desired foundation “autonomics.” We focus on three main challenges: 1) how to specify autonomous system behavior in the face of unpredictability; 2) how to carry out faithful analysis of system behavior with respect to rich environments that include humans, physical artifacts, and other systems; and 3) how to build such systems by combining executable modeling techniques from software engineering with artificial intelligence and machine learning.

The evolution of multicellular eukaryotes expresses two sorts of adaptations: local adaptations like fur or feathers, which characterize species in particular environments, and universal adaptations like microbiomes or sexual reproduction, which characterize most multicellulars in any environment. We reason that the mechanisms driving the universal adaptations of multicellulars should themselves be universal, and propose a mechanism based on properties of matter and systems: energy, entropy, and interaction. Energy from the sun, earth and beyond creates new arrangements and interactions. Metabolic networks channel some of this energy to form cooperating, interactive arrangements. Entropy, used here as a term for all forces that dismantle ordered structures (rather than as a physical quantity), acts as a selective force. Entropy selects for arrangements that resist it long enough to replicate, and dismantles those that do not. Interactions, energy-charged and dynamic, restrain entropy and enable survival and propagation of integrated living systems. This fosters survival-of-the-fitted - those entities that resist entropic destruction - and not only of the fittest - the entities with the greatest reproductive success. The \"unit\" of evolution is not a discrete entity, such as a gene, individual, or species; what evolves are collections of related interactions at multiple scales. Survival-of-the-fitted explains universal adaptations, including resident microbiomes, sexual reproduction, continuous diversification, programmed turnover, seemingly wasteful phenotypes, altruism, co-evolving environmental niches, and advancing complexity. Indeed survival-of-the-fittest may be a particular case of the survival-of-the-fitted mechanism, promoting local adaptations that express reproductive advantages in addition to resisting entropy. Survival-of-the-fitted accounts for phenomena that have been attributed to neutral evolution: in the face of entropy, there is no neutrality; all variations are challenged by ubiquitous energy and entropy, retaining those that are \"fit enough\". We propose experiments to test predictions of the survival-of-the-fitted theory, and discuss implications for the wellbeing of humans and the biosphere.

The potential benefits of autonomous systems are obvious. However, there are still major issues to be dealt with before developing such systems becomes a commonplace engineering practice, with accepted and trustworthy deliverables. We argue that a solid, evolving, publicly available, community-controlled foundation for developing next-generation autonomous systems is a must, and term the desired foundation “autonomics.” We focus on three main challenges: 1) how to specify autonomous system behavior in the face of unpredictability; 2) how to carry out faithful analysis of system behavior with respect to rich environments that include humans, physical artifacts, and other systems; and 3) howto build such systems by combining executable modeling techniques from software engineering with artificial intelligence and machine learning.

Deep learning (DL), which includes deep reinforcement learning (DRL), holds great promise for carrying out real-world tasks that human minds seem to cope with quite readily. That promise is already delivering extremely impressive results in a variety of areas. However, while DL-enabled systems achieve excellent performance, they are far from perfect. It has been demonstrated, in several domains, that DL systems can err when they encounter cases they had not hitherto encountered. Furthermore, the opacity of the produced agents makes it difficult to explain their behavior and ensure that they adhere to various requirements posed by human engineers. At the other end of the software development spectrum of methods, behavioral programming (BP) facilitates orderly system development using self-standing executable modules aligned with how humans intuitively describe desired system behavior. In this paper, we elaborate on different approaches for combining DRL with BP and, more generally, machine learning (ML) with executable specifications (ES). We begin by defining a framework for studying the various approaches, which can also be used to study new emerging approaches not covered here. We then briefly review state-of-the-art approaches to integrating ML with ES, continue with a focus on DRL, and then present the merits of integrating ML with BP. We conclude with guidelines on how this categorization can be used in decision making in system development, and outline future research challenges.

Deep reinforcement learning agents have achieved unprecedented results when learning to generalize from unstructured data. However, the “black-box” nature of the trained DRL agents makes it difficult to ensure that they adhere to various requirements posed by engineers. In this work, we put forth a novel technique for enhancing the reinforcement learning training loop, and specifically—its reward function, in a way that allows engineers to directly inject their expert knowledge into the training process. This allows us to make the trained agent adhere to multiple constraints of interest. Moreover, using scenario-based modeling techniques, our method allows users to formulate the defined constraints using advanced, well-established, behavioral modeling methods. This combination of such modeling methods together with ML learning tools produces agents that are both high performing and more likely to adhere to prescribed constraints. Furthermore, the resulting agents are more transparent and hence more maintainable. We demonstrate our technique by evaluating it on a case study from the domain of internet congestion control, and present promising results.

The evolution of multicellular eukaryotes expresses two sorts of adaptations: local adaptations like fur or feathers, which characterize species in particular environments, and universal adaptations like microbiomes or sexual reproduction, which characterize most multicellulars in any environment. We reason that the mechanisms driving the universal adaptations of multicellulars should themselves be universal, and propose a mechanism based on properties of matter and systems: energy, entropy, and interaction. Energy from the sun, earth and beyond creates new arrangements and interactions. Metabolic networks channel some of this energy to form cooperating, interactive arrangements. Entropy, used here as a term for all forces that dismantle ordered structures (rather than as a physical quantity), acts as a selective force. Entropy selects for arrangements that resist it long enough to replicate, and dismantles those that do not. Interactions, energy-charged and dynamic, restrain entropy and enable survival and propagation of integrated living systems. This fosters survival-of-the-fitted - those entities that resist entropic destruction - and not only of the fittest - the entities with the greatest reproductive success. The \"unit\" of evolution is not a discrete entity, such as a gene, individual, or species; what evolves are collections of related interactions at multiple scales. Survival-of-the-fitted explains universal adaptations, including resident microbiomes, sexual reproduction, continuous diversification, programmed turnover, seemingly wasteful phenotypes, altruism, co-evolving environmental niches, and advancing complexity. Indeed survival-of-the-fittest may be a particular case of the survival-of-the-fitted mechanism, promoting local adaptations that express reproductive advantages in addition to resisting entropy. Survival-of-the-fitted accounts for phenomena that have been attributed to neutral evolution: in the face of entropy, there is no neutrality; all variations are challenged by ubiquitous energy and entropy, retaining those that are \"fit enough\". We propose experiments to test predictions of the survival-of-the-fitted theory, and discuss implications for the wellbeing of humans and the biosphere.

We believe that future models of complex software and systems will combine the crucial traits of intuitiveness, compositionality, and executability. The importance of each of these to modeling is already well recognized, but our vision suggests a far more powerful synergy between them. First, models will be aligned with cognitive processes used by humans to think about system behavior and will be understood, and perhaps creatable, by almost anyone. Second, one will be able to build models incrementally, adding to, refining or sculpting away already-specified behaviors without changing most existing parts of the model. Third, there will be powerful ways to execute such intuitive and compositional models, in whole or in part, at any stage of the development. The presence of these three traits in a single artifact will blur the boundaries between natural-language requirements, formal models, and actual software, bringing in its wake a major advance in the way systems are built, and in their cost and quality. We propose the term runware to refer to this kind of higher level artifact.