Enhancing robot evolution through Lamarckian principles.

Evolutionary robot systems offer two principal advantages: an advanced way of developing robots through evolutionary optimization and a special research platform to conduct what-if experiments regarding questions about evolution. Our study sits at the intersection of these. We investigate the question "What if the 18th-century biologist Lamarck was not completely wrong and individual traits learned during a lifetime could be passed on to offspring through inheritance?" We research this issue through simulations with an evolutionary robot framework where morphologies (bodies) and controllers (brains) of robots are evolvable and robots also can improve their controllers through learning during their lifetime. Within this framework, we compare a Lamarckian system, where learned bits of the brain are inheritable, with a Darwinian system, where they are not. Analyzing simulations based on these systems, we obtain new insights about Lamarckian evolution dynamics and the interaction between evolution and learning. Specifically, we show that Lamarckism amplifies the emergence of 'morphological intelligence', the ability of a given robot body to acquire a good brain by learning, and identify the source of this success: newborn robots have a higher fitness because their inherited brains match their bodies better than those in a Darwinian system.

Practical hardware for evolvable robots.

The evolutionary robotics field offers the possibility of autonomously generating robots that are adapted to desired tasks by iteratively optimising across successive generations of robots with varying configurations until a high-performing candidate is found. The prohibitive time and cost of actually building this many robots means that most evolutionary robotics work is conducted in simulation, but to apply evolved robots to real-world problems, they must be implemented in hardware, which brings new challenges. This paper explores in detail the design of an example system for realising diverse evolved robot bodies, and specifically how this interacts with the evolutionary process. We discover that every aspect of the hardware implementation introduces constraints that change the evolutionary space, and exploring this interplay between hardware constraints and evolution is the key contribution of this paper. In simulation, any robot that can be defined by a suitable genetic representation can be implemented and evaluated, but in hardware, real-world limitations like manufacturing/assembly constraints and electrical power delivery mean that many of these robots cannot be built, or will malfunction in operation. This presents the novel challenge of how to constrain an evolutionary process within the space of evolvable phenotypes to only those regions that are practically feasible: the viable phenotype space. Methods of phenotype filtering and repair were introduced to address this, and found to degrade the diversity of the robot population and impede traversal of the exploration space. Furthermore, the degrees of freedom permitted by the hardware constraints were found to be poorly matched to the types of morphological variation that would be the most useful in the target environment. Consequently, the ability of the evolutionary process to generate robots with effective adaptations was greatly reduced. The conclusions from this are twofold. 1) Designing a hardware platform for evolving robots requires different thinking, in which all design decisions should be made with reference to their impact on the viable phenotype space. 2) It is insufficient to just evolve robots in simulation without detailed consideration of how they will be implemented in hardware, because the hardware constraints have a profound impact on the evolutionary space.

The Effects of Learning in Morphologically Evolving Robot Systems.

Simultaneously evolving morphologies (bodies) and controllers (brains) of robots can cause a mismatch between the inherited body and brain in the offspring. To mitigate this problem, the addition of an infant learning period has been proposed relatively long ago by the so-called Triangle of Life approach. However, an empirical assessment is still lacking to-date. In this paper, we investigate the effects of such a learning mechanism from different perspectives. Using extensive simulations we show that learning can greatly increase task performance and reduce the number of generations required to reach a certain fitness level compared to the purely evolutionary approach. Furthermore, we demonstrate that the evolved morphologies will be also different, even though learning only directly affects the controllers. This provides a quantitative demonstration that changes in the brain can induce changes in the body. Finally, we examine the learning delta defined as the performance difference between the inherited and the learned brain, and find that it is growing throughout the evolutionary process. This shows that evolution produces robots with an increasing plasticity, that is, consecutive generations become better learners and, consequently, they perform better at the given task. Moreover, our results demonstrate that the Triangle of Life is not only a concept of theoretical interest, but a system methodology with practical benefits.

Robot Evolution: Ethical Concerns.

Rapid developments in evolutionary computation, robotics, 3D-printing, and material science are enabling advanced systems of robots that can autonomously reproduce and evolve. The emerging technology of robot evolution challenges existing AI ethics because the inherent adaptivity, stochasticity, and complexity of evolutionary systems severely weaken human control and induce new types of hazards. In this paper we address the question how robot evolution can be responsibly controlled to avoid safety risks. We discuss risks related to robot multiplication, maladaptation, and domination and suggest solutions for meaningful human control. Such concerns may seem far-fetched now, however, we posit that awareness must be created before the technology becomes mature.

Optimized flocking of autonomous drones in confined environments.

We address a fundamental issue of collective motion of aerial robots: how to ensure that large flocks of autonomous drones seamlessly navigate in confined spaces. The numerous existing flocking models are rarely tested on actual hardware because they typically neglect some crucial aspects of multirobot systems. Constrained motion and communication capabilities, delays, perturbations, or the presence of barriers should be modeled and treated explicitly because they have large effects on collective behavior during the cooperation of real agents. Handling these issues properly results in additional model complexity and a natural increase in the number of tunable parameters, which calls for appropriate optimization methods to be coupled tightly to model development. In this paper, we propose such a flocking model for real drones incorporating an evolutionary optimization framework with carefully chosen order parameters and fitness functions. We numerically demonstrated that the induced swarm behavior remained stable under realistic conditions for large flock sizes and notably for large velocities. We showed that coherent and realistic collective motion patterns persisted even around perturbing obstacles. Furthermore, we validated our model on real hardware, carrying out field experiments with a self-organized swarm of 30 drones. This is the largest of such aerial outdoor systems without central control reported to date exhibiting flocking with collective collision and object avoidance. The results confirmed the adequacy of our approach. Successfully controlling dozens of quadcopters will enable substantially more efficient task management in various contexts involving drones.

From evolutionary computation to the evolution of things.

Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems.

The comparison of thrombocytosis and platelet-lymphocyte ratio as potential prognostic markers in colorectal cancer.

The aim of the present study was to analyse the preoperative platelet count and the platelet-lymphocyte ratio (PLR) in patients with colorectal cancer (CRC) of different stages and with hepatic metastasis of CRC (mCRC) and to compare these factors as potential prognostic markers. Clinicopathological data of 10 years were collected retrospectively from 336 patients with CRC and 118 patients with mCRC. Both in the CRC and the mCRC group overall survival (OS) was significantly worse in patients who had elevated platelet count (hazard ratio [HR] = 2.2, p < 0.001 and HR = 2.9, p = 0.018, respectively). Multivariate analysis indicated that elevated platelet count was an independent prognostic factor of CRC (HR = 1.7, p = 0.035) and mCRC (HR = 3.1, p = 0.017). Disease-free survival (DFS) was significantly worse in patients with elevated platelet count in the CRC group (HR = 2.0, p = 0.011). In the multivariate analysis the PLR was not a prognostic factor in either of the two cohorts (HR = 0.92, p < 0.001 and HR = 0.89, p = 0.789, respectively). The platelet count is a valuable prognostic marker for the survival in patients both with CRC and mCRC while the PLR is not prognostic in either group.

Tubal and ovarian pathways to pelvic epithelial cancer: a pathological perspective.

Prolongation of ovarian epithelial cancer survival depends on early detection or improved responses to chemotherapy. Gains in either have been modest at best. Understanding the diverse pathogenesis of this disease is critical to early intervention or prevention. This review addresses six important variables, including (i) cell of origin, (ii) site of origin, (iii) initial genotoxic events, (iv) risks imposed by hereditary and other promoting conditions, (v) subsequent factors that promote different patterns of metastatic spread, and (vi) prospects for intervention. This review proposes two distinct pathways to pelvic epithelial cancer. The first initiates in ovarian surface epithelium (OSE), Mullerian inclusions or endometriosis in the ovary. The second arises from the endosalpinx and encompasses a subset of serous carcinomas. The serous carcinogenic sequence in the distal fallopian tube is described and contrasted with lower grade serous tumors based on tumour location, earliest genetic change and ability (or lack of) to undergo terminal (ciliated) differentiation. Ultimately, a clear understanding of tumour origin and the mechanism(s) leading to the earliest phases of the serous and endometrioid carcinogenic sequences may hold the greatest promise for designing prevention strategies and/or developing new therapies.

Vitamin D analogs as anti-carcinogenic agents.

Deltanoids are the class of compounds comprising all natural and synthetic vitamin D molecules. The anti-proliferative, pro-differentiation, and pro-apoptotic properties of deltanoids have garnered interest in the fields of cancer chemoprevention and chemotherapy. The naturally occurring, biologically active form of vitamin D, 1,25(OH)2D3, causes hypercalcemia at pharmacologically relevant doses which forms a major obstacle in the clinical development of this compound. Design of new deltanoids has shown promise in separating the beneficial effects from the toxic effects. The Vitamin D receptor (VDR) is a major target for deltanoid design, and the structural features of deltanoid binding have been described. Effective compounds must also exhibit beneficial pharmacokinetic properties in vivo, and the plasma vitamin D binding protein (DBP) is likely to play an important role in the success of deltanoids in the clinic. Further, dual strategies of avoiding vitamin D toxicity through altering the dosing schedule and using less toxic deltanoids are in development. The three main categories of structural modification to the vitamin D backbone include the C,D-ring, the A-ring, and the C,D-ring side chain, and the ways each area has impacted efficacy and toxicity have been described through structure-activity relationships (SARs). Lastly, there is evidence that deltanoids can enhance the activity of other chemopreventive agents. The use of a cocktail approach will be discussed as a potential avenue for deltanoids in chemoprevention and chemotherapy.