Navigation of male moths toward females during the mating search offers a unique perspective on the exploration-exploitation (EE) model in decision-making. This study uses the EE model to explain male moth pheromone-driven flight paths. We leverage wind tunnel measurements and 3D tracking using infrared cameras to gain insights into male moth behavior. During the experiments in the wind tunnel, we add disturbance to the airflow and analyze the effect of increased fluctuations on moth flights in the context of the proposed EE model. We separate the exploration and exploitation phases by applying a genetic algorithm to the dataset of moth 3D trajectories. First, we demonstrate that the exploration-to-exploitation rate (EER) increases with distance from the source of the female pheromone, which can be explained in the context of the EE model. Furthermore, our findings reveal a compelling relationship between EER and increased flow fluctuations near the pheromone source. Using the open-source pheromone plume simulation and our moth-inspired navigation model, we explain why male moths exhibit an enhanced EER as turbulence levels increase, emphasizing the agent's adaptation to dynamically changing environments. This research extends our understanding of optimal navigation strategies based on general biological EE models and supports the development of advanced, theoretically supported bio-inspired navigation algorithms. We provide important insights into the potential of bio-inspired navigation models for addressing complex decision-making challenges.

Can we build an artificial system that would be able to generate endless surprises if ran "forever" in Minecraft? While there is not a single path toward solving that grand challenge, this article presents what we believe to be some working ingredients for the endless generation of novel increasingly complex artifacts in Minecraft. Our framework for an open-ended system includes two components: a complex system used to recursively grow and complexify artifacts over time, and a discovery algorithm that leverages the concept of meta-diversity search. Since complex systems have shown to enable the emergence of considerable complexity from set of simple rules, we believe them to be great candidates to generate all sort of artifacts in Minecraft. Yet, the space of possible artifacts that can be generated by these systems is often unknown, challenging to characterize and explore. Therefore automating the long-term discovery of novel and increasingly complex artifacts in these systems is an exciting research field. To approach these challenges, we formulate the problem of meta-diversity search where an artificial "discovery assistant" incrementally learns a diverse set of representations to characterize behaviors and searches to discover diverse patterns within each of them. A successful discovery assistant should continuously seek for novel sources of diversities while being able to quickly specialize the search toward a new unknown type of diversity. To implement those ideas in the Minecraft environment, we simulate an artificial "chemistry" system based on Lenia continuous cellular automaton for generating artifacts, as well as an artificial "discovery assistant" (called Holmes) for the artifact-discovery process. Holmes incrementally learns a hierarchy of modular representations to characterize divergent sources of diversity and uses a goal-based intrinsically-motivated exploration as the diversity search strategy.

An efficient method for next-best-view (NBV) estimation with high accuracy in the case of the limited camera field of view (FOV) was proposed. Abstract: Fusion of three-dimensional (3D) and multispectral (MS) imaging data has a great potential for high-throughput plant phenotyping of structural and biochemical as well as physiological traits simultaneously, which is important for decision support in agriculture and for crop breeders in selecting the best genotypes. However, lacking of 3D data integrity of various plant canopy structures and low-quality of MS images caused by the complex illumination effects make a great challenge, especially at the proximal imaging scale. Therefore, this study proposed a novel approach for adaptive data acquisition and radiometric calibration to generate high-quality 3D multispectral point clouds (3DMPCs) of plants. An efficient next-best-view (NBV) planning method based on an unmanned ground vehicle (UGV) plant phenotyping system with a multisensor-equipped robotic arm was proposed to achieve adaptive data acquisition. The neural reference field (NeREF) was employed to predict the digital number (DN) values of the hemispherical reference for radiometric calibration. For NBV planning, the average total time for single plant at a joint speed of 1.55 rad/s was about 62.8 s, with an average reduction of 18.0% compared to the unplanned. The integrity of the wholeplant data was improved by an average of 23.6% compared to the fixed viewpoints alone. Compared with the ASD measurements, the average root mean square error (RMSE) of the reflectance spectra obtained from 3DMPCs at different regions of interest was 0.08 with an average decrease of 58.93% compared to the results obtained from the single-frame of MS images without 3D radiometric calibration. The 3Dcalibrated plant 3DMPCs improved the predictive accuracy of partial least squares regression (PLSR) for chlorophyll content, with an average increase of 0.07 in the coefficient of determination (R Our approach introduced a fresh perspective on generating high-quality 3DMPCs of plants under the natural light condition, enabling more precise analysis of plant morphological and physiological parameters. Keywords: adaptive data acquisition; 3DMPC; NBV planning; radiometric calibration; NeREF; chlorophyll content 1. Introduction High-throughput plant phenotyping provides an unprecedented way to systematically evaluate plant development and functionality with the precise quantification of morphological, physiological, biochemical, and performance traits over the whole growth period. It can help on decision support in agriculture, for ecological diversity studies, and for crop breeding in the selection of superior genotypes to improve crop performance, and thus revolutionize the agriculture and breeding strategies to meet the future need of agricultural sustainable development (Freschet et al. 2018; Hu and Schmidhalter 2023).

Testing autonomous vehicles (AVs) under various environmental scenarios that lead the vehicles to unsafe situations is known to be challenging. Given the infinite possible environmental scenarios, it is essential to find critical scenarios efficiently. To this end, we propose a novel testing method, named EpiTESTER, by taking inspiration from epigenetics, which enables species to adapt to sudden environmental changes. In particular, EpiTESTER adopts gene silencing as its epigenetic mechanism, which regulates gene expression to prevent the expression of a certain gene, and the probability of gene expression is dynamically computed as the environment changes. Given different data modalities (e.g., images, lidar point clouds) in the context of AV, EpiTESTER benefits from a multi-model fusion transformer to extract high-level feature representations from environmental factors and then calculates probabilities based on these features with the attention mechanism. To assess the cost-effectiveness of EpiTESTER, we compare it with a classical genetic algorithm (GA) (i.e., without any epigenetic mechanism implemented) and EpiTESTER with equal probability for each gene. We evaluate EpiTESTER with four initial environments from CARLA, an open-source simulator for autonomous driving research, and an end-to-end AV controller, Interfuser. Our results show that EpiTESTER achieved a promising performance in identifying critical scenarios compared to the baselines, showing that applying epigenetic mechanisms is a good option for solving practical problems.

Many-Objective Feature Selection (MOFS) approaches use four or more objectives to determine the relevance of a subset of features in a supervised learning task. As a consequence, MOFS typically returns a large set of non-dominated solutions, which have to be assessed by the data scientist in order to proceed with the final choice. Given the multi-variate nature of the assessment, which may include criteria (e.g. fairness) not related to predictive accuracy, this step is often not straightforward and suffers from the lack of existing tools. For instance, it is common to make use of a tabular presentation of the solutions, which provide little information about the trade-offs and the relations between criteria over the set of solutions. This paper proposes an original methodology to support data scientists in the interpretation and comparison of the MOFS outcome by combining post-processing and visualisation of the set of solutions. The methodology supports the data scientist in the selection of an optimal feature subset by providing her with high-level information at three different levels: objectives, solutions, and individual features. The methodology is experimentally assessed on two feature selection tasks adopting a GA-based MOFS with six objectives (number of selected features, balanced accuracy, F1-Score, variance inflation factor, statistical parity, and equalised odds). The results show the added value of the methodology in the selection of the final subset of features.

Influence Maximization is the task of selecting optimal nodes maximising the influence spread in social networks. This study proposes a Discretized Quantum-based Salp Swarm Algorithm (DQSSA) for optimizing influence diffusion in social networks. By discretizing meta-heuristic algorithms and infusing them with quantum-inspired enhancements, we address issues like premature convergence and low efficacy. The proposed method, guided by quantum principles, offers a promising solution for Influence Maximisation. Experiments on four real-world datasets reveal DQSSA's superior performance as compared to established cutting-edge algorithms.

Counterfactual explanations (CFE) are methods that explain a machine learning model by giving an alternate class prediction of a data point with some minimal changes in its features. It helps the users to identify their data attributes that caused an undesirable prediction like a loan or credit card rejection. We describe an efficient and an actionable counterfactual (CF) generation method based on particle swarm optimization (PSO). We propose a simple objective function for the optimization of the instance-centric CF generation problem. The PSO brings in a lot of flexibility in terms of carrying out multi-objective optimization in large dimensions, capability for multiple CF generation, and setting box constraints or immutability of data attributes. An algorithm is proposed that incorporates these features and it enables greater control over the proximity and sparsity properties over the generated CFs. The proposed algorithm is evaluated with a set of action-ability metrics in real-world datasets, and the results were superior compared to that of the state-of-the-arts.

Multi-cellular robot design aims to create robots comprised of numerous cells that can be efficiently controlled to perform diverse tasks. Previous research has demonstrated the ability to generate robots for various tasks, but these approaches often optimize robots directly in the vast design space, resulting in robots with complicated morphologies that are hard to control. In response, this paper presents a novel coarse-to-fine method for designing multi-cellular robots. Initially, this strategy seeks optimal coarse-grained robots and progressively refines them. To mitigate the challenge of determining the precise refinement juncture during the coarse-to-fine transition, we introduce the Hyperbolic Embeddings for Robot Design (HERD) framework. HERD unifies robots of various granularity within a shared hyperbolic space and leverages a refined Cross-Entropy Method for optimization. This framework enables our method to autonomously identify areas of exploration in hyperbolic space and concentrate on regions demonstrating promise. Finally, the extensive empirical studies on various challenging tasks sourced from EvoGym show our approach's superior efficiency and generalization capability.

In this paper, we introduce a novel swarm application, swarm synergy, where robots in a swarm intend to form communities. Each robot is considered to make independent decisions without any communication capability (silent agent). The proposed algorithm is based on parameters local to individual robots. Engaging scenarios are studied where the silent robots form communities without the preset conditions on the number of communities, community size, goal location of each community, and specific members in the community. Our approach allows silent robots to achieve this self-organized swarm behavior using only sensory inputs from the environment. The algorithm facilitates the formation of multiple swarm communities at arbitrary locations with unspecified goal locations. We further infer the behavior of swarm synergy to ensure the anonymity/untraceability of both robots and communities. The robots intend to form a community by sensing the neighbors, creating synergy in a bounded environment. The time to achieve synergy depends on the environment boundary and the onboard sensor's field of view. Compared to the state-of-art with similar objectives, the proposed communication-free swarm synergy shows comparative time to synergize with untraceability features.

The problem of firebreak placement is crucial for fire prevention, and its effectiveness at landscape scale will depend on their ability to impede the progress of future wildfires. To provide an adequate response, it is therefore necessary to consider the stochastic nature of fires, which are highly unpredictable from ignition to extinction. Thus, the placement of firebreaks can be considered a stochastic optimization problem where: (1) the objective function is to minimize the expected cells burnt of the landscape; (2) the decision variables being the location of firebreaks; and (3) the random variable being the spatial propagation/behavior of fires. In this paper, we propose a solution approach for the problem from the perspective of simulation-based optimization (SbO), where the objective function is not available (a black-box function), but can be computed (and/or approximated) by wildfire simulations. For this purpose, Genetic Algorithm and GRASP are implemented. The final implementation yielded favorable results for the Genetic Algorithm, demonstrating strong performance in scenarios with medium to high operational capacity, as well as medium levels of stochasticity