Adaptive Decision Research Articles

Advanced Computational Intelligence Techniques for Real-Time Decision-Making in Autonomous Systems

This research explores advanced computational intelligence techniques aimed at enhancing real-time decision-making in autonomous systems. The increasing reliance on autonomous technologies across sectors such as transportation, healthcare, and industrial automation demands robust, adaptive, and reliable decision-making frameworks. This study introduces a novel hybrid model that integrates Reinforcement Learning (RL), Deep Neural Networks (DNN), and Fuzzy Logic to enable autonomous systems to make accurate and timely decisions in complex, dynamic environments. The proposed framework leverages RL for adaptive decision-making, DNNs for pattern recognition and prediction, and Fuzzy Logic for handling uncertainty in system states. Experimental evaluations were conducted using high-fidelity simulations across three scenarios: autonomous vehicle navigation, real-time patient monitoring in healthcare, and robotic process automation. Results indicate a 25% improvement in decision accuracy, a 30% reduction in response time, and enhanced robustness against environmental variability compared to conventional decision-making methods. The findings underscore the effectiveness of computational intelligence in supporting critical decisions in real-time, marking a significant step toward more capable and reliable autonomous systems.

Adaptive Prediction Structure for Learned Video Compression

Learned video compression has developed rapidly and shown competitive rate-distortion performance compared with the latest traditional video coding standard H.266 (VVC). However, existing works were restricted to fixed prediction direction and GoP size. The inflexibility on prediction structure hinders learned video compression towards optimal compression efficiency in diverse motion scenarios. In this paper, we propose to advance learned video compression with adaptive prediction structure decision. Specifically, we propose a unified compression framework that supports both forward prediction and bi-directional prediction. The framework can flexibly switch to different prediction direction to achieve better prediction performance. Meanwhile, we propose a low-complexity prediction structure decision algorithm, where prediction direction and GoP size are adaptively determined based on motion complexity to achieve optimal compression efficiency. Experimental results demonstrate that the proposed unified framework with adaptive decision algorithm improves compression efficiency of pure forward prediction-based or bi-directional prediction-based framework with neglectable ( \(0.9\%\) ) encoding time increment. Meanwhile, it achieves comparable compression performance with VVC and recent learned video coding methods.

An adaptive decision support system for outpatient appointment scheduling with heterogeneous service times

An adaptive decision support system for outpatient appointment scheduling with heterogeneous service times

Decision-making about climate change adaptation

Decision-making about and for climate change adaptation has fascinated the field of adaptation science for decades. We now have a solid set of decision-support tools, frameworks and approaches available to support decision-makers. Yet, in this piece, I argue that the black box of decision-making requires unravelling for us to better understand what heuristics (rule of thumb) are used about climate adaptation and how these impact the very decisions made. I also point out areas in need of more research including a new focus on decision quality, artificial intelligence and data, and the role of imagination in driving climate adaptation decision-making.

Adaptive fuzzy neighborhood decision tree

Decision tree algorithms have gained widespread acceptance in machine learning, with the central challenge lying in devising an optimal splitting strategy for node sample subspaces. In the context of continuous data, conventional approaches typically involve fuzzifying data or adopting a dichotomous scheme akin to the CART tree. Nevertheless, fuzzifying continuous features often entails information loss, whereas the dichotomous approach can generate an excessive number of classification rules, potentially leading to overfitting. To address these limitations, this study introduces an adaptive growth decision tree framework, termed the fuzzy neighborhood decision tree (FNDT). Initially, we establish a fuzzy neighborhood decision model by leveraging the concept of fuzzy inclusion degree. Furthermore, we delve into the topological structure of misclassified samples under the proposed decision model, providing a theoretical foundation for the construction of FNDT. Subsequently, we utilize conditional information entropy to sift through original features, prioritizing those that offer the maximum information gain for decision tree nodes. By leveraging the conditional decision partitions derived from the fuzzy neighborhood decision model, we achieve an adaptive splitting method for optimal features, culminating in an adaptive growth decision tree algorithm that relies solely on the inherent structure of real-valued data. Experimental evaluations reveal that, compared with advanced decision tree algorithms, FNDT exhibits a simple tree structure, stronger generalization capabilities, and superior performance in classifying continuous data.

Pulse position encoded RZ-OOK transmission for free-space optical communications

This paper proposes a pulse position encoded return-to-zero on-off keying (PP-RZ-OOK) transmission for free space optical (FSO) communications. At the transmitter end, PP-RZ-OOK signal is modulated by encoding the pulse position of RZ-OOK in the symbol duration. At the receiver end, PP-RZ-OOK signal is demodulated by symbol-by-symbol signal estimation. First, the existence of pulse is distinguished by adaptive threshold decision using the channel state information (CSI). Then, with respect to the symbol with the pulse, the knowledge of pulse position is estimated by slot-by-slot pulse power comparison. Finally, PP-RZ-OOK signal is effectively demodulated by both pulse existence and pulse position estimation. Therefore, the spectral efficiency of RZ-OOK is increased by the proposed pulse position encoding technique. The performance of the proposed PP-RZ-OOK modulation was verified through simulations under different turbulence channels and pulse durations, and it was compared with non-return-to-zero OOK (NRZ-OOK) and RZ-OOK modulations. The simulation results demonstrated that the capability of scintillation effect compensation was significantly enhanced compared with NRZ-OOK due to the pulse power increasing. Besides, the spectral efficiency was improved compared with RZ-OOK due to the additional pulse position encoding.

Perspectives on the quality of climate information for adaptation decision support

We summarise the contributions to the Topical Collection on quality of climate information for adaptation decision support. Based on these contributions, we draw some further lessons for the development of high-quality climate information and services, bridging between a “credibility-first” paradigm (exemplified by top-down information provision from systematic downscaling or impact projections) and a “salience-first” paradigm (exemplified by user-led tailored information products or storylines) by looking to identify their respective strengths and use cases. We emphasise that a more nuanced collective understanding of the dimensions of information quality in climate information and services would be beneficial to users and providers and ultimately support more confident and effective climate adaptation decisions and policy-making.

Bayesian additive regression trees for predicting childhood asthma in the CHILD cohort study

BackgroundAsthma is a heterogeneous disease that affects millions of children and adults. There is a lack of objective gold standard diagnosis that spans the ages; instead, diagnoses are made by clinician assessment based on a cluster of signs, symptoms and objective tests dependent on age. Yet, there is a clear morbidity associated with chronic asthma symptoms. Machine learning has become a popular tool to improve asthma diagnosis and classification. There is a paucity of literature on the use of Bayesian machine learning algorithms to predict asthma diagnosis in children. This paper develops a prediction model using the Bayesian additive regression trees (BART) and compares its performance to various machine learning algorithms in predicting the diagnosis of childhood asthma.MethodsClinically relevant variables collected at or before 3 years of age from 2794 participants in the CHILD Cohort Study were used to predict physician-diagnosed asthma at age 5. BART and six other commonly used machine learning algorithms, namely adaptive boosting, logistic regression, decision tree, neural network, random forest, and support vector machine were trained. Measures of performance including sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve were calculated. The confidence intervals were calculated using Bootstrapping samples. Important predictors and interaction effects associated with asthma were also identified using BART.ResultsBART, logistic regression and random forest showed the highest area under the ROC curve compared to other machine learning algorithms. Based on BART, recurrent wheeze, respiratory infection and food sensitization at 3 years of age were the most important predictors. The three most important interaction effects were found to be interaction terms of respiratory infection at 3 years and recurrent wheezing at 3 years, maternal asthma and paternal asthma, and maternal wheezing and inhalant sensitization of child at 3 years.ConclusionsBART demonstrated promising prediction performance when compared to other machine learning algorithms. Future research could validate the BART in an external cohort to evaluate its reliability and generalizability.

Computation noise promotes zero-shot adaptation to uncertainty during decision-making in artificial neural networks.

Random noise in information processing systems is widely seen as detrimental to function. But despite the large trial-to-trial variability of neural activity, humans show a remarkable adaptability to conditions with uncertainty during goal-directed behavior. The origin of this cognitive ability, constitutive of general intelligence, remains elusive. Here, we show that moderate levels of computation noise in artificial neural networks promote zero-shot generalization for decision-making under uncertainty. Unlike networks featuring noise-free computations, but like human participants tested on similar decision problems (ranging from probabilistic reasoning to reversal learning), noisy networks exhibit behavioral hallmarks of optimal inference in uncertain conditions entirely unseen during training. Computation noise enables this cognitive ability jointly through "structural" regularization of network weights during training and "functional" regularization by shaping the stochastic dynamics of network activity after training. Together, these findings indicate that human cognition may ride on neural variability to support adaptive decisions under uncertainty without extensive experience or engineered sophistication.

Evaluating the Effect of Adaptive Reuse in the Energy Performance of Historic Buildings: A Case Study from Türkiye

The building sector accounts for 30% to 40% of total energy consumption, and historic buildings play an important role in this proportion. Historical buildings that do not meet the required comfort conditions for the residents are adaptively reused, with various revisions. Recognizing the energy design of a historical building in its original condition and comparing the current situation can help create future solutions. This study examines the changes that a historic house in a hot climate zone in Türkiye experiences, from its original state up until the current situation. Energy analyses of the pre- and post-restoration situation are carried out, and the effect of adaptive reuse decisions on the energy performance of the building is investigated. A dynamic thermal simulation created with DesignBuilder was used to identify the energy use, carbon emissions, and thermal comfort. TM59 adaptive thermal comfort was used for the pre-restoration and the Fanger model for the post-restoration phase. This building, which was repurposed from a three-block residence, consists of a four-block hotel. Although the preservation of its original value is at the forefront, various structural changes were observed. The analysis demonstrates a higher occurrence of discomfort hours during summer compared to winter, consistent across both phases. Furthermore, energy consumption increased significantly, predominantly for heating, representing a doubling of energy use during the post-restoration phase. This is attributed to the building’s conversion into a hotel and the use of mechanical systems. Future research is required to develop strategies to reduce the energy consumption, carbon emissions, and discomfort hours while maintaining the value of the historic building and its materials.

Simulation-based adaptive optimization for passenger flow control measures at metro stations

Effective passenger flow control measures are essential for the safe operation of metro stations. Existing in-station control measures include adjusting the operation mode of escalators and setting up temporary fences. However, in practice, metro operators often adopt fixed operation modes during fixed periods, indicating that the current passenger flow control measures at metro stations are overly rigidified. Therefore, developing an adaptive control strategy to constantly balance the wildly fluctuating passenger flow and optimize the operation performance is a key issue in current research. In this study, transportation efficiency and congestion risk are selected as evaluation objectives for passenger transportation risk, and passenger flow feature, station structure, and passenger flow control measures are considered key influential factors. Subsequently, an adaptive optimization method integrating simulation and data interpolation is proposed. The software Legion is used to conduct 150 orthogonal simulations, and prediction models for passenger transportation risk are obtained by performing data interpolation on the simulation results. Finally, taking a certain metro station as a case study, the optimal passenger flow control strategy under any passenger flow composition is obtained by scenario acquisition, risk identification, and adaptive decision-making. The results show that setting up temporary fences can reduce the passenger density near the fare gates, while adjusting the running direction of escalators can reduce overcrowding on the platform. Under varying passenger flow composition, the optimal strategy for the current scenario can be obtained, controlling passenger transportation risk within an acceptable range and providing assistance for metro operators in decision-making.

Data visualisation for decision making under deep uncertainty: current challenges and opportunities

Abstract This perspective article explores the role of data visualisation in decision-making under deep uncertainty (DMDU), a growing discipline tackling complex socio-environmental challenges, such as climate impacts and adaptation, natural resource management, and preparedness for extreme events. We discuss the role of visualisation for both analysis (or exploratory) purposes, as well as communication (or explanatory) purposes, including to stakeholders and the public. We identify a lack of comprehensive guidelines on how visualisations are currently used and their potential in enhancing DMDU processes. Drawing on literature and insights from a recent workshop, we identify key challenges DMDU analysts face when visualising data: managing complexity and dimensionality, effectively communicating uncertainty, and ensuring user engagement and interpretability. We propose a research agenda to address these challenges, by taxonomising and evaluating the effectiveness of different visual forms in decision-making contexts, and fostering interdisciplinary collaboration. We argue that, through these efforts, we can improve the communication and usability of DMDU analyses, ultimately aiding in more informed and adaptive decision-making in the face of deep uncertainty.

Application of intelligent self-organizing algorithms in UAV cooperative inspection of power distribution networks

In the rapidly evolving technological landscape, the advent of collaborative Unmanned Aerial Vehicle (UAV) inspections represents a revolutionary leap forward in the monitoring and maintenance of power distribution networks. This innovative approach harnesses the synergy of UAVs working together, marking a significant milestone in enhancing the reliability and efficiency of infrastructure management. Despite its promise, current research in this domain frequently grapples with challenges related to efficient coordination, data processing, and adaptive decision-making under complex and dynamic conditions. Intelligent self-organizing algorithms emerge as pivotal in addressing these gaps, offering sophisticated methods to enhance the autonomy, efficiency, and reliability of UAV collaborative inspections. In response to these challenges, we propose the MARL-SOM-GNNs network model, an innovative integration of Multi-Agent Reinforcement Learning, Self-Organizing Maps, and Graph Neural Networks, designed to optimize UAV cooperative behavior, data interpretation, and network analysis. Experimental results demonstrate that our model significantly outperforms existing approaches in terms of inspection accuracy, operational efficiency, and adaptability to environmental changes. The significance of our research lies in its potential to revolutionize the way power distribution networks are inspected and maintained, paving the way for more resilient and intelligent infrastructure systems. By leveraging the capabilities of MARL for dynamic decision-making, SOM for efficient data clustering, and GNNs for intricate network topology understanding, our model not only addresses current shortcomings in UAV collaborative inspection strategies but also sets a new benchmark for future developments in autonomous infrastructure monitoring, highlighting the crucial role of intelligent algorithms in advancing UAV technologies.

Impacts of adaptation to climate change on farmers’ food security and level of sesame production in Western Ethiopia

The existence of multiple stresses and poor adaptive capacity make Africa most susceptible to climate change. In Ethiopia the potential adverse effects of climate change on the agricultural sector, the main stay of the country’s economy, are major concerns. To ensure food security, reducing the vulnerability of agricultural systems through different feasible adaptation strategies is one of the policy options in response to climate change impact This study is a first of its kind in examining the relative effectiveness of various adaptation strategies in ensuring farmers’ food security and enhancing level of sesame production in rural area of Western Ethiopia. In addition to data obtained from meteorological stations, cross-sectional data were collected interviewing 400 farm households. Descriptive statistics, two-stage least square (2SLS) and double-hurdle (D-H) models were used to analyze the data. The results of the study indicate that households are adapting using various strategies to the looming climate change in the area. The study also indicated that, though sesame production was negatively impacted by the climate hazards, smallholders have continued its production at minimum level due mainly to its high value crop character. 2SLS estimation results revealed that rainfall and temperature variability have negative impact on household’s food security. Moreover, the result indicates effectiveness of climate adaptation strategies namely agronomic practices, irrigation and soil and water conservation in reducing climatic risks and ensuring household food security. The result also implicitly indicated that farmers continued to adapt sesame production under risk climate and it is contributing to farmers’ food security. Further, the result revealed that climate change adaptation strategies have positively impacted the level of sesame production. Consequently, policy that augments households’ climate awareness and promotes adaptation decision and strategies could help reducing risks pertaining to climate and thereby improves farmers’ food security status and production of high value export potential crop—sesame.

Computational processes of simultaneous learning of stochasticity and volatility in humans

Making adaptive decisions requires predicting outcomes, and this in turn requires adapting to uncertain environments. This study explores computational challenges in distinguishing two types of noise influencing predictions: volatility and stochasticity. Volatility refers to diffusion noise in latent causes, requiring a higher learning rate, while stochasticity introduces moment-to-moment observation noise and reduces learning rate. Dissociating these effects is challenging as both increase the variance of observations. Previous research examined these factors mostly separately, but it remains unclear whether and how humans dissociate them when they are played off against one another. In two large-scale experiments, through a behavioral prediction task and computational modeling, we report evidence of humans dissociating volatility and stochasticity solely based on their observations. We observed contrasting effects of volatility and stochasticity on learning rates, consistent with statistical principles. These results are consistent with a computational model that estimates volatility and stochasticity by balancing their dueling effects.

Chronic Ethanol Exposure Produces Sex-Dependent Impairments in Value Computations in the Striatum.

Value-based decision-making relies on the striatum, where neural plasticity can be altered by chronic ethanol (EtOH) exposure, but the effects of such plasticity on striatal neural dynamics during decision-making remain unclear. This study investigated the long-term impacts of EtOH on reward-driven decision-making and striatal neurocomputations in male and female rats using a dynamic probabilistic reversal learning task. Following a prolonged withdrawal period, EtOH-exposed male rats exhibited deficits in adaptability and exploratory behavior, with a preference for value updating based on rewards rather than omissions. These behavioral changes were linked to altered neural encoding in the dorsomedial striatum (DMS), where EtOH increased outcome-related signals and decreased choice-related signals. In contrast, female rats showed minimal behavioral changes with distinct EtOH-evoked alterations of neural signals, revealing significant sex differences in the impact of chronic EtOH. Our findings underscore the profound impact of chronic EtOH exposure on adaptive decision-making, revealing enduring changes in neurocomputational processes in the striatum underlying cognitive deficits that differ by sex.

Adaptive and transparent decision-making in autonomous robots through graph-structured world models

ABSTRACT The growing ubiquity of autonomous robots across different fields necessitates that agents adapt to diverse tasks and ensure transparency and intelligibility in their decision-making processes. This study presents a novel framework that combines a graph-structured world model with large language models (LLMs) to address these requirements. First, a latent space is created to capture the reachability between distinct states. Next, a graph is constructed within this latent space by clustering an offline dataset that effectively captures the complex dynamics of the environment. Subsequently, LLMs are employed to redefine the reward function and relabel the dataset, thereby establishing a well-defined Markov decision process based on the previously learned graph. This relabeling process ensures that the agent's decision space aligns with user intentions. Consequently, a predictive world model is obtained, offering insights into potential future states and facilitating graph-based planning. Moreover, by inputting the planned path of the agent in the graph-structured world model into LLMs, natural language explanations can be generated to provide transparency in the decision-making process. Experiments on the D4RL benchmark validated the effectiveness of our approach in long-horizon planning, its adaptability to different user tasks, and its inherent explainability.

Robust Truncated Statistics Constant False Alarm Rate Detection of UAVs Based on Neural Networks

With the rapid popularity of unmanned aerial vehicles (UAVs), airspace safety is facing tougher challenges, especially for the identification of non-cooperative target UAVs. As a vital approach for non-cooperative target identification, radar signal processing has attracted continuous and extensive attention and research. The constant false alarm rate (CFAR) detector is widely used in most current radar systems. However, the detection performance will sharply deteriorate in complex and dynamical environments. In this paper, a novel truncated statistics- and neural network-based CFAR (TSNN-CFAR) algorithm is developed. Specifically, we adopt a right truncated Rayleigh distribution model combined with the characteristics of pattern recognition using a neural network. In the simulation environments of four different backgrounds, the proposed algorithm does not need guard cells and outperforms the traditional mean level (ML) and ordered statistics (OS) CFAR algorithms. Especially in high-density target and clutter edge environments, since utilizing 19 statistics obtained from the numerical calculation of two reference windows as the input characteristics, the TSNN-CFAR algorithm has the best adaptive decision ability, accurate background clutter modeling, stable false alarm regulation property and superior detection performance.

Enhancing UAV Swarm Tactics with Edge AI: Adaptive Decision Making in Changing Environments

This paper presents a drone system that uses an improved network topology and MultiAgent Reinforcement Learning (MARL) to enhance mission performance in Unmanned Aerial Vehicle (UAV) swarms across various scenarios. We propose a UAV swarm system that allows drones to efficiently perform tasks with limited information sharing and optimal action selection through our Efficient Self UAV Swarm Network (ESUSN) and reinforcement learning (RL). The system reduces communication delay by 53% and energy consumption by 63% compared with traditional MESH networks with five drones and achieves a 64% shorter delay and 78% lower energy consumption with ten drones. Compared with nonreinforcement learning-based systems, mission performance and collision prevention improved significantly, with the proposed system achieving zero collisions in scenarios involving up to ten drones. These results demonstrate that training drone swarms through MARL and optimized information sharing significantly increases mission efficiency and reliability, allowing for the simultaneous operation of multiple drones.

Adaptive transition decisions and identity exploration among transgender and nonbinary persons exposed to gender identity conversion efforts

Introduction Transgender and nonbinary (TNB) persons’ healthcare experiences and related transition decisions have received increasing attention in recent years. Growing literature indicates gender non-affirming practices, such as gender identity conversion efforts (GICE), are harmful for the wellbeing of TNB persons. Yet, how exposure to GICE is linked to transition related decisions among TNB persons remains unexplored. This study examines links between GICE and TNB persons’ transition decisions and identity exploration, using a conceptual framework that distinguishes adaptive transition decisions (e.g. transition interruptions due to interpersonal coercion) from identity-related transition decisions. Methods This study is a secondary data analysis of the 2015 U.S. Transgender Survey (N = 27,630). Multinomial logistic regression was conducted to examine the relationship between GICE and transition decisions among TNB persons while controlling for demographic covariates. Results Overall, 13.5% of TNB participants experienced GICE. Participants interrupted their transition due to interpersonal coercion (4.9%), structural factors (2.0%), and identity-related factors (0.4%). Participants with GICE exposure were more likely to report interrupting their transition due to interpersonal coercion than not interrupting their transition. However, GICE-exposed participants did not have a higher chance of identity-related transition interruptions than no interruptions. Conclusions These findings point to a potentially harmful role GICE may play in the transition decisions of TNB persons. Our analysis adds to evidence indicating the need for banning GICE and calls for a more nuanced understanding, recognition, and respect for TNB persons’ non-linear transitioning trajectories. Otherwise, we risk vilifying gender-affirming practices and pathologizing adaptive transitioning decisions of TNB persons.