Decision Procedure Research Articles

Intelligent Agents and Causal Inference: Enhancing Decision-Making through Causal Reasoning

This study examines the incorporation of causal inference methods into intelligent entities and examines the benefits of utilizing causal reasoning to improve decision-making procedures. This study entails conducting an experimental evaluation within a video game setting to evaluate the performance of three separate agent types: ExplorerBOT, GuardBOT, and CausalBOT. The ExplorerBOT utilizes a stochastic path selection technique for task completion, whereas the GuardBOT remains immobile yet exhibits exceptional proficiency in identifying and neutralizing other bots. On the other hand, the CausalBOT utilizes sophisticated causal inference methods to examine the underlying factors contributing to the failures noticed in the task completion of the ExplorerBOT. The aforementioned feature allows CausalBOT to make informed decisions by selecting paths that have a greater likelihood of achieving success. The main purpose of these experiments is to assess and compare the effectiveness of two distinct bots, namely ExplorerBOT and CausalBOT, in accomplishing their respective objectives. To facilitate comparison, two iterations of the ExplorerBOT are utilized. The initial iteration is predicated exclusively on stochastic path selection and necessitates a more profound understanding of the variables that impact the achievement of tasks. On the other hand, the second version integrates an algorithm for informed search. In contrast, CausalBOT employs causal inference techniques to discover the underlying causes of failures exhibited by ExplorerBOTs and collect pertinent data. Through the process of discerning the fundamental causal mechanisms, CausalBOT is able to make well-informed decisions by selecting pathways that maximize the probability of successfully completing a given job. The utilization of this approach greatly boosts the decision-making powers of CausalBOT, hence enabling it to effectively adapt and overcome problems in a more efficient manner when compared to alternative agents.

Deciding atomicity of subword-closed languages

We study languages closed under the non-contiguous (scattered) subword containment order. Any subword-closed language L can be uniquely described by its anti-dictionary, i.e. the set of minimal words that do not belong to L. For a language over a finite alphabet, the anti-dictionary is necessarily finite. A language L is said to be atomic if it cannot be presented as the union of two subword-closed languages different from L. In this work, we provide a decision procedure which, given a language over a finite alphabet defined by its anti-dictionary, decides whether it is atomic or not. We also develop an algorithmic procedure for decomposing a language, which is not atomic, into finitely many atomic sublanguages.

Forensic Dimensions of Chronic Stress (PTSD) and its Impact on Brain Activity and Mental Health: A Systematic Review

Introduction: The effects of chronic stress on the brain and mental health are significant and are frequently linked to post-traumatic stress disorder (PTSD). The impact of persistent stress, especially PTSD, on the brain and mental health is examined in this study from a forensic perspective. To ensure fair evaluations, assessments, and decision-making procedures, the legal system needs to comprehend these ramifications. Aim:This study aims to investigate the neurobiological modifications, cognitive deficits, and psychological effects brought on by chronic stress and their applicability in a forensic setting. The study emphasizes the necessity for thorough evaluations in judicial procedures and seeks to shed light on the impact of prolonged stress on brain function and mental health. Methodology: Using pertinent keywords including chronic stress, brain structure, brain function, depression, anxiety, and PTSD, a thorough search of scientific databases was carried out, including PubMed, PsycINFO, and Google Scholar. Relevance to the subject, the study's caliber, and the publication date were all considered while choosing papers. Key facts and insights were determined by a comprehensive study of the literature. Results: Chronic stress affects emotional regulation, memory, and the stress response by causing neurobiological changes in the prefrontal cortex, hippocampus, amygdala, and HPA axis. Prolonged stress is accompanied by cognitive impairments, especially in PTSD, which include executive dysfunction and attention problems. Anxiety, sadness, and increased susceptibility to substance misuse are examples of psychological effects. Conclusion There are important forensic ramifications to persistent stress, particularly PTSD, on brain function and mental health. While psychological issues make it difficult to participate in court proceedings effectively, neurobiological changes and cognitive deficits might damage someone's credibility as a witness or victim. Fair evaluations require a bridge between neuroscience, mental health, and the legal system. Future studies should pinpoint distinct neuronal processes and consider efficient cures to lessen the effects on forensic outcomes.

Selection of optimal intensity measures for seismic performance evaluation of underground utility tunnel and internal pipeline system

Seismic intensity measures (IMs), which are closely related to both earthquake hazards and structural responses, play a critical role in the performance-based earthquake engineering (PBEE) framework. This paper aims to investigate and define the most representative IMs for probabilistic seismic demand model (PSDM) for the longitudinal utility tunnel and internal pipeline system. Similar to the requirements by the Chinese Code for seismic design of urban rail transit structures, the utility tunnel in this study is assumed being buried in four typical engineering site classes for the analyses. Soil-tunnel-pipeline interactions are simulated using a double-beam system resting on a nonlinear foundation. Soil-tunnel interaction, tunnel-pipeline interaction and joint connections are modeled as nonlinear springs with different mechanical properties. A series of 17 pairs of outcrop ground-motion records and 27 commonly used IMs are selected in the analyses. Engineering demand parameter (EDP) is defined based on the maximum joint opening and the maximum strain of the utility tunnel and internal pipeline, respectively. The selected IMs for predicting the seismic responses of tunnel-pipeline systems are tested using different criteria, i.e., correction, efficiency, practicality, and proficiency. The final decision-making procedure employs a fuzzy multi-criteria decision method. The numerical results indicate that: (1) The velocity-related IMs are the most representative IMs based on the four criteria for structures buried in four sites of different conditions; (2) The optimal seismic IMs vary with different criteria, site classes and structure types. According to the fuzzy multi-criteria decision approach, optimal IMs for the tunnel-pipeline system are PGV, PGV, EPV and HI for site classes I – IV, respectively. The proposed optimal IMs can be utilized to construct seismic fragility curves of utility tunnel, internal pipeline and tunnel-pipeline systems in four different site classes.

Prepotential approach: a unified approach to exactly, quasi-exactly, and rationally extended solvable quantal systems

We give a brief overview of a simple and unified way, called the prepotential approach, to treat both exact and quasi-exact solvabilities of the one-dimensional Schrödinger equation. It is based on the prepotential together with Bethe ansatz equations. Unlike the the supersymmetric method for the exactly-solvable systems and the Lie-algebraic approach for the quasi-exactly solvable problems, this approach does not require any knowledge of the underlying symmetry of the system. It treats both quasi-exact and exact solvabilities on the same footing. In this approach the system is completely defined by the choice of two polynomials and a set of Bethe ansatz equations. The potential, the change of variables as well as the eigenfunctions and eigenvalues are determined in the same process. We illustrate the approach by several paradigmatic examples of Hermitian and non-Hermitian Hamiltonians with real energies. Hermitian systems with complex energies, called the quasinormal modes, are also presented. Extension of the approach to the newly discovered rationally extended models is briefly discussed.

Artificial intelligence driven cognitive optimization and predictive analysis using blockchain privacy-based machine learning model

According to the cognitive radio paradigm, spectrum sensing, decision-making, sharing, and mobility phases can be integrated to enable both authorised and unauthorised users to coexist in the radio-electric spectrum to the fullest extent possible. Using a blockchain privacy model and ML approaches in a B5G network, this study suggests a new approach to cognitive network optimisation and predictive analysis which stands for blockchain privacy-based transfer encoder neural networks. One further step is to optimise the network using binary swarm optimisation (BSMO). Bandwidth efficiency, throughput, forecast accuracy, and quality of service are the experimentally measured variables for a given number of channels and users. An innovative QoS-based optimisation phase and two separate decision-making procedures are proposed for usage in a proactive approach. One use of artificial neural networks (ANNs) is the prediction of future traffic loads for different radio access technologies (RATs), which employ different bands of the electromagnetic spectrum.

A logic-incorporated gene regulatory network deciphers principles in cell fate decisions.

Organisms utilize gene regulatory networks (GRN) to make fate decisions, but the regulatory mechanisms of transcription factors (TF) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision-making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top-down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.

Expedited re-design of multi-band passive microwave circuits using orthogonal scaling directions and gradient-based tuning

Geometry scaling of microwave circuits is an essential but challenging task. In particular, the employment of a given passive structure in a different application area often requires re-adjustment of the operating frequencies/bands while maintaining top performance. Achieving this necessitates the utilization of numerical optimization methods. Nonetheless, if the intended frequencies are distant from the ones at the starting point, local search procedures tend to fail, whereas global search algorithms are computationally expensive. As recently demonstrated, a combination of large-scale concurrent geometry parameter scaling with intermittent local tuning allows for dependable re-design of high-frequency circuits at low CPU costs. Unfortunately, the procedure is only applicable to single-band structures due to synchronized modifications of all operating bands under scaling. This article discusses a novel procedure that leverages a similar overall concept, but allows for independent control of all center frequencies. To achieve this goal, an automated decision-making procedure is developed in which a set of orthogonal scaling directions are determined based on their effect on individual circuit bands, and using auxiliary optimization sub-problems. The scaling range is then automatically computed by solving an appropriately-defined least-square design relocation problem. The methodology introduced in the work is illustrated using two planar passive devices. In both cases, wide-range operating frequency re-design has been demonstrated and favorably compared to conventional gradient-based tuning. Furthermore, the presented procedure has been shown to be computationally efficient. It is also easy to implement and integrate with a variety of gradient-based optimization procedures of a descent type.

A logic-incorporated gene regulatory network deciphers principles in cell fate decisions

Organisms utilize gene regulatory networks (GRN) to make fate decisions, but the regulatory mechanisms of transcription factors (TF) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision-making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top-down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.

All on board? The role of institutional design for public support for differentiated integration

Differentiated integration is often considered a solution to gridlock in the European Union. However, questions remain concerning its perceived legitimacy among the public. While research shows that most citizens are not, in principle, opposed to differentiated integration – although support varies across different differentiated integration models and different country contexts – we still know little about the role institutional design plays in citizens’ evaluations of differentiated integration. This article inspects how citizens evaluate different hypothetical differentiated integration arrangements, with varying decision-making procedures, using a conjoint experiment. We ask whether institutional arrangements can overcome citizens’ preference heterogeneity over differentiated integration, and thereby foster the legitimacy of a differentiated European Union. We find that while a majority of citizens care about the inclusiveness of differentiated integration arrangements, they also support limiting the number of veto points. Our analysis also reveals noteworthy differences across citizens with pro- and anti-European Union attitudes in the perceived fairness of differentiated integration arrangements.

Comparison of MOEAs in an Optimization-Decision Methodology for a Joint Order Batching and Picking System

This paper investigates the performance of a two-stage multi-criteria decision-making procedure for order scheduling problems. These problems are represented by a novel nonlinear mixed integer program. Hybridizations of three Multi-Objective Evolutionary Algorithms (MOEAs) based on dominance relations are studied and compared to solve small, medium, and large instances of the joint order batching and picking problem in storage systems with multiple blocks of two and three dimensions. The performance of these methods is compared using a set of well-known metrics and running an extensive battery of simulations based on a methodology widely used in the literature. The main contributions of this paper are (1) the hybridization of MOEAs to deal efficiently with the combination of orders in one or several picking tours, scheduling them for each picker, and (2) a multi-criteria approach to scheduling multiple picking teams for each wave of orders. Based on the experimental results obtained, it can be stated that, in environments with a large number of different items and orders with high variability in volume, the proposed approach can significantly reduce operating costs while allowing the decision-maker to anticipate the positioning of orders in the dispatch area.

МОДЕЛИРОВАНИЕ РИСКОВ ЭКОНОМИЧЕСКОЙ БЕЗОПАСНОСТИ ПРИ РЕАЛИЗАЦИИ ПРОЕКТОВ ГОСУДАРСТВЕННО-ЧАСТНОГО ПАРТНЕРСТВА

The modern economy is in a state of unstable, turbulent development. This is caused by a series of macroeconomic shocks (Covid-19 pandemic; anti-Russian sanctions) in recent years. As a result, there is a weakening of economic security at the regional and national levels. It is necessary to expand the range of tools to ensure this. One such tool is public-private partnership (PPP). Traditionally, partnerships are not considered in the context of ensuring economic security. They act as a tool for economic development and satisfaction of socially significant needs. The author's study proposes two approaches to considering PPP in the context of economic security. On the one hand, the implementation of PPP projects stabilizes the economy, i.e. contributes to ensuring economic security. On the other hand, when implementing PPP projects, all stakeholders have to face economic security risks. The author has proposed a 3D model that allows for the assessment and monitoring of such risks. Clear and understandable visualization of risks in the model leads to simplification of decision-making procedures for managing PPP economic security risks.

Influence of online digital communication in the Decision-making Process amongst Engineering Students

The research project reflects the impact of internet marketing on engineering learners' decision-making in the Ernakulam District. The goal of the study is to understand how educational organisations use the internet and to determine which online marketing tools and tactics work best for connecting with learners in engineering. The impact of online digital communication on engineering students' decision-making procedure when selecting educational programmes and institutions is also reflected in the study. Information from the sample of engineering students in the study region will be gathered using a survey-based research approach. The purpose of this research is to ascertain the manner in which the target group's decision-making procedure is impacted by digitalization. The study is probably going to help elevate the status of online digital communication in the education sector and offer guidance and knowledge to colleges and marketers that want to reach students in the engineering field.

Feature space separation by conformity loss driven training of CNN

Convolutional neural networks (CNNs) have enabled tremendous achievements in image classification, as the model can automatically extract image features and assign a proper classification. Nevertheless, the classification is lacking robustness to — for humans’ invisible perturbations on the input. To improve the robustness of the CNN model, it is necessary to understand the decision-making procedure of CNN models. By inspecting the learned feature space, we found that the classification regions are not always clearly separated by the CNN model. The overlap of classification regions increases the possibility to less perturbation induced input changes on classification results. Therefore, the clear separation of feature spaces of the CNN model should support decision robustness. In this paper, we propose to use a novel loss function called “conformity loss” to strengthen disjoint feature spaces during learning at different layers of the CNN, in order to improve the intra-class compactness and inter-class differences in trained representations. The same function was used as an evaluation metric to measure the feature space separation during the testing process. In conclusion, the conformity loss driven trained model has shown better feature space separation at comparable output performance.

PresRecST: a novel herbal prescription recommendation algorithm for real-world patients with integration of syndrome differentiation and treatment planning.

Herbal prescription recommendation (HPR) is a hot topic and challenging issue in field of clinical decision support of traditional Chinese medicine (TCM). However, almost all previous HPR methods have not adhered to the clinical principles of syndrome differentiation and treatment planning of TCM, which has resulted in suboptimal performance and difficulties in application to real-world clinical scenarios. We emphasize the synergy among diagnosis and treatment procedure in real-world TCM clinical settings to propose the PresRecST model, which effectively combines the key components of symptom collection, syndrome differentiation, treatment method determination, and herb recommendation. This model integrates a self-curated TCM knowledge graph to learn the high-quality representations of TCM biomedical entities and performs 3 stages of clinical predictions to meet the principle of systematic sequential procedure of TCM decision making. To address the limitations of previous datasets, we constructed the TCM-Lung dataset, which is suitable for the simultaneous training of the syndrome differentiation, treatment method determination, and herb recommendation. Overall experimental results on 2 datasets demonstrate that the proposed PresRecST outperforms the state-of-the-art algorithm by significant improvements (eg, improvements of P@5 by 4.70%, P@10 by 5.37%, P@20 by 3.08% compared with the best baseline). The workflow of PresRecST effectively integrates the embedding vectors of the knowledge graph for progressive recommendation tasks, and it closely aligns with the actual diagnostic and treatment procedures followed by TCM doctors. A series of ablation experiments and case study show the availability and interpretability of PresRecST, indicating the proposed PresRecST can be beneficial for assisting the diagnosis and treatment in real-world TCM clinical settings. Our technology can be applied in a progressive recommendation scenario, providing recommendations for related items in a progressive manner, which can assist in providing more reliable diagnoses and herbal therapies for TCM clinical task.

Effectiveness of the Decision-Making Process in International Security Organizations in the Context of International Crises

The relevance of the chosen topic is determined by the aggravation of interstate conflicts at the regional and interregional levels. In these conditions, the issue of the effectiveness of decision-making mechanisms and procedures in the activities of the United Nations (UN), the North Atlantic Treaty Organization (NATO), and the Organization for Security and Cooperation in Europe (OSCE) becomes relevant. The aim of the article is to carry out a comparative analysis of the decision-making process in the activities of international security organisations, such as the UN, NATO, and the OSCE, and determine their effectiveness in the context of aggravation of interstate conflicts at the regional and interregional level. Using the methodology of content analysis, descriptive, comparative methods and analysis of legal acts. The procedure for the development and adoption was analysed, and the effectiveness of the adoption and implementation of UN, NATO, and OSCE decisions was considered. Further research prospects may be the study of the mechanisms of such limitations.

A Conceptual Architecture of Fuzzy Risk-Based Decision Support Systems (R+DSS)

This paper introduces a conceptual architecture for Fuzzy Risk-Based Decision Support Systems (R+DSS). This architecture is designed to provide a comprehensive and efficient approach to decision-making procedures in various domains involving assessing and controlling potential risks. The proposed architecture exhibits versatility in its applicability across multiple fields, such as finance, healthcare, engineering, and environmental management. It incorporates these components flexibly and scalable while also being user-friendly. The framework employs fuzzy logic principles such as membership functions, rule sets, and inference methods to facilitate a thorough evaluation of the risk that accommodates the inherent uncertainties and imprecisions characteristic of real-world risk scenarios. The Fuzzy Inference Engine is a versatile and resilient risk analysis tool capable of accommodating diverse data and systems, enabling effective risk mitigation strategies. The adaptability of this architecture to effectively handle complex, uncertain and dynamic environments makes it a promising tool for decision-makers looking to improve risk assessment and management protocols.

Positivity-hardness results on Markov decision processes

This paper investigates a series of optimization problems for one-counter Markov decision processes (MDPs) and integer-weighted MDPs with finite state space. Specifically, it considers problems addressing termination probabilities and expected termination times for one-counter MDPs, as well as satisfaction probabilities of energy objectives, conditional and partial expectations, satisfaction probabilities of constraints on the total accumulated weight, the computation of quantiles for the accumulated weight, and the conditional value-at-risk for accumulated weights for integer-weighted MDPs. Although algorithmic results are available for some special instances, the decidability status of the decision versions of these problems is unknown in general. The paper demonstrates that these optimization problems are inherently mathematically difficult by providing polynomial-time reductions from the Positivity problem for linear recurrence sequences. This problem is a well-known number-theoretic problem whose decidability status has been open for decades and it is known that decidability of the Positivity problem would have far-reaching consequences in analytic number theory. So, the reductions presented in the paper show that an algorithmic solution to any of the investigated problems is not possible without a major breakthrough in analytic number theory. The reductions rely on the construction of MDP-gadgets that encode the initial values and linear recurrence relations of linear recurrence sequences. These gadgets can flexibly be adjusted to prove the various Positivity-hardness results.

Improving Computer Vision-Based Wildfire Smoke Detection by Combining SE-ResNet with SVM

Wildfire is one of the most critical natural disasters that poses a serious threat to human lives as well as ecosystems. One issue hindering a high accuracy of computer vision-based wildfire detection is the potential for water mists and clouds to be marked as wildfire smoke due to the similar appearance in images, leading to an unacceptable high false alarm rate in real-world wildfire early warning cases. This paper proposes a novel hybrid wildfire smoke detection approach by combining the multi-layer ResNet architecture with SVM to extract the smoke image dynamic and static characteristics, respectively. The ResNet model is improved via the SE attention mechanism and fully convolutional network as SE-ResNet. A fusion decision procedure is proposed for wildfire early warning. The proposed detection method was tested on open datasets and achieved an accuracy of 98.99%. The comparisons with AlexNet, VGG-16, GoogleNet, SE-ResNet-50 and SVM further illustrate the improvements.

Advancements in Reinforcement Learning Algorithms for Autonomous Systems

Reinforcement learning, often known as RL, has developed as a strong paradigm to teach autonomous software agents to make choices in contexts that are both complicated and dynamic. This abstract investigates recent developments and uses of RL in a variety of fields, showing both its transformational potential and the constraints that it faces at present. Recent developments in reinforcement learning (RL) algorithms, in particular deep reinforcement learning (DRL), have made it possible to make major advancements in autonomous decision- making tasks. DRL algorithms can learn complicated representations of state-action spaces by using deep neural networks. This allows for more efficient exploration and exploitation methods to be implemented. Additionally, advancements in algorithmic enhancements, such as prioritized experience replay and distributional reinforcement learning, have improved the stability and sample efficiency of reinforcement learning algorithms, which has made it possible for these algorithms to be used in real-world applications. Robotics, autonomous cars, game playing, finance, and healthcare are just a few of the many fields that may benefit from the use of RL. In the field of robotics, reinforcement learning (RL) makes it possible for autonomous agents to learn how to navigate, manipulate, and move about in environments that are both complicated and unstructured. To improve both safety and efficiency on the road, autonomous cars make use of reinforcement learning (RL) to make decisions in dynamic traffic situations. In finance, RL algorithms are used for portfolio optimization, algorithmic trading, and risk management. These applications serve to improve investment techniques and decision-making procedures. Furthermore, in the field of healthcare, RL supports individualized treatment planning, clinical decision support, and medical image analysis, which enables physicians to provide patients with care that is specifically suited to their needs. Despite the promising improvements and applications, RL is still confronted with several difficulties that restrict its capacity to be widely adopted and scaled. Among these problems are the inefficiency of the sample, the trade-offs between exploration and exploitation, concerns about safety and dependability, and the need for explainability and interpretability in decision-making processes. To effectively address these difficulties, it is necessary to engage in collaborative efforts across several disciplines, conduct research on algorithmic developments, and establish extensive assessment frameworks (Anon, 2022).