Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

Education Programs for Invasive Procedures Involving Nurses: A Scoping Review

Background: Implementing invasive procedures is an important part of patient management by clinical nurses;however, there are gaps between nurses’ actual knowledge and skills and expected professional care capacity. Purpose: This scoping review aimed to map the existing literature related to recent institution-provided educational programs for invasive procedures involving clinical nurses. This study seeks to understand the contents of educational programs and the methods for assessing educational effects. Methods: This scoping review was completed using the following four databases: PubMed (MEDLINE), Embase, Cochrane Library, and Emcare for the period 2000-2022. We included studies that used all forms of educational approaches (e.g., didactic lectures, hands-on training, or on-the-job training). This scoping review considered peer-reviewed publications published in English using quantitative, qualitative, or mixed approaches. A total of 83 studies underwent in the final analysis. Results and Conclusion: A combination of didactic lectures and hands-on training was provided as an educational program in most studies. Contrary to our prediction, educational interventions with advanced technologies such as VR are extremely rare, suggesting that the effectiveness of advanced technologies in learning invasive procedures should be examined to facilitate and retain educational effects more efficiently in future studies. Regarding the assessment of educational effects, nurses’ cognitive (i.e., theoretical knowledge about procedures), psychomotor (i.e., implementing procedures), and psychological aspects (e.g., confidence and self-efficacy in procedures) were evaluated using questionnaires and observational methods. While most studies used a one-group pretest-posttest design, the ratio of randomized controlled trials (RCT) was relatively low. Thus, an RCT design should be introduced in future studies to test the validity of the developed educational program more accurately.

Is it necessary to stop glucagon-like peptide-1 receptor agonists prior to endoscopic procedure? A retrospective study

BACKGROUND Glucagon-like peptide-1 receptor agonists(GLP-1 RA)are effective in diabetes and obesity,reducing hyperglycemia by increasing insulin release and delaying gastric emptying.However,they can cause gastroparesis,raising concerns about aspiration during procedures.Recent guidelines advise discontinuing GLP-1 RA before surgery to reduce the risk of pulmonary aspiration.AIM To evaluate the effect of GLP-1 RAs on gastric residual contents during endosco-pic procedures.METHODS A retrospective chart review at BronxCare Health System,New York,from January 2019 to October 2023,assessed gastric residue and aspiration in GLP-1 RA patients undergoing endoscopic procedures.Two groups were compared based on dietary status before the procedure.Data included demographics,symptoms of gastroparesis,opiate use,hemoglobin A1c,GLP-1 agonist indication,endo-scopic details,and aspiration occurrence.IBM SPSS was used for analysis,cal-culating means,standard deviations,and applying Pearson’s chi-square and t-tests for associations,with P<0.05 as being significant.RESULTS During the study,306 patients were included,with 41.2%on a clear liquid/low residue diet and 58.8%on a regular diet before endoscopy.Most patients(63.1%)were male,with a mean age of 60±12 years.The majority(85.6%)were on GLP-1 RAs for diabetes,and 10.1%reported digestive symptoms before endoscopy.Among those on a clear liquid diet,1.5%had residual food at endoscopy compared to 10%on a regular diet,which was statistically significant(P=0.03).Out of 31 patients with digestive symptoms,13%had residual food,all from the regular diet group(P=0.130).No complications were reported during or after the procedures.CONCLUSION The study reflects a significant rise in GLP-1 RA use for diabetes and obesity.A 24-hour liquid diet seems safe for endoscopic procedures without aspiration.Patients with upper gastrointestinal symptoms might have a higher residual food risk,though not statistically significant.Further research is needed to assess risks based on diabetes duration,gastroparesis,and GLP-1 RA dosing,aiming to minimize interruptions in therapy during procedures.

Invasive Procedures for Prenatal Diagnosis in Salmaniya Medical Complex in Bahrain: A Retrospective Cross-Sectional Descriptive Study

Background: Prenatal diagnosis is the process of evaluating the presence of disease or potential disease in the fetus, this enables families to be better prepared before the birth of the baby. There are non-invasive prenatal diagnosis procedures and invasive prenatal diagnosis procedures. The invasive prenatal diagnosis procedures are CVS (chorionic villus sampling) and amniocentesis. The American College of Obstetricians and Gynecologists states that invasive diagnostic testing should be available to all women, regardless of age or risk. Objective: To determine the indications, outcome and results of diagnostic invasive prenatal procedures. Study setting: The obstetrics and Gynecology Department in Salmaniya Medical Complex in Kingdom of Bahrain. Study design: Retrospective descriptive study. Study subjects and Methods: This retrospective descriptive study was conducted on 175 pregnant women who underwent invasive prenatal procedures (CVS and amniocentesis) between January 2013 and December 2018 at SMC in Kingdom of Bahrain. All medical records of the participants were reviewed and entered the study. According to the implemented procedures, medical records were categorized into two chorionic villus sampling (CVS) and amniocentesis groups. The study subject will include indications of the procedures which are advanced maternal age, hematological disorders, genetic disorders, metabolic disorders, abnormal structural findings in fetal ultrasound and previous child with aneuploidy. In addition, the study will address the complications, outcome and results of procedures. Results: About half of our indications of the procedures were due to hematological disorders (47.6%) followed by abnormal structural findings in fetal ultrasound (30.1%) then genetic disorders (15.7%), metabolic disorders (4.8%) and advanced maternal age (1.8%). Regarding complications of the procedure;threatened miscarriage or loss of pregnancy within 3 weeks was (2.3%), amniotic fluid leakage (0.7%), abdominal cramps (0.7%) and Insufficient or contaminated sample (6.2%). Regarding outcome of the pregnancy, our results showed that the loss of pregnancy was (4.8%), intrauterine fetal death or still birth was (13.9%), live birth was (63.9%), preterm delivery was (7.8%), preterm premature rupture of membrane (PPROM) was (1.8%), limbs reduction was (0.0%). Termination of pregnancy outside the country was (7.8%) of chorionic villus sampling and amniocentesis. Conclusion: CVS and amniocentesis are useful outpatient procedures to detect diagnosis or to assess whether a patient is at increased risk of having an affected fetus and that will minimize the psychological impact on the patient and to provide a proper antenatal care to the pregnant women by her obstetrician and follow up to the baby by pediatrician. In this study it was observed that most of the patients who underwent the procedure were couples either carrier or affected to sickle cell disease or Beta thalassemia.

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stability and usefulness,a sensitivity analysis is done.

Endoscopic clipping of gastrojejunostomy leakage following Whipple procedure: A case report

BACKGROUND Currently,perioperative complications of classic Whipple surgery occur at a rate of approximately 40%.Common complications include delayed gastric emptying,pancreatic fistula,and bile leakage,whereas gastrojejunostomy(GJ)leakage is rare.CASE SUMMARY This case report will assess the management of a GJ leak in a 71-year-old male patient following the Whipple procedure.After surgery,the patient was trans-ferred to the clinic after four days of intensive care,where vacuum therapy was used to handle a developing subcutaneous collection.The patient,who had bile in the drains and incision during follow-up,underwent endoscopic examination on the 21st day after the operation.An opening of approximately 4 mm was observed in the GJ anastomosis during endoscopy.Five titanium clips were used to close the openings.The drainage of bile decreased to less than 50 mL on the first day after the procedure,and the patient's oral intake was opened.CONCLUSION Current literature reports a GJ leakage rate of 0.54%following Whipple surgery,with clinical findings lasting on average between 4-34 days.Surgery was the main form of therapy for this case,with a success rate of 84%,and percutaneous drai-nage was also utilized as a treatment option.This case report is the first to docu-ment endoscopic treatment of GJ leaks following the classic Whipple procedure.

A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying capacity.This approach can capture the imprecision and ambiguity often present in human perception.With the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making algorithms.By implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

UAV maneuvering decision-making algorithm based on deep reinforcement learning under the guidance of expert experience

Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.

Hysteroscopic cervical biopsy for women with persistent human papillomavirus infection after loop electrosurgical excision procedure: A case report

BACKGROUNDAlmost all cases of cervical cancer can be attributed to human papillomavirus(HPV) infection. The loop electrosurgical excision procedure (LEEP) is widelyused to treat HPV-mediated disease;thus, cervical cancer is highly preventable.However, LEEP does not necessarily clear HPV rapidly and may affect theaccuracy of the results of ThinPrep cytology test (TCT) and cervical biopsy due tothe formation of cervical scars.CASE SUMMARYA 40-year-old woman underwent LEEP for cervical intraepithelial neoplasia grade1 approximately 10 years ago. Subsequent standard cervical cancer screeningsuggested persistent HPV-52 infection, but TCT results were negative. Cervicalbiopsy under colposcopy was performed thrice over a 10-year period, yieldingnegative pathology results. She developed abnormal vaginal bleeding after sexualactivity, persisting for approximately 1 year, and underwent hysteroscopy in ourhospital. Histopathologic evaluation confirmed adenocarcinoma in situ of theuterine cervix.CONCLUSIONPatients with long-term persistent, high-risk HPV infection and negative pathologyresults of cervical biopsy after LEEP are at risk of cervical cancer. Hysteroscopicresection of cervical canal tissue is recommended as a supplement tocervical biopsy because it helps define the lesion site and may yield a pathologicdiagnosis.

Adverse Pregnancy Outcomes Following Cryotherapy, Thermal Ablation and Loop Electrosurgical Excision Procedure for Cervical Intraepithelial Neoplasia Treatment: A Pilot Study among Zambian Women

Background: Cervical Intraepithelial neoplasia treatments have become essential interventions to manage cervical lesions. Majority of the recipients of these treatments are women within the reproductive age group, who according to literature may be at risk of adverse pregnancy outcomes. This pilot study is part of a study investigating adverse pregnancy outcomes among women who received Cryotherapy, Thermal ablation and Loop Electrosurgical Excision Procedure compared to the untreated women in Zambia. Materials and Methods: This descriptive study analyzed records of 886 (n = 443 treated and n = 443 untreated) women aged 15 - 49 years. The women were either screened with Visual Inspection with Acetic Acid or treated for Cervical Intraepithelial neoplasia at the Adult Infectious Disease Centre between January 2010 and December 2020. Women meeting the criteria were identified using the Visual Inspection with Acetic Acid screening records and telephone interviews to obtain the adverse pregnancy outcome experienced. Data were analysed using STATA version 16 to determine the prevalence and obtain frequency distribution of outcomes of interest. Univariate and multivariable binary logistic regression estimated odds of adverse pregnancy outcomes across the three treatments. Results: The respondents were aged 15 to 49 years. Adverse pregnancy outcomes were observed to be more prevalent in the treatment group (18.5%) compared to the untreated group (5.4%). Normal pregnancy outcomes were lower in the treated (46.3%;n = 443) than the untreated (53.7%;n = 443). The treated group accounted for the majority of abortions (85.2%), prolonged labour (85.7%) and low birth weight (80%), whereas, the untreated accounted for the majority of still births (72.7%). Women treated with cryotherapy (aOR = 2.43, 95% CI = 1.32 - 4.49, p = 0.004), thermal ablation (aOR = 6.37, 95% CI = 0.99 - 41.2, p = 0.052) and Loop Electrosurgical Excision Procedure (aOR = 9.67, 95% CI = 2.17 - 43.1, p = 0.003) had two-, six- and ten-times higher odds of adverse pregnancy outcomes respectively, relative to women who required no treatment. Conclusion: Adverse pregnancy outcomes are prevalent among women who have received treatment in Zambia. The findings indicate that treating Cervical Intraepithelial Neoplasia has been linked to higher chances of experiencing abortion, delivering low birth weight babies and enduring prolonged labor that may result in a caesarean section delivery. Cervical neoplasia treatments, particularly Loop Electrosurgical Excision Procedure, are associated with significantly increased odds of adverse pregnancy outcomes. It is essential to include information about prior Cervical Intraepithelial neoplasia treatment outcomes in obstetric care.

Coordinated Mining Procedures of Open Pit Mines Based on River Management

This paper primarily concerns the effective coordination of the procedures and methods employed in open pit mining operations under the background of river management.The central objective of this study is to identify a viable approach for ensuring rational and efficient development of open pit mineral resources while simultaneously protecting and restoring the ecological environment of the river.This approach should facilitate the realization of a harmonious symbiosis between mining and river management.The intricate mutual influence relationship between river management and open pit mining is first analyzed in depth,which provides a solid foundation for the subsequent coordination strategy development.In light of the aforementioned considerations,a set of coordination procedures for open pit mining based on river management conditions is proposed.These procedures emphasize the integration of river protection into the overall layout of mining at the planning stage.The implementation of scientific mining schemes,accompanied by rigorous control of the scope and depth of mining operations,has proven to be an effective means of reducing the impact of mining activities on river environments.This approach has also facilitated the achievement of a balance and coordination between mining and river management.

Optimization of Open-cast Mining Procedure Based on RSR Method

To explore the optimal evaluation mechanism of open-cast mining procedure,this paper takes the actual operation status of Huolinhe No.1 Open-cast Mine as the research basis,and makes a deep analysis of the four representative mining procedures proposed by this mine.A detailed and comprehensive evaluation system is constructed using rank-sum ratio(RSR)method.The system covers 17 key indicators and aims to evaluate the advantages and disadvantages of each scheme in an all-round and multi-angle manner.Through the calculation and analysis by RSR method,the comprehensive evaluation of the four types of mining procedure schemes is carried out,and finally the secondary river improvement project is determined as the optimal mining implementation scheme,and the joint mining scheme of the south and north areas is the alternative strategy.The research results of this paper are objective,clear and definite,can not only reveal the effectiveness and feasibility of RSR method in solving the problem of open-cast mining procedure optimization,but also provide a strong technical support and decision-making basis for the future production development of Huolinhe No.1 Open-cast Mine.Thus,this study is expected to further promote the scientific and refined process of mining operations.

Decision-Making and Management of Self-Care in Persons with Traumatic Spinal Cord Injuries: A Preliminary Study

Patients and physicians understand the importance of self-care following spinal cord injury (SCI), yet many individuals with SCI do not adhere to recommended self-care activities despite logistical supports. Neurobehavioral determinants of SCI self-care behavior, such as impulsivity, are not widely studied, yet understanding them could inform efforts to improve SCI self-care. We explored associations between impulsivity and self-care in an observational study of 35 US adults age 18 - 50 who had traumatic SCI with paraplegia at least six months before assessment. The primary outcome measure was self-reported self-care. In LASSO regression models that included all neurobehavioral measures and demographics as predictors of self-care, dispositional measures of greater impulsivity (negative urgency, lack of premeditation, lack of perseverance), and reduced mindfulness were associated with reduced self-care. Outcome (magnitude) sensitivity, a latent decision-making parameter derived from computationally modeling successive choices in a gambling task, was also associated with self-care behavior. These results are preliminary;more research is needed to demonstrate the utility of these findings in clinical settings. Information about associations between impulsivity and poor self-care in people with SCI could guide the development of interventions to improve SCI self-care and help patients with elevated risks related to self-care and secondary health conditions.

Research on Public Engineering Emergency Decision-Making Based on Multi-Granularity Language Information

To effectively deal with fuzzy and uncertain information in public engineering emergencies,an emergency decision-making method based on multi-granularity language information is proposed.Firstly,decision makers select the appropriate language phrase set according to their own situation,give the preference information of the weight of each key indicator,and then transform the multi-granularity language information through consistency.On this basis,the sequential optimization technology of the approximately ideal scheme is introduced to obtain the weight coefficient of each key indicator.Subsequently,the weighted average operator is used to aggregate the preference information of each alternative scheme with the relative importance of decision-makers and the weight of key indicators in sequence,and the comprehensive evaluation value of each scheme is obtained to determine the optimal scheme.Lastly,the effectiveness and practicability of the method are verified by taking the earthwork collapse accident in the construction of a reservoir as an example.

A Blind Spot in the Reframing of a Universe of Possibles: Towards a Suitable Model for Decision-Making Theory and A.I.

Bayesian inference model is an optimal processing of incomplete information that, more than other models, better captures the way in which any decision-maker learns and updates his degree of rational beliefs about possible states of nature, in order to make a better judgment while taking new evidence into account. Such a scientific model proposed for the general theory of decision-making, like all others in general, whether in statistics, economics, operations research, A.I., data science or applied mathematics, regardless of whether they are time-dependent, have in common a theoretical basis that is axiomatized by relying on related concepts of a universe of possibles, especially the so-called universe (or the world), the state of nature (or the state of the world), when formulated explicitly. The issue of where to stand as an observer or a decision-maker to reframe such a universe of possibles together with a partition structure of knowledge (i.e. semantic formalisms), including a copy of itself as it was initially while generalizing it, is not addressed. Memory being the substratum, whether human or artificial, wherein everything stands, to date, even the theoretical possibility of such an operation of self-inclusion is prohibited by pure mathematics. We make this blind spot come to light through a counter-example (namely Archimedes’ Eureka experiment) and explore novel theoretical foundations, fitting better with a quantum form than with fuzzy modeling, to deal with more than a reference universe of possibles. This could open up a new path of investigation for the general theory of decision-making, as well as for Artificial Intelligence, often considered as the science of the imitation of human abilities, while being also the science of knowledge representation and the science of concept formation and reasoning.

Crossing the Achilles Heel of Algorithms:Identifying the Developmental Dilemma of Artificial Intelligence-Assisted Judicial Decision-Making

In the developmental dilemma of artificial intelligence(AI)-assisted judicial decision-making,the technical architecture of AI determines its inherent lack of transparency and interpretability,which is challenging to fundamentally improve.This can be considered a true challenge in the realm of AI-assisted judicial decision-making.By examining the court’s acceptance,integration,and trade-offs of AI technology embedded in the judicial field,the exploration of potential conflicts,interactions,and even mutual shaping between the two will not only reshape their conceptual connotations and intellectual boundaries but also strengthen the cognition and re-interpretation of the basic principles and core values of the judicial trial system.