Intuitive Rules Research Articles

The epistemic and the deontic preface paradox

Abstract This paper generalizes the preface paradox beyond the conjunctive aggregation of beliefs and constructs an analogous paradox for deontic reasoning. The analysis of the deontic case suggests a systematic restriction of intuitive rules for reasoning with obligations. This proposal can be transferred to the epistemic case: It avoids the preface and the lottery paradox and saves one of the two directions of the Lockean Thesis (i.e. high credence is sufficient, but not necessary for rational belief). The resulting account compares favorably to competing proposals; in particular, we can formulate the rules of correct doxastic reasoning without reference to probabilistic features of the involved propositions.

Intelligent adaptive lighting algorithm: Integrating reinforcement learning and fuzzy logic for personalized interior lighting

The lighting requirements are subjective and one light setting cannot work for all. However, there is little work on developing smart lighting algorithms that can adapt to user preferences. To address this gap, this paper uses fuzzy logic and reinforcement learning to develop an adaptive lighting algorithm. In particular, we develop a baseline fuzzy inference system (FIS) using the domain knowledge, generating light recommendation based on a set of intuitive rules. These rules, derived from existing literature, are based on environmental conditions i.e. daily glare index, and user information including age, activity, and chronotype. Through a feedback mechanism, the user interacts with the algorithm, correcting the algorithm output to their preferences. We interpret these corrections as rewards to a Q-learning algorithm, which tunes the FIS parameters online to match the user preferences. The Q-learning is a model-free learning algorithm that learns to act optimally by interacting with the user and the rewards it receives. This allows the proposed algorithm to work in a model-free manner, effectively handling the uncertainties that might arise from the individualistic preferences of users. To the authors’ best knowledge, this algorithm is pioneering work in designing intelligent algorithms for personalized lighting control, featuring several elements of novelty, including the number of environmental and user-related inputs, the continuous control of light intensity as opposed to common on/off control, and the ability to learn user preferences. The algorithm is implemented in a real aircraft cabin and is evaluated in an extensive user study. The implementation results demonstrate that the developed algorithm possesses the capability to learn user preferences while successfully adapting to a wide range of environmental conditions and user characteristics. This underscores its viability as a potent solution for intelligent light management, featuring advanced learning capabilities.

Chemical Mapping of Excitons in Halide Double Perovskites.

Halide double perovskites comprise an emerging class of semiconductors with tremendous chemical and electronic diversity. While their band structure features can be understood from frontier-orbital models, chemical intuition for optical excitations remains incomplete. Here, we use ab initio many-body perturbation theory within the GW and the Bethe-Salpeter equation approach to calculate excited-state properties of a representative range of Cs2BB'Cl6 double perovskites. Our calculations reveal that double perovskites with different combinations of B and B' cations display a broad variety of electronic band structures and dielectric properties and form excitons with binding energies ranging over several orders of magnitude. We correlate these properties with the orbital-induced anisotropy of charge-carrier effective masses and the long-range behavior of the dielectric function by comparing them with the canonical conditions of the Wannier-Mott model. Furthermore, we derive chemically intuitive rules for predicting the nature of excitons in halide double perovskites using computationally inexpensive density functional theory calculations.

An updated conceptualization of the intuition construct for mathematics education research

To improve instruction, conduct communicable studies on student intuition, and provide an entry point for researchers interested in this area, the meaning of intuition in mathematics education needs to be addressed. This paper reviews 816 journal papers from top tier mathematics education journals to obtain an updated conceptualization of the intuition construct. Different research camps, such as a camp focused on instructional design that leverages learners’ primary intuitions in the classroom and a camp concerned with sources of errors through the lens of intuitive rules theory, emerged. A critical discussion of theoretical choices for intuition research will be followed by several directions for future research on intuition specific to university mathematics education.

Fuzzy dynamical system for robot learning motion skills from human demonstration

Learning from demonstration (LfD) is an intuitive strategy for transferring human motion skills to robots in an agile and adaptable manner. The major goal of LfD is to identify significant movement primitives (MPs) from human demonstrations and then recompose those intrinsic primitives to adapt to a variety of new situations. However, maintaining the simplicity of MPs representation while guaranteeing their adaptability is not an easy undertaking. To achieve these two goals, two approaches are possible: (1) learning models that can capture and utilize the inherent patterns and main characteristics of the human demonstrations, and (2) dynamical systems that can respond to perturbations online without requiring to re-plan the entire trajectory. In this paper, we present a novel and efficient model that combines these two benefits to formulate MPs using a fuzzy dynamical system (Fuzzy-DS), which enables robots to adaptively alter the learned motion skills to meet various additional constraints in the process of performing tasks. Due to the joint use of a fuzzy inference system and a dynamic system, Fuzzy-DS is well-suited to human inputs and intuitive fuzzy rules, resulting in a computationally efficient model. To verify the effectiveness of the proposed method, experiments have been designed where the robot learned a plant pruning task and a pick-and-place task, subsequently, it can replicate and generalize these tasks to novel situations.

Different intuitive rules in mathematical thinking

This study examines the behavior of individuals when two intuitive rules in their minds lead to two different outcomes concerning a problem. In the absence of the formal knowledge, intuitive rules can affect the mathematical thinking. Hence, studies have usually compared the correct answer of the related formal knowledge with the incorrect one obtained from one intuitive rule. What has been rather less explored is the situation where both correct and incorrect answers are obtained from intuitive rules. In so doing, comparing infinite sets as a challenging issue for learning was selected as the theme of the study. Two specific geometric representations regarding the comparison of two intervals were employed, one in line with the intuitive rule ‘More A - More B’ and the other in line with the ‘Same A - Same B’ rule, so that two different results are produced by each of the rules. 85 first grader high school students who had no formal knowledge about comparing infinite sets were participated in the study, and the data were collected through interview. Analysis of the data showed that when the two intended different intuitive rules can be activated in mind and, in turn, create a cognitive conflict, individuals rank intuitive rules based on the degree of transparency of the concepts they have already known.

Comparing Bayesian-Based Reconstruction Strategies in Topology-Based Pathway Enrichment Analysis.

The development of high-throughput omics technologies has enabled the quantification of vast amounts of genes and gene products in the whole genome. Pathway enrichment analysis (PEA) provides an intuitive solution for extracting biological insights from massive amounts of data. Topology-based pathway analysis (TPA) represents the latest generation of PEA methods, which exploit pathway topology in addition to lists of differentially expressed genes and their expression profiles. A subset of these TPA methods, such as BPA, BNrich, and PROPS, reconstruct pathway structures by training Bayesian networks (BNs) from canonical biological pathways, providing superior representations that explain causal relationships between genes. However, these methods have never been compared for their differences in the PEA and their different topology reconstruction strategies. In this study, we aim to compare the BN reconstruction strategies of the BPA, BNrich, PROPS, Clipper, and Ensemble methods and their PEA and performance on tumor and non-tumor classification based on gene expression data. Our results indicate that they performed equally well in distinguishing tumor and non-tumor samples (AUC > 0.95) yet with a varying ranking of pathways, which can be attributed to the different BN structures resulting from the different cyclic structure removal strategies. This can be clearly seen from the reconstructed JAK-STAT networks by different strategies. In a nutshell, BNrich, which relies on expert intervention to remove loops and cyclic structures, produces BNs that best fit the biological facts. The plausibility of the Clipper strategy can also be partially explained by intuitive biological rules and theorems. Our results may offer an informed reference for the proper method for a given data analysis task.

Fuzzy-reasoning-based robust vibration controller for drivetrain mechanism with various control input updating timings

For active vibration control of powertrain oscillations, the purpose of this research is to present a simple fuzzy-reasoning-based compensation strategy for a time-varying control cycle limitation of an actuator. A simplified drivetrain dynamics model is shown, and the simulation configuration with the control cycle limitation is constructed. A model predictive compensation using a sampled-data controller is employed to tackle the maximal phase delay of the control input due to the time-varying control cycle. The control input update timing, which is perturbed from that of the fixed periodical controller, is expressed as fuzzy sets such as “Approximately past step” and “Approximately future step”. Based on these fuzzy sets, the Tsukamoto-type fuzzy reasoning with only four intuitive rules can flexibly infer unknown control inputs updated at various timings. Simulation verifications demonstrate that the 2-norm of the vibration performance by the proposed method is reduced by more than 27.8% compared to other previous controllers. Moreover, it is revealed that the better transient performance can be maintained even with 10% plant parameter variations, indicating the high robustness of the proposed damping controller.

An Approximation Scheme for Data Monetization

The unprecedented rate at which data are being generated has led to the growth of data markets where valuable data sets are bought and sold. A salient feature of this market is that a data‐buyer (agent) is endowed with multidimensional private information, namely, her “ideal” record that she values the most and how her valuation for a given record changes as its distance from her ideal record changes. Consequently, the revenue‐maximization problem faced by a data‐seller (principal), who serves multiple buyers, is a multidimensional mechanism‐design problem, which is well recognized as being difficult to solve. Our main result in this paper is an approximation scheme that guarantees a revenue within as close a positive amount from the optimal revenue as desired. The scheme generates a posted‐price menu consisting of a set of item–price pairs—each entry in the menu consists of an item, that is, a set of records from the data set, and the price corresponding to that item. As a trade‐off, the length of the menu resulting from the scheme increases as the desired guarantee gets closer to zero. For convenience in practice, data‐sellers may want the ability to limit the length of the menu used by the scheme. To facilitate this, we extend our analysis to obtain a general approximation guarantee corresponding to a menu of any given length. We also demonstrate how the seller can exploit buyers' preferences to generate intuitive and useful rules of thumb for an effective practical implementation of the scheme.

Method for Cleaning Abnormal Data of Wind Turbine Power Curve Based on Density Clustering and Boundary Extraction

This paper creatively proposes a complete set of procedures to identify and eliminate outliers of wind-power data based on the framework of classification processing. Outliers are divided into three types. According to the characteristics of each type of outlier, separate suitable algorithms are proposed. Through a comprehensive and in-depth analysis of the bottom stacked points, several operating modes of wind turbines at ultra-low wind speeds are discovered, and an intuitive rules method is innovatively proposed based on mechanism analysis. With the intuitive rules method, normal points and type 1 outliers are separated accurately in a reasonable way, and then type 1 outliers are cleaned up completely. For type 3 outliers, an improved density clustering method is proposed that makes full use of the density difference between normal points and type 3 outliers to achieve better cleaning performance. For type 2 outliers, a combined method including boundary extraction and boundary regularization is proposed. Type 2 outliers are completely removed as abnormal bulges on the boundary. Twelve datasets from three wind farms were employed to verify the effectiveness, superiority, and strong generalization of the proposed method. The power curve models based on smoothing spline method are used to visually display the effect of outlier cleaning. The identification accuracy (P), identification rate (R) and overall accuracy (F1) are introduced to quantitatively evaluate the cleaning performance.

Simulating favorable adsorption in lithium-ion batteries using a novel cellular-automaton-based method

The advances in micro and nano-technologies have increased the demand to study surface effects such as adsorption using novel methods. Cellular automata, being a simple and to some extend intuitive method, seems a great candidate. Here, we have developed a simple adsorption model based on cellular automata to model ‘favorable’ adsorption isotherms. Using intuitive local rules based on the three common adsorption isotherms, i.e., Langmuir, Elovich, and Hill-Deboer isotherms, the model matched the predicted behavior of these isotherms completely. To further show the power of the model, we analyzed the adsorption of Li-ions on titania. Although we have neglected the absorption in the bulk of the titania, our results are in good accord with the experimental data. This accordance shows that even using a high level of simplification, the model can predict the behavior of a very complicated system such as a Li-ion battery. Therefore, considering the potential of parallelization, ease of calibration with experiments, and modifiability, this model can serve as a suitable computational tool for studying adsorption.

Scheduling heterogeneous delivery tasks on a mixed logistics platform

Large e-commerce retailers usually establish their own logistics systems. Such systems make use of their own dedicated fleets but will also use a crowdsourced delivery mode by hiring occasional fleets. These mixed logistics systems with both dedicated and occasional fleets serve both retailers’ internal delivery tasks and external tasks requested by local businesses. This paper studies the problem of scheduling heterogeneous (internal and external) delivery tasks on a mixed logistics platform with multiple depots and two types of vehicles (dedicated and occasional). A delivery task is executed by either a dedicated vehicle or an occasional vehicle. The dedicated vehicles depart from and return to the platform's depots; the occasional vehicles depart from their original location and pick up goods from depots or external pickup locations, fulfill the delivery tasks, and finish their route at the final delivery location. We propose mixed integer programming models and column generation-based solution methods to solve the problem. A computational study is conducted based on a series of randomly generated instances and real-world instances involving 15 depots, 120 internal customers, 15 external delivery tasks, and 38 dedicated and occasional vehicles. The results obtained demonstrate the efficiency of the column generation-based solution methods. Moreover, the effectiveness of the proposed models is validated by a significant cost saving in comparison to intuitive decision rules. A sensitivity analysis is also conducted to derive a number of managerial implications.

Modeling the Glass Transition and Glassy Dynamics of Random Copolymers Using the TS2 Mean-Field Approach

Understanding the glass transition and glassy dynamics in random copolymers and miscible polymer blends is crucial for both fundamental and applied polymer science. While there are multiple models describing the dependence of the glass transition temperature on the copolymer or blend composition (Fox, Gordon-Taylor, Kwei, and others), it is still not easy to describe or predict the broader temperature dependence of relaxation times in these systems. Here, we expand our TS2 (two states, two (time)scales) mean-field model to random copolymers and formulate simple and intuitive mixing rules for the model parameters as a function of composition. We obtain a new equation for the characteristic transition temperature, T*, that can serve as an approximate predictor for the glass transition temperature, Tg. The model then allows us to refine the prediction and calculate Tg based on the standard criterion (τα(Tg) = 10² s). We can also calculate the temperature-dependent α- and β-relaxation times for different copolymer compositions. To test the theory, we computed the glass transition temperatures and relaxation times for random styrene-methylmethacrylate (PS-r-PMMA) and statistical styrene-butylmethacrylate (PS-stat-PnBMA) copolymers and found good qualitative and semi-quantitative agreement with published literature data. The described approach can be easily extended to blends and copolymers with multiple components.

An Approximation Scheme for Data Monetization

The unprecedented rate at which data is being generated has led to the growth of data markets where valuable datasets are bought and sold. A salient feature of this market is that a data-buyer (agent) is endowed with multi-dimensional private information, namely her ``ideal'' record that she values the most and how her valuation for a given record changes as its distance from her ideal record changes. Consequently, the revenue-maximization problem faced by a data-seller (principal), who serves multiple buyers, is a multi-dimensional mechanism-design problem, which is well-recognized as being difficult to solve. Our main result in this paper is an approximation scheme that guarantees a revenue within as close a positive amount from the optimal revenue as desired. The scheme generates a posted-price menu consisting of a set of item-price pairs -- each entry in the menu consists of an item, i.e., a set of records from the dataset, and the price corresponding to that item. As a tradeoff, the length of the menu resulting from the scheme increases as the desired guarantee gets closer to zero. For convenience in practice, data-sellers may want the ability to limit the length of the menu used by the scheme. To facilitate this, we extend our analysis to obtain a general approximation guarantee corresponding to a menu of any given length. We also demonstrate how the seller can exploit buyers' preferences to generate intuitive and useful rules of thumb for an effective practical implementation of the scheme.

Hypothetical tensor-based multi-criteria recommender system for new users with partial preferences

Multi-Criteria Recommender Systems (MCRSs) have been developed to improve the accuracy of single-criterion rating-based recommender systems that could not express and reflect users? fine-grained rating behaviors. In most MCRSs, new users are asked to express their preferences on multi-criteria of items, to address the cold-start problem. However, some of the users? preferences collected are usually not complete due to users? cognitive limitation and/or unfamiliarity on item domains, which is called ?partial preferences?. The fundamental challenge and then negatively affects to accurately recommend items according to users? preferences through MCRSs. In this paper, we propose a Hypothetical Tensor Model (HTM) to leverage auxiliary data complemented through three intuitive rules dealing with user?s unfamiliarity. First, we find four patterns of partial preferences that are caused by users? unfamiliarity. And then the rules are defined by considering relationships between multi-criteria. Lastly, complemented preferences are modeled by a tensor to maintain an inherent structure of and correlations between the multi-criteria. Experiments on a TripAdvisor dataset showed that HTM improves MSE performances from 40 to 47% by comparing with other baseline methods. In particular, effectivenesses of each rule regarding multi-criteria on HTM are clearly revealed.

Modeling the Spread of Epidemics Based on Cellular Automata

Mathematical modeling is a powerful tool to study the process of the spread of infectious diseases. Among various mathematical methods for describing the spread of infectious diseases, the cellular automaton makes it possible to explicitly simulate both the spatial and temporal evolution of epidemics with intuitive local rules. In this paper, a model is proposed and realized on a cellular automata platform, which is applied to simulate the spread of coronavirus disease 2019 (COVID-19) for different administrative districts. A simplified social community is considered with varying parameters, e.g., sex ratio, age structure, population movement, incubation and treatment period, immunity, etc. COVID-19 confirmation data from New York City and Iowa are adopted for model validation purpose. It can be observed that the disease exhibits different spread patterns in different cities, which could be well accommodated by this model. Then, scenarios under different control strategies in the next 100 days in Iowa are simulated, which could provide a valuable reference for decision makers in identifying the critical factors for future infection control in Iowa.

Learning (to disagree?) in large worlds

Beginning with Robert Aumann's 1976 “Agreeing to Disagree” result, a collection of papers have established conditions under which it is impossible for rational agents to disagree, or bet against each other, or speculate in markets. The subsequent literature has provided many explanations for disagreement and trade, typically exploiting differences in prior beliefs or information processing. We view such differences as arising most naturally in a “large worlds” setting, where there is no commonly-accepted understanding of the underlying uncertainty. This paper develops a large-worlds model of reasoning and examines how agents learn in such a setting, with particular interest in whether accumulated experience will lead them to common beliefs (and hence to agree, and to cease trading). No learning rule invariably ensures learning, leaving ample room for persistent disagreement and trade. However, there are intuitive learning rules that lead people with different models of the underlying uncertainty to a common view of the world if the data generating process is sufficiently structured.

Toward Developing Intuitive Rules for Protein Variant Effect Prediction Using Deep Mutational Scanning Data.

Protein structure and function can be severely altered by even a single amino acid mutation. Predictions of mutational effects using extensive artificial intelligence (AI)-based models, although accurate, remain as enigmatic as the experimental observations in terms of improving intuitions about the contributions of various factors. Inspired by Lipinski’s rules for drug-likeness, we devise simple thresholding criteria on five different descriptors such as conservation, which have so far been limited to qualitative interpretations such as high conservation implies high mutational effect. We analyze systematic deep mutational scanning data of all possible single amino acid substitutions on seven proteins (25153 mutations) to first define these thresholds and then to evaluate the scope and limits of the predictions. At this stage, the approach allows us to comment easily and with a low error rate on the subset of mutations classified as neutral or deleterious by all of the descriptors. We hope that complementary to the accurate AI predictions, these thresholding rules or their subsequent modifications will serve the purpose of codifying the knowledge about the effects of mutations.

Directed correlation network of concepts determined by the dynamics of publications

A technique for forming, clustering and visualizing so-called directed correlation networks is herein proposed. The links between nodes of such networks correspond to the values of cross-correlations between vectors - sets of parameters corresponding to these nodes modified in a certain way. To build network structures for each node (topic), vectors are formed - arrays of numbers corresponding to a certain time series. As an example, the article considers a time series generated by the Google Books Ngram Viewer service. This approach, unlike the existing one, has advantages such as intuitive and realistic rules, the definition of the weight of nodes and links, a reliable mathematical basis for correlation analysis, an accounting of previously unused parameters of time series of publications corresponding to entities, allowing one to the group said entities according to their trends in time, and objectivity and relative simplicity. This technique can be based on data obtained, for example, from content monitoring systems, and can be used in analytical systems for various purposes in order to generalize a set of variables without explicit links between them.

Simulation optimization iteration approach on traffic integrated yard allocation problem in transshipment terminals

To improve the efficiency of the container terminals, we propose a simulation optimization iteration approach to integrate the yard allocation problem (YAP) with the vehicle congestion problem. YAP is formulated as a mixed integer programming model to reduce the total job travel time. A discrete event simulation model is developed to simulate the terminal operation and traffic movement within the terminal. The approach solves two models iteratively to improve the allocation decisions. Experiment results show that this approach can effectively generate yard allocation decisions, which reduce the overall traffic time comparing with traditional intuitive rules.