Search results for: backward chaining inference

Authors: Gennady P. Ginkul, Sergey Yu. Soloviov

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The article proposes to use so-called "quotation-based algorithm" for simulation of decision making process in distributed expert systems and multi-agent systems. The idea was adopted from the techniques for group decision-making. It is based on the assumption that one expert system to perform its logical inference may use rules from another expert system. The application of the algorithm was demonstrated on the example in which the consolidated decision is the decision that requires minimal quotation.

Keywords: backward chaining inference, distributed expert systems, group decision making, multi-agent systems

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Authors: Pierce Radecki, Pulkit Malik, Bharath Ramaswamy, Ben Shapiro

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The ability to effectively design and test magnetic nanoparticles for controlled movement has been an elusive goal in the design of these particles. Magnetic nanoparticles of various characteristics have been created for use towards therapeutic effects, however the challenge of designing for controlled movement remains unmet. A step towards design in this aspect is a first principles model that captures and predicts the behaviors of particles in a magnetic field. The model is governed by four forces acting on the particles, the magnetic gradient, the dipole-dipole forces, the steric forces, and the viscous drag force. The particles are multi-core or single core, and incorporate a preferred magnetization axis. Particles exhibit behaviors, such as chaining, in simulations that are similar to those witnessed through experimentation. Currently, experimental results are being compared to the modeling results for verification of the model, through the analysis of chaining behaviors. This modeling system will be used in designing magnetic nanoparticles for specific chaining and movement behaviors.

Keywords: controlled movement, modeling, magnetic nanoparticles, nanoparticle design

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Authors: K. Vandenboer, L. Dolphen, A. Bezuijen

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Backward erosion piping is an important failure mechanism for water-retaining structures, a phenomenon that results in the formation of shallow pipes at the interface of a sandy or silty foundation and a cohesive cover layer. This paper studies the effect of two soil types on backward erosion piping; both in case of a homogeneous sand layer, and in a vertically layered sand sample, where the pipe is forced to subsequently grow through the different layers. Two configurations with vertical sand layers are tested; they both result in wider pipes and higher critical gradients, thereby making this an interesting topic in research on measures to prevent backward erosion piping failures.

Keywords: backward erosion piping, embankments, physical modeling, sand

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Authors: Neda Dekamei, Mostafa Sarabzadeh, Masoumeh Bigdeli

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Backward running is commonly used in different sports conditioning, motor learning, and neurological purposes, and even more commonly in physical rehabilitation. The present study evaluated the effects of six weeks backward and forward running methods on balance promotion adaptation in students. 12 male and female preseniors with the age range of 45-60 years participated and were randomly classified into two groups of backward running (n: 6) and forward running (n: 6) training interventions. During six weeks, 3 sessions per week, all subjects underwent stated different models of backward and forward running training on treadmill (65-80 of HR max). Pre and post-tests were performed by force plate and electromyogram, two times before and after intervention. Data were analyzed using by T test. On the basis of obtained data, significant differences were recorded on balance and plantar flexion force in backward running (BR) and no difference for forward running (FR). It seems the training model of backward running can generate more stimulus to achieve better plantar flexion force and strengthening ankle protectors which leads to balance improvement in pre aging period. It can be recommended as an effective method to promote seniors life quality especially in balance neuromuscular parameters.

Keywords: backward running, balance, plantar flexion, pre seniors

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Authors: Iliya Musa Adamu

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This paper seeks to develop an expert system that would troubleshoot computer network and evaluate the network system performance so as to reduce the workload on technicians and increase the efficiency and effectiveness of solutions proffered to computer network problems. The platform of the system was developed using ASP.NET, whereas the codes are implemented in Visual Basic and integrated with SQL Server 2005. The knowledge base was represented using production rule, whereas the searching method that was used in developing the network troubleshooting expert system is the forward-chaining-rule-based-system. This software tool offers the advantage of providing an immediate solution to most computer network problems encountered by computer users.

Keywords: expert system, forward chaining rule based system, network, troubleshooting

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Authors: Jemal Ebrahim Dessie, Zsolt Lukacs

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The wall of the aerosol can is extruded from the backward extrusion process. Necking is another forming process stage developed on the can shoulder after the backward extrusion process. Due to the thinner thickness of the wall, buckling is the critical challenge for current pure aluminum aerosol can industries. Design and investigation of extrusion with inhomogeneous wall thickness could be the best solution for reducing and optimization of neck retraction numbers. FEM simulation of inhomogeneous wall thickness has been simulated through this investigation. From axisymmetric Deform-2D backward extrusion, an aerosol can with a thickness of 0.4 mm at the top and 0.33 mm at the bottom of the aerosol can have been developed. As the result, it can optimize the number of retractions of the necking process and manufacture defect-free aerosol can shoulder due to the necking process.

Keywords: aerosol can, backward extrusion, Deform-2D, necking

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Authors: Hussein Togun, Tuqa Abdulrazzaq, S. N. Kazi, A. Badarudin, M. K. A. Ariffin, M. N. M. Zubir

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Heat transfer and laminar fluid flow over backward facing step with and without obstacle numerically studied in this paper. The finite volume method adopted to solve continuity, momentum and energy equations in two dimensions. Backward facing step without obstacle and with different dimension of obstacle were presented. The step height and expansion ratio of channel were 4.8mm and 2 respectively, the range of Reynolds number varied from 75 to 225, constant heat flux subjected on downstream of wall was 2000W/m2, and length of obstacle was 1.5, 3, and 4.5mm with width 1.5mm. The separation length noticed increase with increase Reynolds number and height of obstacle. The result shows increase of heat transfer coefficient for backward facing step with obstacle in compared to those without obstacle. The maximum enhancement of heat transfer observed at 4.5mm of height obstacle due to increase recirculation flow after the obstacle in addition that at backward. Streamline of velocity showing the increase of recirculation region with used obstacle in compared without obstacle and highest recirculation region observed at obstacle height 4.5mm. The amount of enhancement heat transfer was varied between 3-5% compared to backward without obstacle.

Keywords: separation flow, backward facing step, heat transfer, laminar flow

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Authors: May George

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Inference plays an important role in the learning process and it can lead to a rapid acquisition of a second language. When learning a non-native language, i.e., a critical language like Arabic, the students depend on the teacher’s support most of the time to learn new concepts. The students focus on memorizing the new vocabulary and stress on learning all the grammatical rules. Hence, the students became mechanical and cannot produce the language easily. As a result, they are unable to predict the meaning of words in the context by relying heavily on the teacher, in that they cannot link their prior knowledge or even identify the meaning of the words without the support of the teacher. This study explores how the teacher guides students learning during the inference process and what are the processes of learning that can direct student’s inference.

Keywords: inference, reading, Arabic, language acquisition

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Authors: Faisal Mehmood

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In this globalization era, the supply chain management has acquired strategic importance in diverse business environments. In the current highly competitive business environment, the success of any business considerably depends on the efficiency of the supply chain. Management has now realized that due to the inefficiency of any member of supply chain, the profitability of the business will be affected. This paper proposes an analysis of backward supply chain in the beverages industry of Pakistan. Although reuse of products and materials is a common phenomenon, companies have long ignored this important part of the supply chain, known as backward supply chain or reverse logistics. The beverage industry is among the pioneers of backward supply chain or reverse logistics in Pakistan. The empty glass bottles are returned back from the point of consumption to the warehouse for refilling and reusability purposes. Due to the lack of information on reverse flow of logistics and more attention on the forward distribution, beverages industry in Pakistan is facing high rate of inefficiencies and ineffectiveness. Analysis of backward or reverse logistics practiced in beverages industry is the subject of this study in which framework dictating the current needs of market will be developed.

Keywords: backward supply chain, reverse logistics, refilling, re-usability

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Authors: Soyoon Bak, Sunyoung Bu, Philsu Kim

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In this paper, a backward semi-Lagrangian scheme combined with the second-order backward difference formula is designed to calculate the numerical solutions of nonlinear advection-diffusion equations. The primary aims of this paper are to remove any iteration process and to get an efficient algorithm with the convergence order of accuracy 2 in time. In order to achieve these objects, we use the second-order central finite difference and the B-spline approximations of degree 2 and 3 in order to approximate the diffusion term and the spatial discretization, respectively. For the temporal discretization, the second order backward difference formula is applied. To calculate the numerical solution of the starting point of the characteristic curves, we use the error correction methodology developed by the authors recently. The proposed algorithm turns out to be completely iteration-free, which resolves the main weakness of the conventional backward semi-Lagrangian method. Also, the adaptability of the proposed method is indicated by numerical simulations for Burgers’ equations. Throughout these numerical simulations, it is shown that the numerical results are in good agreement with the analytic solution and the present scheme offer better accuracy in comparison with other existing numerical schemes. Semi-Lagrangian method, iteration-free method, nonlinear advection-diffusion equation, second-order backward difference formula

Keywords: Semi-Lagrangian method, iteration free method, nonlinear advection-diffusion equation, second-order backward difference formula

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Authors: Yihao Kuang, Bowen Ding

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With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graphs and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improved strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain a better and more efficient inference effect by introducing PPO into knowledge inference technology.

Keywords: reinforcement learning, PPO, knowledge inference

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Authors: Yihao Kuang, Bowen Ding

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With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graph and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improve strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain better and more efficient inference effect by introducing PPO into knowledge inference technology.

Keywords: reinforcement learning, PPO, knowledge inference, supervised learning

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Authors: Yas Barzegaar, Atrin Barzegar

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The aim of this research is to develop an intelligent system to analyze the risk level of wheat flour product manufacturing system. The model consists of five Fuzzy Inference Systems in two different layers to analyse the risk of a wheat flour product manufacturing system. The first layer of the model consists of four Fuzzy Inference Systems with three criteria. The output of each one of the Physical, Chemical, Biological and Environmental Failures will be the input of the final manufacturing systems. The proposed model based on Mamdani Fuzzy Inference Systems gives a performance ranking of wheat flour products manufacturing systems. The first step is obtaining data to identify the failure modes from expert’s opinions. The second step is the fuzzification process to convert crisp input to a fuzzy set., then the IF-then fuzzy rule applied through inference engine, and in the final step, the defuzzification process is applied to convert the fuzzy output into real numbers.

Keywords: failure modes, fuzzy rules, fuzzy inference system, risk assessment

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Authors: H. Abdolvand, M. Riazat, H. Sohrabi, G. Faraji

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An experimental investigation into the Tube Backward Extrusion (TBE) process on AZ31 magnesium alloy is studied. Microstructures and grain size distribution of the specimens before and after TBE process are investigated by optical microscopy. Tensile and Vickers microhardness tests along extrusion direction were performed at room temperature. It is found that the average grain size is refined remarkably from the initial 33 µm down to 3.5 µm after TBE process. Also, the microhardness increased significantly to 58 HV after the process from an initial value of 36 HV.

Keywords: tube backward extrusion, AZ31, grain size distribution, grain refinement

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Authors: Purnima Pandit

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Malaria remains one of the world’s most deadly infectious disease and arguably, the greatest menace to modern society in terms of morbidity and mortality. To choose the right treatment and to ensure a quality of life suitable for a specific patient condition, early and accurate diagnosis of malaria is essential. It reduces transmission of disease and prevents deaths. Our work focuses on designing an efficient, accurate fuzzy inference system for malaria diagnosis.

Keywords: fuzzy inference system, fuzzy logic, malaria disease, triangular fuzzy number

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Authors: Atrin Barzegar, Yas Barzegar, Stefano Marrone, Francesco Bellini, Laura Verde

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The aim of this research is to develop an intelligent system to analyze the risk level of wheat flour product manufacturing system. The model consists of five Fuzzy Inference Systems in two different layers to analyse the risk of a wheat flour product manufacturing system. The first layer of the model consists of four Fuzzy Inference Systems with three criteria. The output of each one of the Physical, Chemical, Biological and Environmental Failures will be the input of the final manufacturing systems. The proposed model based on Mamdani Fuzzy Inference Systems gives a performance ranking of wheat flour products manufacturing systems. The first step is obtaining data to identify the failure modes from expert’s opinions. The second step is the fuzzification process to convert crisp input to a fuzzy set., then the IF-then fuzzy rule applied through inference engine, and in the final step, the defuzzification process is applied to convert the fuzzy output into real numbers.

Keywords: failure modes, fuzzy rules, fuzzy inference system, risk assessment

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Authors: Sharon-Yasotha Veerayah-Mcgregor, Valipuram Manoranjan

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A backward or inverse problem is known to be an ill-posed problem due to its instability that easily emerges with any slight change within the conditions of the problem. Therefore, only a limited number of numerical approaches are available to solve a backward problem. This paper considers the Nagumo equation, an equation that describes impulse propagation in nerve axons, which also models population growth with the Allee effect. A creative operator splitting numerical scheme is constructed to solve the inverse Nagumo equation. Computational simulations are used to verify that this scheme is stable, accurate, and efficient.

Keywords: inverse/backward equation, operator-splitting, Nagumo equation, ill-posed, finite-difference

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Authors: Givi Kupatadze

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Backward economies are facing a challenge of achieving sustainable high economic growth rate. Dominant growth models represent a roadmap in framing economic development strategy. This paper examines a relevance of the dominant growth models for backward economies. Cobb-Douglas production function, the Harrod-Domar model of economic growth, the Solow growth model and general formula of gross domestic product are examined to undertake a comprehensive study of the dominant growth models. Deductive research method allows to uncover major weaknesses of the dominant growth models and to come up with practical implications for economic development strategy. The key finding of the paper shows, contrary to what used to be taught by textbooks of economics, that constant returns to scale property of the dominant growth models are a mere coincidence and its generalization over space and time can be regarded as one of the most unfortunate mistakes in the whole field of political economy. The major suggestion of the paper for backward economies is that understanding and considering taxonomy of economic activities based on increasing and diminishing returns to scale represent a cornerstone of successful economic development strategy.

Keywords: backward economies, constant returns to scale, dominant growth models, taxonomy of economic activities

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Authors: Nazanin Pilevari, Kamyar Mahmoodi

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The purpose of this research is to provide a model based on fuzzy inference system for evaluating empowerment of Knowledge management. The first prototype of the research was developed based on the study of literature. In the next step, experts were provided with these models and after implementing consensus-based reform, the views of Fuzzy Delphi experts and techniques, components and Index research model were finalized. Culture, structure, IT and leadership were considered as dimensions of empowerment. Then, In order to collect and extract data for fuzzy inference system based on knowledge and Experience, the experts were interviewed. The values obtained from designed fuzzy inference system, made review and assessment of the organization's empowerment of Knowledge management possible. After the design and validation of systems to measure indexes ,empowerment of Knowledge management and inputs into fuzzy inference) in the AYANDEH Bank, a questionnaire was used. In the case of this bank, the system output indicates that the status of empowerment of Knowledge management, culture, organizational structure and leadership are at the moderate level and information technology empowerment are relatively high. Based on these results, the status of knowledge management empowerment in AYANDE Bank, was moderate. Eventually, some suggestions for improving the current situation of banks were provided. According to studies of research history, the use of powerful tools in Fuzzy Inference System for assessment of Knowledge management and knowledge management empowerment such an assessment in the field of banking, are the innovation of this Research.

Keywords: knowledge management, knowledge management empowerment, fuzzy inference system, fuzzy Delphi

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Authors: S. A. M. Yatim, Z. B. Ibrahim, K. I. Othman, M. Suleiman

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The method of solving second order stiff ordinary differential equation (ODEs) that is based on backward differentiation formula (BDF) is considered in this paper. We derived the method by increasing the order of the existing method using an improved strategy in choosing the step size. Numerical results are presented to compare the efficiency of the proposed method to the MATLAB’s suite of ODEs solvers namely ode15s and ode23s. The method was found to be efficient to solve second order ordinary differential equation.

Keywords: backward differentiation formulae, block backward differentiation formulae, stiff ordinary differential equation, variable step size

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Authors: Mikhail Zarechnev, Bora I. Kumova

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A syllogism is a deductive inference scheme used to derive a conclusion from a set of premises. In a categorical syllogisms, there are only two premises and every premise and conclusion is given in form of a quantified relationship between two objects. The different order of objects in premises give classification known as figures. We have shown that the ordered combinations of 3 generalized quantifiers with certain figure provide in total of 108 syllogistic moods which can be considered as different inference rules. The classical syllogistic system allows to model human thought and reasoning with syllogistic structures always attracted the attention of cognitive scientists. Since automated reasoning is considered as part of learning subsystem of AI agents, syllogistic system can be applied for this approach. Another application of syllogistic system is related to inference mechanisms on the Semantic Web applications. In this paper we proposed the mathematical model and algorithm for syllogistic reasoning. Also the model of iterative syllogistic reasoning in case of continuous flows of incoming data based on case–based reasoning and possible applications of proposed system were discussed.

Keywords: categorical syllogism, case-based reasoning, cognitive architecture, inference on the semantic web, syllogistic reasoning

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Authors: Archana Thangavelu, Han-Ik Park, Young-Chul Park, Joon-Hong Park

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In this numerical study, we have proposed a method for evaluation of backward extrusion process of pressure vessel made up of steel. Demand for lighter and stiffer products have been increasing in the last years especially in automobile engineering. Through detailed finite element analysis, effective stress, strain and velocity profile have been obtained with optimal range. The process design of a forward and backward extrusion axe-symmetric part has been studied. Forging is mainly carried out because forged products are highly reliable and possess superior mechanical properties when compared to normal products. Performing computational simulations of 3D hot forging with various dimensions of billet and optimization of weight is carried out using Taguchi Orthogonal Array (OA) Optimization technique. The technique used in this study can be used for newly developed materials to investigate its forgeability for much complicated shapes in closed hot die forging process.

Keywords: backward extrusion, hot forging, optimization, finite element analysis, Taguchi method

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Authors: Sung Hyun Yoo, In Hwan Choi, Jun Ho Jung, Choon Ki Ahn, Myo Taeg Lim

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Home energy management system (HEMS) has been issued to reduce the power consumption. The HEMS performs electric power control for the indoor electric device. However, HEMS commonly treats the smart devices. In this paper, we suggest the output estimation of home legacy device using the artificial neural fuzzy inference system (ANFIS). This paper discusses the overview and the architecture of the system. In addition, accurate performance of the output estimation using the ANFIS inference system is shown via a numerical example.

Keywords: artificial neural fuzzy inference system (ANFIS), home energy management system (HEMS), smart device, legacy device

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Authors: Khairil I. Othman, Nur N. Kamal, Zarina B. Ibrahim

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In this paper, we present modified Newton method as a new strategy for improving the efficiency of Two Point Block Backward Differentiation Formulas (BBDF) when solving stiff systems of ordinary differential equations (ODEs). These methods are constructed to produce two approximate solutions simultaneously at each iteration The detailed implementation of the predictor corrector BBDF with PE(CE)2 with modified Newton are discussed. The proposed modification of BBDF is validated through numerical results on some standard problems found in the literature and comparisons are made with the existing Block Backward Differentiation Formula. Numerical results show the advantage of using the new strategy for solving stiff ODEs in improving the accuracy of the solution.

Keywords: newton method, two point, block, accuracy

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Authors: Md Yeasin, Ranjit Kumar Paul

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In recent years, recommendation systems have become indispensable tools for agricultural system. The accurate and timely recommendations can significantly impact crop yield and overall productivity. Causal inference modeling aims to establish cause-and-effect relationships by identifying the impact of variables or factors on outcomes, enabling more accurate and reliable recommendations. New advancements in causal inference models have been found in the literature. With the advent of the modern era, deep learning and machine learning models have emerged as efficient tools for modeling. This study proposed an innovative approach to enhance recommendation systems-based machine learning based casual inference model. By considering the causal effect and opportunity cost of covariates, the proposed system can provide more reliable and actionable recommendations for cereal farmers. To validate the effectiveness of the proposed approach, experiments are conducted using cereal cultivation data of eastern India. Comparative evaluations are performed against existing correlation-based recommendation systems, demonstrating the superiority of the advanced causal inference modeling approach in terms of recommendation accuracy and impact on crop yield. Overall, it empowers farmers with personalized recommendations tailored to their specific circumstances, leading to optimized decision-making and increased crop productivity.

Keywords: agriculture, casual inference, machine learning, recommendation system

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Authors: Shael Brown, Reza Farivar

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Persistence diagrams capture valuable topological features of datasets that other methods cannot uncover. Still, their adoption in data pipelines has been limited due to the lack of publicly available tools in R (and python) for analyzing groups of them with machine learning and statistical inference. In an easy-to-use and scalable R package called TDApplied, we implement several applied analysis methods tailored to groups of persistence diagrams. The two main contributions of our package are comprehensiveness (most functions do not have implementations elsewhere) and speed (shown through benchmarking against other R packages). We demonstrate applications of the tools on simulated data to illustrate how easily practical analyses of any dataset can be enhanced with topological information.

Keywords: machine learning, persistence diagrams, R, statistical inference

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Authors: Orhun Aydin, Mark V. Janikas, Rodrigo Alves, Renato Assuncao

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In this paper, a tree-based perturbation methodology for regionalization inference is presented. Regionalization is a constrained optimization problem that aims to create groups with similar attributes while satisfying spatial contiguity constraints. Similar to any constrained optimization problem, the spatial constraint may hinder convergence to some global minima, resulting in spatially contiguous members of a group with dissimilar attributes. This paper presents a general methodology for rigorously perturbing spatial constraints through the use of random spanning trees. The general framework presented can be used to quantify the effect of the spatial constraints in the overall regionalization result. We compare several types of stochastic spanning trees used in inference problems such as fuzzy regionalization and determining the number of regions. Performance of stochastic spanning trees is juxtaposed against the traditional permutation-based hypothesis testing frequently used in spatial statistics. Inference results for fuzzy regionalization and determining the number of regions is presented on the Local Area Personal Incomes for Texas Counties provided by the Bureau of Economic Analysis.

Keywords: regionalization, constrained clustering, probabilistic inference, fuzzy clustering

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Authors: Azeddine El-Hassouny, Chandrashekhar Meshram, Geraldin Nanfack

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In recent years, learning from data with noisy labels (Label Noise) has been a major concern in supervised learning. This problem has become even more worrying in Deep Learning, where the generalization capabilities have been questioned lately. Indeed, deep learning requires a large amount of data that is generally collected by search engines, which frequently return data with unreliable labels. In this paper, we investigate the Label Noise in Deep Learning using variational inference. Our contributions are : (1) exploiting Label Noise concept where the true labels are learnt using reparameterization variational inference, while observed labels are learnt discriminatively. (2) the noise transition matrix is learnt during the training without any particular process, neither heuristic nor preliminary phases. The theoretical results shows how true label distribution can be learned by variational inference in any discriminate neural network, and the effectiveness of our approach is proved in several target datasets, such as MNIST and CIFAR32.

Keywords: label noise, deep learning, discrete latent variable, variational inference, MNIST, CIFAR32

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Authors: Eva L. Sanjuán, Jacinto Martín, M. Isabel Parra, Mario M. Pizarro

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In Extreme Value Theory, inference estimation for the parameters of the distribution is made employing a small part of the observation values. When block maxima values are taken, many data are discarded. We developed a new Bayesian inference model to seize all the information provided by the data, introducing informative priors and using the relations between baseline and limit parameters. Firstly, we studied the accuracy of the new model for three baseline distributions that lead to a Gumbel extreme distribution: Exponential, Normal and Gumbel. Secondly, we considered mixtures of Normal variables, to simulate practical situations when data do not adjust to pure distributions, because of perturbations (noise).

Keywords: bayesian inference, extreme value theory, Gumbel distribution, highly informative prior

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Authors: J. Marie Hicks

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The art of the poetic conjoins meaning and symbolism with imagery and rhythm. Perhaps the reader might read this opening sentence as 'The art of the poetic combines meaning and symbolism with imagery and rhythm,' which holds a similar message, but is not quite the same. The reader understands that these factors are combined in this literary form, but to gain a sense of the conjoining of these factors, the reader is forced to consider that these aspects of poetry are not simply combined, but actually adjoin, abut, skirt, or touch in the poetic form. This alternative word choice is an example of substitutional inference. Poetry is, ostensibly, a literary form where language is used precisely or creatively to evoke specific images or emotions for the reader. Often, the reader can predict a coming rhyme or descriptive word choice in a poem, based on previous rhyming pattern or earlier imagery in the poem. However, there are instances when the poet uses an unexpected word choice to create multiple meanings and connections. In these cases, the reader is presented with an unusual phrase or image, requiring that they think about what that image is meant to suggest, and their mind also suggests the word they expected, creating a second, overlying image or meaning. This is what is meant by the term 'substitutional inference.' This is different than simply using a double entendre, a word or phrase that has two meanings, often one complementary and the other disparaging, or one that is innocuous and the other suggestive. In substitutional inference, the poet utilizes an unanticipated word that is either visually or phonetically similar to the expected word, provoking the reader to work to understand the poetic phrase as written, while unconsciously incorporating the meaning of the line as anticipated. In other words, by virtue of a word substitution, an inference of the logical word choice is imparted to the reader, while they are seeking to rationalize the word that was actually used. There is a substitutional inference of meaning created by the alternate word choice. For example, Louise Bogan, 4th Poet Laureate of the United States, used substitutional inference in the form of homonyms, malapropisms, and other unusual word choices in a number of her poems, lending depth and greater complexity, while actively engaging her readers intellectually with her poetry. Substitutional inference not only adds complexity to the potential interpretations of Bogan’s poetry, as well as the poetry of others, but provided a method for writers to infuse additional meanings into their work, thus expressing more information in a compact format. Additionally, this nuancing enriches the poetic experience for the reader, who can enjoy the poem superficially as written, or on a deeper level exploring gradations of meaning.

Keywords: poetic inference, poetic word play, substitutional inference, word substitution

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