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Journal articles on the topic 'Automatic reasoning'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Apperly, Ian A., Kevin J. Riggs, Andrew Simpson, Claudia Chiavarino, and Dana Samson. "Is Belief Reasoning Automatic?" Psychological Science 17, no. 10 (October 2006): 841–44. http://dx.doi.org/10.1111/j.1467-9280.2006.01791.x.

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2

邱, 德钧. "Automatic Logic Reasoning in Artificial Intelligence." Artificial Intelligence and Robotics Research 08, no. 01 (2019): 7–16. http://dx.doi.org/10.12677/airr.2019.81002.

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3

Ferles, Kostas, Jacob Van Geffen, Isil Dillig, and Yannis Smaragdakis. "Symbolic Reasoning for Automatic Signal Placement." ACM SIGOPS Operating Systems Review 54, no. 1 (August 31, 2020): 64–76. http://dx.doi.org/10.1145/3421473.3421482.

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Arendasy, Martin, Markus Sommer, Georg Gittler, and Andreas Hergovich. "Automatic Generation of Quantitative Reasoning Items." Journal of Individual Differences 27, no. 1 (January 2006): 2–14. http://dx.doi.org/10.1027/1614-0001.27.1.2.

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This paper deals with three studies on the computer-based, automatic generation of algebra word problems. The cognitive psychology based generative/quality control frameworks of the item generator are presented. In Study I the quality control framework is empirically tested using a first set of automatically generated items. Study II replicates the findings of Study I using a larger set of automatically generated algebra word problems. Study III deals with the generative framework of the item generator by testing construct validity aspects of the item generator produced items. Using nine Rasch-homogeneous subscales of the new intelligence structure battery (INSBAT, Hornke et al., 2004 ), a hierarchical confirmatory factor analysis is reported, which provides first evidence of convergent as well as divergent validity of the automatically generated items. The end of the paper discusses possible advantages of automatic item generation in general ranging from test security issues and the possibility of a more precise psychological assessment to mass testing and economical questions of test construction.

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SHETH, AMIT P., SUNIT K. GALA, and SHAMKANT B. NAVATHE. "ON AUTOMATIC REASONING FOR SCHEMA INTEGRATION." International Journal of Cooperative Information Systems 02, no. 01 (March 1993): 23–50. http://dx.doi.org/10.1142/s0218215793000034.

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Success in database schema integration depends on the ability to capture real world semantics of the schema objects, and to reason about the semantics. Earlier schema integration approaches mainly rely on heuristics and human reasoning. In this paper, we discuss an approach to automate a significant part of the schema integration process. Our approach consists of three phases. An attribute hierarchy is generated in the first phase. This involves identifying relationships (equality, disjointness and inclusion) among attributes. We discuss a strategy based on user-specified semantic clustering. In the second phase, a classification algorithm based on the semantics of class subsumption is applied to the class definitions and the attribute hierarchy to automatically generate a class taxonomy. This class taxonomy represents a partially integrated schema. In the third phase, the user may employ a set of well-defined comparison operators in conjunction with a set of restructuring operators, to further modify the schema. These operators as well as the automatic reasoning during the second phase are based on subsumption. The formal semantics and automatic reasoning utilized in the second phase is based on a terminological logic as adapted in the CANDIDE data model. Classes are completely defined in terms of attributes and constraints. Our observation is that the inability to completely define attributes and thus completely capture their real world semantics imposes a fundamental limitation on the possibility of automatically reasoning about attribute definitions. This necessitates human reasoning during the first phase of the integration approach.

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Ferles, Kostas, Jacob Van Geffen, Isil Dillig, and Yannis Smaragdakis. "Symbolic reasoning for automatic signal placement." ACM SIGPLAN Notices 53, no. 4 (December 2, 2018): 120–34. http://dx.doi.org/10.1145/3296979.3192395.

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Oswald, Margit E., and Ingrid Stucki. "Automatic Judgment and Reasoning About Punishment." Social Justice Research 23, no. 4 (December 2010): 290–307. http://dx.doi.org/10.1007/s11211-010-0120-5.

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Shpitalni, M., and H. Lipson. "Automatic Reasoning for Design under Geometrical Constraints." CIRP Annals 46, no. 1 (1997): 85–88. http://dx.doi.org/10.1016/s0007-8506(07)60781-1.

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Abánades, Miguel, Francisco Botana, Zoltán Kovács, Tomás Recio, and Csilla Sólyom-Gecse. "Development of automatic reasoning tools in GeoGebra." ACM Communications in Computer Algebra 50, no. 3 (November 4, 2016): 85–88. http://dx.doi.org/10.1145/3015306.3015309.

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10

Shen, Tzung-Sz, Jianbing Huang, and Chia-Hsiang Menq. "Multiple-Sensor Planning and Information Integration for Automatic Coordinate Metrology." Journal of Computing and Information Science in Engineering 1, no. 2 (May 1, 2001): 167–79. http://dx.doi.org/10.1115/1.1385827.

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Multiple-sensor integration of vision and touch probe sensors has been shown to be a feasible approach for rapid and high-precision coordinate acquisition [Shen, T. S., Huang, J., and Meng, C. H., 2000, “Multiple-sensor integration for rapid and high-precision coordinate metrology,” IEEE/ASME Trans. Mechatron. 5, pp. 110–121]. However, the automation of coordinate measurements is still hindered by unknown surface areas that cannot be digitized using the vision system due to occlusions. It is identified that the estimation and reasoning of unknown surface areas, and automatic sensor planning using multiple sensors are two key issues. In order to advance multiple-sensor integration technologies toward a fully automatic and agile coordinate metrology, information integration algorithms for estimating and reasoning unknown surface areas, and an automatic multiple-sensor planning environment are developed in this paper. Experimental and simulation results are also demonstrated.

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Schulz, Claudia, Christian M. Meyer, and Iryna Gurevych. "Challenges in the Automatic Analysis of Students’ Diagnostic Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6974–81. http://dx.doi.org/10.1609/aaai.v33i01.33016974.

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Diagnostic reasoning is a key component of many professions. To improve students’ diagnostic reasoning skills, educational psychologists analyse and give feedback on epistemic activities used by these students while diagnosing, in particular, hypothesis generation, evidence generation, evidence evaluation, and drawing conclusions. However, this manual analysis is highly time-consuming. We aim to enable the large-scale adoption of diagnostic reasoning analysis and feedback by automating the epistemic activity identification. We create the first corpus for this task, comprising diagnostic reasoning selfexplanations of students from two domains annotated with epistemic activities. Based on insights from the corpus creation and the task’s characteristics, we discuss three challenges for the automatic identification of epistemic activities using AI methods: the correct identification of epistemic activity spans, the reliable distinction of similar epistemic activities, and the detection of overlapping epistemic activities. We propose a separate performance metric for each challenge and thus provide an evaluation framework for future research. Indeed, our evaluation of various state-of-the-art recurrent neural network architectures reveals that current techniques fail to address some of these challenges.

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Álvez, Javier, Montserrat Hermo, Paqui Lucio, and German Rigau. "Automatic white-box testing of first-order logic ontologies." Journal of Logic and Computation 29, no. 5 (February 26, 2019): 723–51. http://dx.doi.org/10.1093/logcom/exz001.

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AbstractFormal ontologies are axiomatizations in a logic-based formalism. The development of formal ontologies is generating considerable research on the use of automated reasoning techniques and tools that help in ontology engineering. One of the main aims is to refine and to improve axiomatizations for enabling automated reasoning tools to efficiently infer reliable information. Defects in the axiomatization cannot only cause wrong inferences, but can also hinder the inference of expected information, either by increasing the computational cost of or even preventing the inference. In this paper, we introduce a novel, fully automatic white-box testing framework for first-order logic (FOL) ontologies. Our methodology is based on the detection of inference-based redundancies in the given axiomatization. The application of the proposed testing method is fully automatic since (i) the automated generation of tests is guided only by the syntax of axioms and (ii) the evaluation of tests is performed by automated theorem provers (ATPs). Our proposal enables the detection of defects and serves to certify the grade of suitability—for reasoning purposes—of every axiom. We formally define the set of tests that are (automatically) generated from any axiom and prove that every test is logically related to redundancies in the axiom from which the test has been generated. We have implemented our method and used this implementation to automatically detect several non-trivial defects that were hidden in various FOL ontologies. Throughout the paper we provide illustrative examples of these defects, explain how they were found and how each proof—given by an ATP—provides useful hints on the nature of each defect. Additionally, by correcting all the detected defects, we have obtained an improved version of one of the tested ontologies: Adimen-SUMO.

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FRUCCI, MARIA, PETRA PERNER, and GABRIELLA SANNITI DI BAJA. "CASE-BASED-REASONING FOR IMAGE SEGMENTATION." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 05 (August 2008): 829–42. http://dx.doi.org/10.1142/s0218001408006491.

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This paper proposes to use case-based-reasoning for grey-level image segmentation. Different approaches to image segmentation have been proposed in the literature. The selection of the segmentation approach and the assignment of the values to the parameters involved in the selected algorithm depend on image domain and on the specific application. Case-based-reasoning seems a promising way to make the above selection automatic. In this paper, we describe the results of a preliminary study done in this respect. In particular, we refer to the automatic selection of the values of the parameters for a new watershed image segmentation algorithm.

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14

IRELAND, ANDREW, and ALAN BUNDY. "Automatic verification of functions with accumulating parameters." Journal of Functional Programming 9, no. 2 (March 1999): 225–45. http://dx.doi.org/10.1017/s0956796899003408.

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Proof by mathematical induction plays a crucial role in reasoning about functional programs. A generalization step often holds the key to discovering an inductive proof. We present a generalization technique which is particularly applicable when reasoning about functional programs involving accumulating parameters. We provide empirical evidence for the success of our technique and show how it is contributing to the ongoing development of a parallelizing compiler for Standard ML.

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15

Luckie, Douglas, Scott H. Harrison, and Diane Ebert-May. "Model-based reasoning: using visual tools to reveal student learning." Advances in Physiology Education 35, no. 1 (March 2011): 59–67. http://dx.doi.org/10.1152/advan.00016.2010.

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Using visual models is common in science and should become more common in classrooms. Our research group has developed and completed studies on the use of a visual modeling tool, the Concept Connector. This modeling tool consists of an online concept mapping Java applet that has automatic scoring functions we refer to as Robograder. The Concept Connector enables students in large introductory science courses to visualize their thinking through online model building. The Concept Connector's flexible scoring system, based on tested grading schemes as well as instructor input, has enabled >1,000 physiology students to build maps of their ideas about plant and animal physiology with the guidance of automatic and immediate online scoring of homework. Criterion concept maps developed by instructors in this project contain numerous expert-generated or “correct” propositions connecting two concept words together with a linking phrase. In this study, holistic algorithms were used to test automated methods of scoring concept maps that might work as well as a human grader.

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Stavy, Ruth, Reuven Babai, and Arava Y. Kallai. "Proportional Reasoning." Zeitschrift für Psychologie 224, no. 4 (October 2016): 266–76. http://dx.doi.org/10.1027/2151-2604/a000262.

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Abstract. Comparison of ratios is difficult for children and adults. We studied the role of salience and congruity in comparison of ratios using reaction time and functional magnetic resonance imaging (fMRI). Participants were asked to decide which of two mixtures of red and white paint drops (presented in Arabic numerals) was darker. In congruent trials the mixture with more red drops was darker and in incongruent trials it was lighter. Half of the trials were red salience (more red than white drops in both mixtures) and half of them were white salience. Interaction between congruity and salience was observed. Behaviorally, accuracy was higher and reaction time of correct responses (RTC) was shorter in congruent red salience and incongruent white salience conditions. For these conditions higher activation in a fronto-parietal numerical network was observed in fMRI. These findings suggest that automatic processing of natural numbers supports or suppresses the comparison of ratios as a function of congruity and salience.

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17

Tang, Liang, Wei-Xin Xie, and Jian-Jun Huang. "AUTOMATIC MULTILEVEL IMAGE SEGMENTATION BASED ON FUZZY REASONING." Image Analysis & Stereology 23, no. 1 (May 3, 2011): 23. http://dx.doi.org/10.5566/ias.v23.p23-31.

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An automatic multilevel image segmentation method based on sup-star fuzzy reasoning (SSFR) is presented. Using the well-known sup-star fuzzy reasoning technique, the proposed algorithm combines the global statistical information implied in the histogram with the local information represented by the fuzzy sets of gray-levels, and aggregates all the gray-levels into several classes characterized by the local maximum values of the histogram. The presented method has the merits of determining the number of the segmentation classes automatically, and avoiding to calculating thresholds of segmentation. Emulating and real image segmentation experiments demonstrate that the SSFR is effective.

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18

Liang, Kai Jian, Lin Feng Bai, and Xi Long Qu. "Research on Indeterminacy Causal Inductive Automatic Reasoning Mechanism." Applied Mechanics and Materials 34-35 (October 2010): 1625–29. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.1625.

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The generalized inductive logic causal model that can synthetically process fuzzy indeterminacy and random indeterminacy was proposed in this paper. On this basis, the new logic indeterminacy causal inductive automatic reasoning mechanism based on fuzzy state description was put forward. At the end of this paper its application on the development of intelligent controller was discussed.

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19

Akbari, A. Sheikh, and J. J. Soraghan. "Multiscale fuzzy reasoning (MFR) for automatic object extraction." Pattern Recognition Letters 26, no. 1 (January 2005): 77–81. http://dx.doi.org/10.1016/j.patrec.2004.09.002.

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20

Robertson, Neil M., and Ian D. Reid. "Automatic Reasoning about Causal Events in Surveillance Video." EURASIP Journal on Image and Video Processing 2011 (2011): 1–19. http://dx.doi.org/10.1155/2011/530325.

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21

Oskarsson, Magnus, and Kalle Åström. "Automatic geometric reasoning in structure and motion estimation." Pattern Recognition Letters 21, no. 13-14 (December 2000): 1105–13. http://dx.doi.org/10.1016/s0167-8655(00)00068-4.

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22

Paterno', Fabio. "Formal reasoning about dialogue properties with automatic support." Interacting with Computers 9, no. 2 (November 1997): 173–96. http://dx.doi.org/10.1016/s0953-5438(97)00015-5.

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23

LI, ZHIYUAN, and JUNJIE GU. "A HIERARCHICAL REASONING SYSTEM FOR AUTOMATIC PROGRAM PARALLELIZATION." International Journal on Artificial Intelligence Tools 09, no. 03 (September 2000): 417–35. http://dx.doi.org/10.1142/s0218213000000264.

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Automatic parallelization of large and complex program regions requires the compiler to reason about complex semantic information. This paper describes a hierarchical reasoning system implemented within the Panorama parallelizing compiler. This reasoning system intelligently and efficiently extracts useful semantic information in the form of first-order predicates from program statements. It uses a hierarchical structure to represent the predicates, which enables fast determination of their truth in common cases. A hierarchical value graph facilitates rule-based, demand-driven retrieval of useful predicates.

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24

Yorsh, Greta, Alexey Skidanov, Thomas Reps, and Mooly Sagiv. "Automatic Assume/Guarantee Reasoning for Heap-Manipulating Programs." Electronic Notes in Theoretical Computer Science 131 (May 2005): 125–38. http://dx.doi.org/10.1016/j.entcs.2005.01.028.

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NEUFELD, ERIC. "Directions in uncertainty reasoning." Knowledge Engineering Review 12, no. 4 (December 1997): 413–15. http://dx.doi.org/10.1017/s0269888997004037.

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‘Uncertainty reasoning” refers in a general way to problems discussed by that subset of the AI community interested in representing and reasoning with knowledge that cannot be expressed as certainties. The range of problems discussed runs the gamut from fundamental philosophical inquiry into the nature of uncertainty and how (if at all) it can be measured and modelled, to practical performance issues arising from the (automatic) construction of real-world models and making inferences from such models.

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KAPPES, ANDREAS, and TALI SHAROT. "The automatic nature of motivated belief updating." Behavioural Public Policy 3, no. 1 (March 1, 2018): 87–103. http://dx.doi.org/10.1017/bpp.2017.11.

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AbstractPeople's risk estimates often do not align with the evidence available to them. In particular, people tend to discount bad news (such as evidence suggesting their risk of being involved in a car accident is higher than they thought) as compared to good news (evidence suggesting it is lower) – this is known as the belief update bias. It has been assumed that individuals use motivated reasoning to rationalise away unwanted evidence (e.g., “I am a safe driver, thus these statistics do not apply to me”). However, whether reasoning is required to discount bad news has not been tested directly. Here, we restrict cognitive resources using a cognitive load (Experiment 1) and a time restriction manipulation (Experiment 3) and find that while these manipulations diminish learning in general, they do not diminish the bias. Furthermore, we also show that the relative neglect of bad news happens the moment new evidence is presented, not when participants are subsequently prompted to state their belief (Experiment 2). Our findings suggest that reasoning is not required for bad news to be discounted as compared to good news.

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TOKUMARU, Masataka, Shinichi FUMURO, Noriaki MURANAKA, and Shigeru IMANISHI. "Automatic Harmonization by Considering of Tonality Using Fuzzy Reasoning." IEEJ Transactions on Electronics, Information and Systems 117, no. 4 (1997): 424–31. http://dx.doi.org/10.1541/ieejeiss1987.117.4_424.

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Zhu, David, and Jean-Claude Latombe. "Mechanization of spatial reasoning for automatic pipe layout design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 5, no. 1 (February 1991): 1–20. http://dx.doi.org/10.1017/s089006040000250x.

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Artificial Intelligence has been very active in developing high-level symbolic reasoning paradigms that have resulted in practical expert systems. However, with a few exceptions, it has paid little attention to the automation of spatial reasoning. On the other hand, spatial reasoning has attracted the interest of several researchers in Robotics. One of the important problems that have been investigated is motion planning, and very significant results have been obtained. This paper describes an implemented system for designing pipe layouts automatically using motion planning techniques. It introduces a new approach to pipe layout design automation in which pipe routes are treated as trajectories left behind by rigid objects (‘robots’). We have implemented this approach in a basic Pipe Router that is described in detail in this paper. We have extended this router in order to make it capable of treating a variety of other constraints which are typical of practical pipe layout design problems. These constraints relate to the process carried out in the pipes, to the design of their mechanical support, and to the constructability and the ease of operation and maintenance of the designed pipe systems.

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29

Garrell i Guiu, J. M., E. Golobardes i Ribé, E. Bernadó i Mansilla, and X. Llorà i Fàbrega. "Automatic diagnosis with genetic algorithms and case-based reasoning." Artificial Intelligence in Engineering 13, no. 4 (October 1999): 367–72. http://dx.doi.org/10.1016/s0954-1810(99)00009-6.

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Economides, Marcos, Zeb Kurth-Nelson, Annika Lübbert, Marc Guitart-Masip, and Raymond J. Dolan. "Model-Based Reasoning in Humans Becomes Automatic with Training." PLOS Computational Biology 11, no. 9 (September 17, 2015): e1004463. http://dx.doi.org/10.1371/journal.pcbi.1004463.

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31

Myznikov, P. V. "Case-Based Reasoning in Automatic Analysis of News Texts." Vestnik NSU. Series: Information Technologies 15, no. 2 (2017): 59–65. http://dx.doi.org/10.25205/1818-7900-2017-15-2-59-65.

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Choi, Ikkyu, Seungmin Rho, and Minkoo Kim. "Semi-automatic construction of domain ontology for agent reasoning." Personal and Ubiquitous Computing 17, no. 8 (September 25, 2012): 1721–29. http://dx.doi.org/10.1007/s00779-012-0606-2.

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33

Duží, Marie, and Aleš Horák. "Hyperintensional Reasoning Based on Natural Language Knowledge Base." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, no. 03 (May 21, 2020): 443–68. http://dx.doi.org/10.1142/s021848852050018x.

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The success of automated reasoning techniques over large natural-language texts heavily relies on a fine-grained analysis of natural language assumptions. While there is a common agreement that the analysis should be hyperintensional, most of the automatic reasoning systems are still based on an intensional logic, at the best. In this paper, we introduce the system of reasoning based on a fine-grained, hyperintensional analysis. To this end we apply Tichy’s Transparent Intensional Logic (TIL) with its procedural semantics. TIL is a higher-order, hyperintensional logic of partial functions, in particular apt for a fine-grained natural-language analysis. Within TIL we recognise three kinds of context, namely extensional, intensional and hyperintensional, in which a particular natural-language term, or rather its meaning, can occur. Having defined the three kinds of context and implemented an algorithm of context recognition, we are in a position to develop and implement an extensional logic of hyperintensions with the inference machine that should neither over-infer nor under-infer.

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Neys, Wim De. "Dual Processing in Reasoning." Psychological Science 17, no. 5 (May 2006): 428–33. http://dx.doi.org/10.1111/j.1467-9280.2006.01723.x.

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Human reasoning has been characterized as an interplay between an automatic belief-based system and a demanding logic-based reasoning system. The present study tested a fundamental claim about the nature of individual differences in reasoning and the processing demands of both systems. Participants varying in working memory capacity performed a reasoning task while their executive resources were burdened with a secondary task. Results were consistent with the dual-process claim: The executive burden hampered correct reasoning when the believability of a conclusion conflicted with its logical validity, but not when beliefs cued the correct response. However, although participants with high working memory spans performed better than those with lower spans in cases of a conflict, all reasoners showed similar effects of load. The findings support the idea that there are two reasoning systems with differential processing demands, but constitute evidence against qualitative individual differences in the human reasoning machinery.

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Zhang, Wei Hua, Jin Sha Yuan, Ke Zhang, and Zhong Li. "A Reasoning Method of Transformer Fault Causes Based on Fuzzy Petri Net." Applied Mechanics and Materials 631-632 (September 2014): 537–42. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.537.

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For a correct judgment on the fault cause of transformers with the rich knowledge in various criteria and guideline, a method of automatic reasoning for the fault cause through fuzzy Petri net has been put forward in this paper. In this method, the knowledge in criteria and guidelines is firstly presented in the form of IF-THEN structure through the production rule, based on which the fuzzy Petri net model of fault cause is established then; and lastly possibility of each fault cause can be worked out through matrix iteration, which means the automatic reasoning is completed. Based on fuzzy Petri net imaging, this method makes the reasoning clearer and the result be got faster. The example calculation verifies that the method is correct and feasible in practical projects.

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Leeuwenberg, Artuur, and Marie-Francine Moens. "A Survey on Temporal Reasoning for Temporal Information Extraction from Text." Journal of Artificial Intelligence Research 66 (September 30, 2019): 341–80. http://dx.doi.org/10.1613/jair.1.11727.

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Time is deeply woven into how people perceive, and communicate about the world. Almost unconsciously, we provide our language utterances with temporal cues, like verb tenses, and we can hardly produce sentences without such cues. Extracting temporal cues from text, and constructing a global temporal view about the order of described events is a major challenge of automatic natural language understanding. Temporal reasoning, the process of combining different temporal cues into a coherent temporal view, plays a central role in temporal information extraction. This article presents a comprehensive survey of the research from the past decades on temporal reasoning for automatic temporal information extraction from text, providing a case study on how combining symbolic reasoning with machine learning-based information extraction systems can improve performance. It gives a clear overview of the used methodologies for temporal reasoning, and explains how temporal reasoning can be, and has been successfully integrated into temporal information extraction systems. Based on the distillation of existing work, this survey also suggests currently unexplored research areas. We argue that the level of temporal reasoning that current systems use is still incomplete for the full task of temporal information extraction, and that a deeper understanding of how the various types of temporal information can be integrated into temporal reasoning is required to drive future research in this area.

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Flores, Amanda, Pedro L. Cobos, and York Hagmayer. "The Diagnosis of Mental Disorders Is Influenced by Automatic Causal Reasoning." Clinical Psychological Science 6, no. 2 (June 21, 2017): 177–88. http://dx.doi.org/10.1177/2167702617709560.

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Causal knowledge has been shown to affect diagnostic decisions. It is unclear, however, how causal knowledge affects diagnosis. We hypothesized that it influences intuitive reasoning processes. More precisely, we speculated that people automatically assess the coherence between observed symptoms and an assumed causal model of a disorder, which in turn affects diagnostic classification. Intuitive causal reasoning was investigated in an experimental study. Participants were asked to read clinical reports before deciding on a diagnosis. Intuitive processing was studied by analyzing reading times. It turned out that reading times were slower when causally expected consequences of present symptoms were missing or effects of absent causes were present. This causal incoherence effect was predictive of participants’ later explicit diagnostic judgments. These and related findings suggest that diagnostic judgments rely on automatic reasoning processes based on the computation of causal coherence. Potential implications of these results for the training of clinicians are discussed.

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38

LI, Tao, Bo ZHANG, and Qian-cai ZHANG. "Research of geometry information reduction in computer automatic reasoning system." Journal of Computer Applications 30, no. 3 (April 7, 2010): 842–45. http://dx.doi.org/10.3724/sp.j.1087.2010.00842.

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39

Liang, Ci, Mohamed Ghazel, Olivier Cazier, and Laurent Bouillaut. "Advanced model-based risk reasoning on automatic railway level crossings." Safety Science 124 (April 2020): 104592. http://dx.doi.org/10.1016/j.ssci.2019.104592.

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40

Eguchi, Kazuhiro, and Sadayuki Murashima. "Automatic adjustment for a dielectric antenna duplexer using fuzzy reasoning." Electronics and Communications in Japan (Part II: Electronics) 81, no. 1 (January 1998): 32–41. http://dx.doi.org/10.1002/(sici)1520-6432(199801)81:1<32::aid-ecjb4>3.0.co;2-a.

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Liao, T. W. "Fuzzy reasoning based automatic inspection of radiographic welds: weld recognition." Journal of Intelligent Manufacturing 15, no. 1 (February 2004): 69–85. http://dx.doi.org/10.1023/b:jims.0000010076.56537.07.

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Rahwan, Iyad, Bita Banihashemi, Chris Reed, Douglas Walton, and Sherief Abdallah. "Representing and classifying arguments on the Semantic Web." Knowledge Engineering Review 26, no. 4 (December 2011): 487–511. http://dx.doi.org/10.1017/s0269888911000191.

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AbstractUntil recently, little work has been dedicated to the representation and interchange of informal, semi-structured arguments of the type found in natural language prose and dialogue. To redress this, the research community recently initiated work towards an Argument Interchange Format (AIF). The AIF aims to facilitate the exchange of semi-structured arguments among different argument analysis and argumentation-support tools. In this paper, we present a Description Logic ontology for annotating arguments, based on a new reification of the AIF and founded in Walton's theory of argumentation schemes. We demonstrate how this ontology enables a new kind of automated reasoning over argument structures, which complements classical reasoning about argument acceptability. In particular, Web Ontology Language reasoning enables significantly enhanced querying of arguments through automatic scheme classifications, instance classification, inference of indirect support in chained argument structures, and inference of critical questions. We present the implementation of a pilot Web-based system for authoring and querying argument structures using the proposed ontology.

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Li, Yan, Jianliang Wu, Lin Zhu, and Kikuo Tachibana. "ROAD NETWORK EXTRACTION FROM DSM BY MATHEMATICAL MORPHOLOGY AND REASONING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 9, 2016): 715–20. http://dx.doi.org/10.5194/isprsarchives-xli-b2-715-2016.

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The objective of this research is the automatic extraction of the road network in a scene of the urban area from a high resolution digital surface model (DSM). Automatic road extraction and modeling from remote sensed data has been studied for more than one decade. The methods vary greatly due to the differences of data types, regions, resolutions et al. An advanced automatic road network extraction scheme is proposed to address the issues of tedium steps on segmentation, recognition and grouping. It is on the basis of a geometric road model which describes a multiple-level structure. The 0-dimension element is intersection. The 1-dimension elements are central line and side. The 2-dimension element is plane, which is generated from the 1-dimension elements. The key feature of the presented approach is the cross validation for the three road elements which goes through the entire procedure of their extraction. The advantage of our model and method is that linear elements of the road can be derived directly, without any complex, non-robust connection hypothesis. An example of Japanese scene is presented to display the procedure and the performance of the approach.

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Li, Yan, Jianliang Wu, Lin Zhu, and Kikuo Tachibana. "ROAD NETWORK EXTRACTION FROM DSM BY MATHEMATICAL MORPHOLOGY AND REASONING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 9, 2016): 715–20. http://dx.doi.org/10.5194/isprs-archives-xli-b2-715-2016.

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The objective of this research is the automatic extraction of the road network in a scene of the urban area from a high resolution digital surface model (DSM). Automatic road extraction and modeling from remote sensed data has been studied for more than one decade. The methods vary greatly due to the differences of data types, regions, resolutions et al. An advanced automatic road network extraction scheme is proposed to address the issues of tedium steps on segmentation, recognition and grouping. It is on the basis of a geometric road model which describes a multiple-level structure. The 0-dimension element is intersection. The 1-dimension elements are central line and side. The 2-dimension element is plane, which is generated from the 1-dimension elements. The key feature of the presented approach is the cross validation for the three road elements which goes through the entire procedure of their extraction. The advantage of our model and method is that linear elements of the road can be derived directly, without any complex, non-robust connection hypothesis. An example of Japanese scene is presented to display the procedure and the performance of the approach.

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Chou, Yon-Chun. "A theoretical framework for automatic layout of machining fixtures." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 6, no. 2 (May 1992): 111–21. http://dx.doi.org/10.1017/s0890060400002997.

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Fixtures are used in almost all manufacturing operations. They take on many different forms, ranging from simple mechanical vises to modular fixtures to specialized fixtures. For cylindrical parts or simple prismatic parts, automatic design of fixtures is feasible. However, for parts of irregular shape such as castings or forged parts, the automation of fixture design is a complex problem. In this paper we address the problem of fixture layout for parts of complicated shape. Issues related to automatic design such as completeness, soundness, and computing efficiency are first discussed. A theoretical framework based on algebraic formulations is developed which models human reasoning employed for workpiece restraint. These formulations will ensure the completeness and soundness of the design methodology. They also provide a mechanism for constraint propagation in the design space. Finally, the generality of the framework is discussed.

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Xu, Shi Wei, Huan Guo Zhang, Zheng Dai, Xin Fa Dai, and Jing Dong Chen. "Modeling and Reasoning about States in Late Launch Based on Horn Clauses." Advanced Materials Research 915-916 (April 2014): 1350–56. http://dx.doi.org/10.4028/www.scientific.net/amr.915-916.1350.

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Late Launch, which is a kind of dynamic measurement technology proposed by both Intel and AMD, offers isolated execution environment for codes needed to be protected. However, since the specifications and documents of Late Launch have hundreds of pages, they are too long and complicated to be fully covered and analyzed. A model based on Horn clauses is presented to solve the problem that there is a lack of realistic models and of automated tools for the verification of security protocols based on Late Launch. A running example is taken to show the execution details of Late Launch. Based on the example, secrecy properties of Late Launch are verified. Whats more, the automatic theorem proving tool ProVerif is used to make the verification more fast and accurate.

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Yamamoto, Hidehiko, and Takayoshi Yamada. "Intelligent AGV Control of Autonomous Decentralized FMS by Oblivion and Memory." Key Engineering Materials 447-448 (September 2010): 326–30. http://dx.doi.org/10.4028/www.scientific.net/kem.447-448.326.

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This paper describes a method to control Autonomous decentralized Flexible Manufacturing System (AD-FMS) by using a memory to determine a priority ranking for competing hypotheses. The aim is to increase the reasoning efficiency of a system the author calls reasoning to anticipate the future (RAF) which controls automatic guided vehicles (AGVs) in AD-FMSs. The system includes memory data of past production conditions and AGV actions. The system was applied to an AD-FMS that was constructed on a computer. The results showed that, compared with conventional reasoning, this reasoning system reduced the number of hypothesis replacements until a true hypothesis was reached.

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Donati, Camillo, Andrea Guazzini, Giorgio Gronchi, and Andrea Smorti. "About Linda Again: How Narratives and Group Reasoning Can Influence Conjunction Fallacy." Future Internet 11, no. 10 (October 8, 2019): 210. http://dx.doi.org/10.3390/fi11100210.

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Conjunction fallacy (together with other systematic reasoning errors) is usually explained in terms of the dual process theory of reasoning: Biases should be ascribed to fast and automatic processes, whereas slow and deliberative processes are responsible of producing answers that are correct with respect of normative criterion. The dual process theory is related to Bruner’s distinction between narrative and paradigmatic thought: Both modes of thought can be characterized by the two different processes of reasoning. In this paper, we explore the role of Bruner’s mode of thought manipulating also the difference between group vs individual reasoning. We observed that the narrative strategy of response induces more wrong answers. However, narrative-based strategies have higher effectiveness in the case of group reasoning. Our results suggest that narrative reasoning and group reasoning may induce violations of the conjunction rule when acceptable by the verisimilitude of the story. Five models are also presented in order to predict answer correctness and strategy of reasoning using a text analysis software.

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Liu, Ying, Zheng Hu, and Shi Gang Zhang. "A Correlation Model Based Failure Mode Analysis Method for Mechatronics Systems." Applied Mechanics and Materials 727-728 (January 2015): 637–40. http://dx.doi.org/10.4028/www.scientific.net/amm.727-728.637.

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Failure mode analysis formechatronics systems has many problems in real applications in terms of excessivedependency on experience, lack of uniform description and tedious analysis work. In order to increaseits effectiveness, an automatic failure modes analysis framework is constructedand an extended fuzzy cognitive map is used as a reasoning technique toanalysis the effect of the failures. A function-failure correlation model witha standard description is adopted to describe the relationship between failuremodes and functions, so that designers and engineers from different fields cancomprehend and communicate within the same framework. Functional flows andfailure modes are defined as basic concepts in the fuzzy cognitive map, whichprovides an easy way to carry out an automatic cause and effect reasoning.

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Mei, Hong. "Control of Automobile's Automatic Parking." Advanced Materials Research 339 (September 2011): 28–31. http://dx.doi.org/10.4028/www.scientific.net/amr.339.28.

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An automatic parking controller is proposed. Fuzzy control is taken to simulate the action of experienced driver as an alternative to conventional methods. The angle between the midline of the car and ideal path and the distance between the midpoint of the car and the ideal path are taken as the inputs of the fuzzy controller. The angle of the steering wheel is taken as the output of the fuzzy controller. A set of fuzzy logic rules are build for reasoning. With sensors installed in the car to replace people’s eyes and computer to replace people’s brain, the automatic parking system is more precise and quicker than human’s parking. At last, simulation is made and proved the validity of the proposed method.

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