Relevant bibliographies by topics / Incremental reasoning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Incremental reasoning'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Incremental reasoning"

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Liao, Lizi, Le Hong Long, Yunshan Ma, Wenqiang Lei, and Tat-Seng Chua. "Dialogue State Tracking with Incremental Reasoning." Transactions of the Association for Computational Linguistics 9 (2021): 557–69. http://dx.doi.org/10.1162/tacl_a_00384.

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Abstract Tracking dialogue states to better interpret user goals and feed downstream policy learning is a bottleneck in dialogue management. Common practice has been to treat it as a problem of classifying dialogue content into a set of pre-defined slot-value pairs, or generating values for different slots given the dialogue history. Both have limitations on considering dependencies that occur on dialogues, and are lacking of reasoning capabilities. This paper proposes to track dialogue states gradually with reasoning over dialogue turns with the help of the back-end data. Empirical results de
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CALIMERI, FRANCESCO, GIOVAMBATTISTA IANNI, FRANCESCO PACENZA, SIMONA PERRI, and JESSICA ZANGARI. "Incremental Answer Set Programming with Overgrounding." Theory and Practice of Logic Programming 19, no. 5-6 (2019): 957–73. http://dx.doi.org/10.1017/s1471068419000292.

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AbstractRepeated executions of reasoning tasks for varying inputs are necessary in many applicative settings, such as stream reasoning. In this context, we propose an incremental grounding approach for the answer set semantics. We focus on the possibility of generating incrementally larger ground logic programs equivalent to a given non-ground one; so calledovergrounded programscan be reused in combination with deliberately many different sets of inputs. Updating overgrounded programs requires a small effort, thus making the instantiation of logic programs considerably faster when grounding is
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OLIVEIRA, BRUNO C. D. S., TOM SCHRIJVERS, and WILLIAM R. COOK. "MRI: Modular reasoning about interference in incremental programming." Journal of Functional Programming 22, no. 6 (2012): 797–852. http://dx.doi.org/10.1017/s0956796812000354.

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AbstractIncremental Programming (IP) is a programming style in which new program components are defined as increments of other components. Examples of IP mechanisms include Object-oriented programming inheritance, aspect-oriented programming advice, and feature-oriented programming. A characteristic of IP mechanisms is that, while individual components can be independently defined, the composition of components makes those components become tightly coupled, sharing both control and data flows. This makes reasoning about IP mechanisms a notoriously hard problem: modular reasoning about a compon
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Tsilionis, Efthimis, Alexander Artikis, and Georgios Paliouras. "Incremental Event Calculus for Run-Time Reasoning." Journal of Artificial Intelligence Research 73 (March 29, 2022): 967–1023. http://dx.doi.org/10.1613/jair.1.12695.

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We present a system for online, incremental composite event recognition. In streaming environments, the usual case is for data to arrive with a (variable) delay from, and to be revised by, the underlying sources. We propose RTECinc, an incremental version of RTEC, a composite event recognition engine with formal, declarative semantics, that has been shown to scale to several real-world data streams. RTEC deals with delayed arrival and revision of events by computing all queries from scratch. This is often inefficient since it results in redundant computations. Instead, RTECinc deals with delay
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Tiger, Mattias, and Fredrik Heintz. "Incremental reasoning in probabilistic Signal Temporal Logic." International Journal of Approximate Reasoning 119 (April 2020): 325–52. http://dx.doi.org/10.1016/j.ijar.2020.01.009.

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Mitra, Debasis, and Gérard Ligozat. "Spatial-reasoning for Agents in Multiple Dimensions." JUCS - Journal of Universal Computer Science 8, no. (8) (2002): 774–91. https://doi.org/10.3217/jucs-008-08-0774.

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Suppose a group of mobile agents situated in some Euclidean space does not have any idea on where they are exactly located within that space. However, they do have some notion about their relative positions with respect to each other. This problem may be formulated as a multi-dimensional point-based qualitative reasoning problem with disjunctive constraints. In this article we have developed a set of incremental algorithms for finding feasible positions of a new agent relative to the other existing agents (located in 1D, 2D and the generalized d-D dimensional space for d_=1), given some qualit
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De Leng, Daniel, and Fredrik Heintz. "Approximate Stream Reasoning with Metric Temporal Logic under Uncertainty." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2760–67. http://dx.doi.org/10.1609/aaai.v33i01.33012760.

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Stream reasoning can be defined as incremental reasoning over incrementally-available information. The formula progression procedure for Metric Temporal Logic (MTL) makes use of syntactic formula rewritings to incrementally evaluate formulas against incrementally-available states. Progression however assumes complete state information, which can be problematic when not all state information is available or can be observed, such as in qualitative spatial reasoning tasks or in robotics applications. In those cases, there may be uncertainty as to which state out of a set of possible states repres
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LIN, YAN, and MAREK J. DRUZDZEL. "RELEVANCE-BASED INCREMENTAL BELIEF UPDATING IN BAYESIAN NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 02 (1999): 285–95. http://dx.doi.org/10.1142/s0218001499000161.

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Relevance reasoning in Bayesian networks can be used to improve efficiency of belief updating algorithms by identifying and pruning those parts of a network that are irrelevant for computation. Relevance reasoning is based on the graphical property of d-separation and other simple and efficient techniques, the computational complexity of which is usually negligible when compared to the complexity of belief updating in general. This paper describes a belief updating technique based on relevance reasoning that is applicable in practical systems in which observations and model revisions are inter
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LEHTONEN, TUOMO, JOHANNES P. WALLNER, and MATTI JӒRVISALO. "Harnessing Incremental Answer Set Solving for Reasoning in Assumption-Based Argumentation." Theory and Practice of Logic Programming 21, no. 6 (2021): 717–34. http://dx.doi.org/10.1017/s1471068421000296.

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AbstractAssumption-based argumentation (ABA) is a central structured argumentation formalism. As shown recently, answer set programming (ASP) enables efficiently solving NP-hard reasoning tasks of ABA in practice, in particular in the commonly studied logic programming fragment of ABA. In this work, we harness recent advances in incremental ASP solving for developing effective algorithms for reasoning tasks in the logic programming fragment of ABA that are presumably hard for the second level of the polynomial hierarchy, including skeptical reasoning under preferred semantics as well as prefer
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Furao, Shen, Akihito Sudo, and Osamu Hasegawa. "An online incremental learning pattern-based reasoning system." Neural Networks 23, no. 1 (2010): 135–43. http://dx.doi.org/10.1016/j.neunet.2009.06.002.

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Dissertations / Theses on the topic "Incremental reasoning"

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Khan, Abdus Salam Computer Science &amp Engineering Faculty of Engineering UNSW. "Incremental knowledge acquisition for case-based reasoning." Awarded by:University of New South Wales. School of Computer Science and Engineering, 2003. http://handle.unsw.edu.au/1959.4/19376.

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Case-Based Reasoning (CBR) is an appealing technique for developing intelligent systems. Besides its psycho- logical plausibility and a substantial body of research during recent years, building a good CBR system remains still a difficult task. The main problems remaining are the development of suitable case retrieval and adaptation mechanisms for CBR. The major issues are how and when to capture the necessary knowledge for both of the above aspects. As a contribution to knowledge this thesis proposes a new approach to address the experienced difficulties. The basic framework of Ripple Down Ru
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Albeladi, Rehab. "Incremental rule-based reasoning on semantic data streams." Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/419658/.

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This thesis investigates the area of semantic stream processing, in which data streams are combined with semantic reasoning techniques. We have investigated techniques for rule-based reasoning over semantic streams in which reasoning is implemented natively over streams as data flow networks, and have developed an adaptive optimisation method to cope with the changing nature of streams. The contributions of this thesis include R4, a native rule-based reasoner for RDF streams using the Rete algorithm, and a cost-based adaptive plan optimiser designed for RDF streams. We have evaluated the perfo
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Shu, I.-hsiang 1979. "Enabling fast flexible planning through incremental temporal reasoning." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/18035.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.<br>Includes bibliographical references (leaves 70-71).<br>In order for a team of autonomous agents to successfully complete its mission, the agents must be able to quickly re-plan on the fly as unforeseen events arise in the environment. This requires temporally flexible plans that allow the agent to adapt to execution uncertainties by not overcommitting on time constraints, and a continuous planner that replans at any point when the current plan fails. To achieve both of these
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Nilsson, Mikael. "Efficient Temporal Reasoning with Uncertainty." Licentiate thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119409.

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Automated Planning is an active area within Artificial Intelligence. With the help of computers we can quickly find good plans in complicated problem domains, such as planning for search and rescue after a natural disaster. When planning in realistic domains the exact duration of an action generally cannot be predicted in advance. Temporal planning therefore tends to use upper bounds on durations, with the explicit or implicit assumption that if an action happens to be executed more quickly, the plan will still succeed. However, this assumption is often false. If we finish cooking too early, t
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Chevalier, Jules. "Raisonnement incrémental sur des flux de données." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSES008/document.

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Nous proposons dans cette thèse une architecture pour le raisonnement incrémental sur des flux de triples. Afin de passer à l’échelle, elle est conçue sous la forme de modules indépendants, permettant l’exécution parallèle du raisonnement. Plusieurs instances d’une même règle peuvent être exécutées simultanément afin d’améliorer les performances. Nous avons également concentré nos efforts pour limiter la dispersion des doublons dans le système, problème récurrent du raisonnement. Pour cela, un triplestore partagé permet à chaque module de filtrer au plus tôt les doublons. La structure de notre
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Kuci, Edlira [Verfasser], Mira [Akademischer Betreuer] Mezini, Klaus [Akademischer Betreuer] Ostermann, and Sebastian [Akademischer Betreuer] Erdweg. "Co-Contextual Type Systems: Contextless Deductive Reasoning for Correct Incremental Type Checking / Edlira Kuci ; Mira Mezini, Klaus Ostermann, Sebastian Erdweg." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2020. http://d-nb.info/1205070117/34.

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Herzig, Sebastian J. I. "A Bayesian learning approach to inconsistency identification in model-based systems engineering." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53576.

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Designing and developing complex engineering systems is a collaborative effort. In Model-Based Systems Engineering (MBSE), this collaboration is supported through the use of formal, computer-interpretable models, allowing stakeholders to address concerns using well-defined modeling languages. However, because concerns cannot be separated completely, implicit relationships and dependencies among the various models describing a system are unavoidable. Given that models are typically co-evolved and only weakly integrated, inconsistencies in the agglomeration of the information and knowledge encod
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Khan, Abdus Salam. "Incremental knowledge acquisition for case-based reasoning /." 2003. http://www.library.unsw.edu.au/~thesis/adt-NUN/public/adt-NUN20040824.114652/index.html.

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TSAI, WAN-CHUN, and 蔡宛純. "The Study of the Incremental Credit Scoring Model:Combined clustering and Case-Based Reasoning Method." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/86522627049075409838.

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碩士<br>國立台北護理學院<br>資訊管理研究所<br>94<br>Credit scoring models are important data analysis technique, and have been successful applied to risk management. Financial institutions usually employed credit scoring system to decide whether or not to grant credit to consumers who apply to them. Besides, credit scoring system obviously reduced time and cost. For the sake of improving model’s accuracy, many approaches have been proposed. However, unrepresentative samples are significantly reduced the accuracy of scoring model in practical application. Therefore many studies reported the usefulness of neural
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Passaro, Valériane. "Analyse du raisonnement covariationnel favorisant le passage de la fonction à la dérivée et des situations qui en sollicitent le déploiement chez des élèves de 15 à 18 ans." Thèse, 2015. http://hdl.handle.net/1866/13509.

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Afin de mieux cerner les enjeux de la transition entre le secondaire et le postsecondaire, nous proposons un examen du passage de la notion de fonction à celle de dérivée. À la lumière de plusieurs travaux mettant en évidence des difficultés inhérentes à ce passage, et nous basant sur les recherches de Carlson et ses collègues (Carlson, 2002; Carlson, Jacobs, Coe, Larsen et Hsu, 2002; Carlson, Larsen et Jacobs, 2001; Oehrtman, Carlson et Thompson, 2008) sur le raisonnement covariationnel, nous présentons une analyse de la dynamique du développement de ce raisonnement chez des petits groupes d’
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Book chapters on the topic "Incremental reasoning"

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Giese, Martin. "Incremental Closure of Free Variable Tableaux." In Automated Reasoning. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45744-5_46.

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Lu, Jing, Xingzhi Sun, Linhao Xu, and Haofen Wang. "Incremental Reasoning over Multiple Ontologies." In Web-Age Information Management. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23535-1_13.

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Dovland, Johan, Einar Broch Johnsen, Olaf Owe, and Martin Steffen. "Incremental Reasoning for Multiple Inheritance." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00255-7_15.

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Urbain, Xavier. "Automated Incremental Termination Proofs for Hierarchically Defined Term Rewriting Systems." In Automated Reasoning. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45744-5_42.

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Soundarajan, Neelam, and Stephen Fridella. "Framework-Based Applications: From Incremental Development to Incremental Reasoning." In Software Reuse: Advances in Software Reusability. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-540-44995-9_7.

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Kremer, Gereon, Andrew Reynolds, Clark Barrett, and Cesare Tinelli. "Cooperating Techniques for Solving Nonlinear Real Arithmetic in the cvc5 SMT Solver (System Description)." In Automated Reasoning. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10769-6_7.

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AbstractThe SMT solver solves quantifier-free nonlinear real arithmetic problems by combining the cylindrical algebraic coverings method with incremental linearization in an abstraction-refinement loop. The result is a complete algebraic decision procedure that leverages efficient heuristics for refining candidate models. Furthermore, it can be used with quantifiers, integer variables, and in combination with other theories. We describe the overall framework, individual solving techniques, and a number of implementation details. We demonstrate its effectiveness with an evaluation on the SMT-LI
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Ng, Raymond T., and Xiaomei Tian. "Incremental methods for optimizing partial instantiation." In Logic Programming and Nonmonotonic Reasoning. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-59487-6_21.

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Fiorentini, Camillo, and Mauro Ferrari. "SAT-Based Proof Search in Intermediate Propositional Logics." In Automated Reasoning. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10769-6_5.

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AbstractWe present a decision procedure for intermediate logics relying on a modular extension of the SAT-based prover $$\texttt {intuitR} $$ intuitR for $$\mathrm {IPL} $$ IPL (Intuitionistic Propositional Logic). Given an intermediate logic L and a formula $$\alpha $$ α , the procedure outputs either a Kripke countermodel for $$\alpha $$ α or the instances of the characteristic axioms of L that must be added to $$\mathrm {IPL} $$ IPL in order to prove $$\alpha $$ α . The procedure exploits an incremental SAT-solver; during the computation, new clauses are learned and added to the solver.
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Soundarajan, Neelam, and Stephen Fridella. "Incremental Reasoning for Object Oriented Systems." In From Object-Orientation to Formal Methods. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39993-3_15.

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Hashemi, Saeed. "Incremental Case-Based Reasoning for Classification." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45153-6_36.

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Conference papers on the topic "Incremental reasoning"

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Tsilionis, Efthimis, Alexander Artikis, and Georgios Paliouras. "Incremental Reasoning for Fleet Management." In DEBS '19: The 13th ACM International Conference on Distributed and Event-based Systems. ACM, 2019. http://dx.doi.org/10.1145/3328905.3332521.

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Wu, QingXiang, Xi Huang, David A. Bell, Guilin Qi, and Zhihui Luo. "Incremental Knowledge Base for Uncertain Reasoning." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.117.

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Bradley, Aaron R. "Incremental, Inductive Model Checking." In 2013 20th International Symposium on Temporal Representation and Reasoning (TIME). IEEE, 2013. http://dx.doi.org/10.1109/time.2013.9.

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Tsilionis, Efthimis, Alexander Artikis, and Georgios Paliouras. "Incremental Event Calculus for Run-Time Reasoning." In DEBS '19: The 13th ACM International Conference on Distributed and Event-based Systems. ACM, 2019. http://dx.doi.org/10.1145/3328905.3329504.

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Mebrek, Wafaa, and Amel Bouzeghoub. "Multi-Context Incremental Reasoning Over Data Streams." In 2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). IEEE, 2023. http://dx.doi.org/10.1109/wi-iat59888.2023.00026.

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Letard, Vincent, Sophie Rosset, and Gabriel Illouz. "Incremental Learning from Scratch Using Analogical Reasoning." In 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2016. http://dx.doi.org/10.1109/ictai.2016.0040.

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Ravve, Elena V., Zeev Volkovich, and Gerhard-Wilhelm Weber. "Incremental Reasoning on Strongly Distributed Multi-agent Systems." In 2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2015. http://dx.doi.org/10.1109/synasc.2015.68.

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Huang, Y., L. Zheng, Brian C. Williams, L. Tang, and H. Yang. "Incremental temporal reasoning in job shop scheduling repair." In EM). IEEE, 2010. http://dx.doi.org/10.1109/ieem.2010.5674383.

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Lima Carneiro Alves De, Bruno Rucy, Merlin Kramer, and Victor Henrique Cabral Pinheiro. "Distributed Incremental Ontology Reasoning over Dynamic T-boxes." In AICCC 2024: 2024 the 7th Artificial Intelligence and Cloud Computing Conference. ACM, 2024. https://doi.org/10.1145/3719384.3719446.

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Nilsson, Mikael, Jonas Kvarnstrom, and Patrick Doherty. "Incremental Dynamic Controllability in Cubic Worst-Case Time." In 2014 21st International Symposium on Temporal Representation and Reasoning (TIME). IEEE, 2014. http://dx.doi.org/10.1109/time.2014.13.

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Reports on the topic "Incremental reasoning"

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Suntisrivaraporn, Boontawee. Module Extraction and Incremental Classification: A Pragmatic Approach for EL ⁺ Ontologies. Technische Universität Dresden, 2003. http://dx.doi.org/10.25368/2022.161.

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The description logic EL⁺ has recently proved practically useful in the life science domain with presence of several large-scale biomedical ontologies such as Snomed ct. To deal with ontologies of this scale, standard reasoning of classification is essential but not sufficient. The ability to extract relevant fragments from a large ontology and to incrementally classify it has become more crucial to support ontology design, maintenance and reuse. In this paper, we propose a pragmatic approach to module extraction and incremental classification for EL⁺ ontologies and report on empirical evaluat
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Yip, Eugene, and Gerald Lüttgen. Heterogeneous Specification of Spacecraft Software. Otto-Friedrich-Universität, 2024. http://dx.doi.org/10.20378/irb-97634.

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The operational behaviour of a reactive system is commonly specified or modelled as concurrent state machines, where each machine models the possible states or modes of a software component and its interactions with the environment. However, state machines can quickly become verbose when execution constraints between concurrent states need to be modelled. Alternatively, constraints could be modelled declaratively as special edges between states. Such a heterogeneous modelling approach is employed by Virtual Satellite (VirSat), a model-based systems engineering tool from the German Aerospace Ce
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Ruvinsky, Alicia, Maria Seale, R. Salter, and Natàlia Garcia-Reyero. An ontology for an epigenetics approach to prognostics and health management. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/46632.

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Techniques in prognostics and health management have advanced considerably in the last few decades, enabled by breakthroughs in computational methods and supporting technologies. These predictive models, whether data-driven or physics-based, target the modeling of a system’s aggregate performance. As such, they generalize assumptions about the modelled system’s components, and are thus limited in their ability to represent individual components and the dynamic environmental factors that affect composite system health. To address this deficiency, we have developed an epigenetics-inspired knowle
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