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Journal articles on the topic 'Incremental reasoning'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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2

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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9

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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BECK, HARALD, THOMAS EITER, and CHRISTIAN FOLIE. "Ticker: A system for incremental ASP-based stream reasoning." Theory and Practice of Logic Programming 17, no. 5-6 (2017): 744–63. http://dx.doi.org/10.1017/s1471068417000370.

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AbstractIn complex reasoning tasks, as expressible by Answer Set Programming (ASP), problems often permit for multiple solutions. In dynamic environments, where knowledge is continuously changing, the question arises how a given model can be incrementally adjusted relative to new and outdated information. This paper introduces Ticker, a prototypical engine for well-defined logical reasoning over streaming data. Ticker builds on a practical fragment of the recent rule-based language LARS, which extends ASP for streams by providing flexible expiration control and temporal modalities. We discuss

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12

SWIFT, TERRANCE. "Incremental Tabling in Support of Knowledge Representation and Reasoning." Theory and Practice of Logic Programming 14, no. 4-5 (2014): 553–67. http://dx.doi.org/10.1017/s1471068414000209.

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AbstractResolution-based Knowledge Representation and Reasoning (KRR) systems, such as Flora-2, Silk or Ergo, can scale to tens or hundreds of millions of facts, while supporting reasoning that includes Hilog, inheritance, defeasibility theories, and equality theories. These systems handle the termination and complexity issues that arise from the use of these features by a heavy use of tabled resolution. In fact, such systems table by default all rules defined by users, unless they are simple facts.Performing dynamic updates within such systems is nearly impossible unless the tables themselves

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Forti, Stefano, Giuseppe Bisicchia, and Antonio Brogi. "Declarative continuous reasoning in the cloud-IoT continuum." Journal of Logic and Computation 32, no. 2 (2022): 206–32. http://dx.doi.org/10.1093/logcom/exab083.

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Abstract Developing and releasing multiservice applications rely upon a pipeline of automation tools known as Continuous Integration/Continuous Deployment. Among those tools, continuous reasoning is exploited by large companies to perform incremental static analyses on their code commits as soon as they are integrated into a shared codebase. In this article, we extend continuous reasoning towards the continuous QoS- and context-aware management of multiservice applications in Cloud-IoT scenarios. We propose a novel continuous reasoning methodology that supports runtime decision on service plac

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14

Luo, Jie, Yifei Wang, and Yi Xu. "Incremental Theory Closure Reasoning for Large Scale Knowledge Graphs." IEEE Access 7 (2019): 24593–601. http://dx.doi.org/10.1109/access.2019.2900297.

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Dovland, Johan, Einar Broch Johnsen, Olaf Owe, and Martin Steffen. "Incremental reasoning with lazy behavioral subtyping for multiple inheritance." Science of Computer Programming 76, no. 10 (2011): 915–41. http://dx.doi.org/10.1016/j.scico.2010.09.006.

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Zhang, Junhui, Hongying Zan, Shuning Wu, Kunli Zhang, and Jianwei Huo. "Adaptive Graph Neural Network with Incremental Learning Mechanism for Knowledge Graph Reasoning." Electronics 13, no. 14 (2024): 2778. http://dx.doi.org/10.3390/electronics13142778.

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Knowledge graphs are extensively utilized in diverse fields such as search engines, recommendation systems, and dialogue systems, and knowledge graph reasoning plays an important role in the aforementioned domains. Graph neural networks demonstrate the capability to effectively capture and process the graph structure inherent in knowledge graphs, leveraging the relationships between nodes and edges to enable efficient reasoning. Current research on graph neural networks relies on predefined propagation paths. The models based on predefined propagation paths overlook the correlation between ent

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Gomes, Cristiano Mauro Assis, Jhonys De Araujo, and Enio Galinkin Jelihovschi. "Predictive and incremental validity of Students’ Learning Approach Test (SLAT-Thinking)." Revista Interamericana de Psicología/Interamerican Journal of Psychology 57, no. 3 (2023): e1514. http://dx.doi.org/10.30849/ripijp.v57i3.1514.

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SLAT-Thinking is the only test that evaluates and distinguishes stages of approaches through performance. Although SLAT-Thinking shows evidence of internal validity, its external validity has not yet been examined. In this paper we study the predictive and incremental validities of SLAT-Thinking. Two models were tested. The predictors were inductive reasoning, SLAT-Thinking approaches and Learning Approaches Scale (EABAP) approaches. The outcome was the Brazilian large-scale exam that evaluates the students that finish secondary education. In both models, the superficial approach of SLAT-Think

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18

Tang, Wen Ting, Chao Li Tang, Lei Huang, and He Yang. "Application Research on Expert System of Incremental In-Plane Bending." Materials Science Forum 575-578 (April 2008): 600–605. http://dx.doi.org/10.4028/www.scientific.net/msf.575-578.600.

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Incremental in-plane bending is a flexible and laborsaving manufacturing technology for short production runs in a variety of sizes and shapes. But the technology parameters are interactive intimately and it is hard to forecast and control the bending radius accurately. Based on the features of incremental in-plane bending and the advantage of expert system dealing with problems, an expert system of incremental in-plane bending by designing knowledge base of representation of creation regulation, reasoning system based on rules and search mechanism and explaining system based on prefabricating

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Galić, Zvonimir. "Conditional Reasoning Test for Aggression: Further evidence about incremental validity." International Journal of Selection and Assessment 24, no. 1 (2016): 24–33. http://dx.doi.org/10.1111/ijsa.12126.

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20

Unel, Gulay. "Incremental reasoning on monadic second-order logics with logic programming." Journal of Logical and Algebraic Methods in Programming 85, no. 2 (2016): 316–31. http://dx.doi.org/10.1016/j.jlamp.2015.11.002.

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Ianni, Giovambattista, Francesco Pacenza, and Jessica Zangari. "Incremental maintenance of overgrounded logic programs with tailored simplifications." Theory and Practice of Logic Programming 20, no. 5 (2020): 719–34. http://dx.doi.org/10.1017/s147106842000040x.

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AbstractThe repeated execution of reasoning tasks is desirable in many applicative scenarios, such as stream reasoning and event processing. When using answer set programming in such contexts, one can avoid the iterative generation of ground programs thus achieving a significant payoff in terms of computing time. However, this may require some additional amount of memory and/or the manual addition of operational directives in the declarative knowledge base at hand. We introduce a new strategy for generating series of monotonically growing propositional programs. The proposed overgrounded progr

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22

de Niz, Dionisio, and Lutz Wrage. "Symbolic Refinement for CPS." ACM SIGAda Ada Letters 43, no. 1 (2023): 88–93. http://dx.doi.org/10.1145/3631483.3631498.

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In this paper we present an analysis contract approach that takes advantage of efficient domain-specific analysis algorithms, enable incremental analysis of architectural model refinements, and implement assume-guarantee reasoning in symbolic domains in SMT.

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JARD, CLAUDE, and GUY-VINCENT JOURDAN. "INCREMENTAL TRANSITIVE DEPENDENCY TRACKING IN DISTRIBUTED COMPUTATIONS." Parallel Processing Letters 06, no. 03 (1996): 427–35. http://dx.doi.org/10.1142/s0129626496000406.

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The notion of causal dependency between events in distributed systems plays a central role in reasoning about distributed program behaviours [14]. Different techniques have been designed to track these dependencies during execution. We suggest a new incremental transitive dependency tracking technique. Once the transitive dependencies are recorded for an observable event, the dependency tracking cost can be reduced by propagating only future dependencies beyond that event. Furthermore, in contrast with the direct dependency tracking technique already proposed in the literature, our technique a

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Lee, Wan-Gon, Sung-Hyuk Bang, and Young-Tack Park. "Large Scale Incremental Reasoning using SWRL Rules in a Distributed Framework." Journal of KIISE 44, no. 4 (2017): 383–91. http://dx.doi.org/10.5626/jok.2017.44.4.383.

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Mokhtarian, Hossein, Eric Coatanéa, and Henri Paris. "Function modeling combined with physics-based reasoning for assessing design options and supporting innovative ideation." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 31, no. 4 (2017): 476–500. http://dx.doi.org/10.1017/s0890060417000403.

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AbstractFunctional modeling is an analytical approach to design problems that is widely taught in certain academic communities but not often used by practitioners. This approach can be applied in multiple ways to formalize the understanding of the systems, to support the synthesis of the design in the development of a new product, or to support the analysis and improvement of existing systems incrementally. The type of usage depends on the objectives that are targeted. The objectives can be categorized into two key groups: discovering a totally new solution, or improving an existing one. This

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Balduccini, Marcello, Michael Barborak, and David Ferrucci. "Pushing the Limits of Clingo’s Incremental Grounding and Solving Capabilities in Practical Applications." Algorithms 16, no. 3 (2023): 169. http://dx.doi.org/10.3390/a16030169.

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Incremental techniques aim at making it possible to improve the performance of the grounding and solving processes by reusing the results of previous executions. Clingo supports both incremental grounding and incremental solving computations. In order to leverage incremental computations in clingo, the incremental fragments of ASP programs must satisfy certain safety-related conditions. In a number of problem domains and reasoning tasks, these conditions can be satisfied in a fairly straightforward way. However, we have observed that in certain practical applications, satisfying the conditions

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Cheng, Gaojie, Jianmo Liu, Jianfeng Xu, and Xiqiu Wang. "Dynamic Online learning Algorithm For Three-way decision." MATEC Web of Conferences 232 (2018): 03024. http://dx.doi.org/10.1051/matecconf/201823203024.

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Three-way decision is an important theory for solving uncertain problems. Online computing is a new dynamic Stream computing form. How to execute three-way decision quickly in online computing is a challenging topic. In this paper, Online computing process is divided into incremental computing portion and decreasing computing portion. And a three-way decision dynamic incremental and decreasing learning algorithm for online computing is proposed. Firstly, the dynamic incremental and decreasing learning models is studied for stream computing based on probabilistic rough set . Then, the logical r

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Gogic, G., C. H. Papadimitriou, and M. Sideri. "Incremental Recompilation of Knowledge." Journal of Artificial Intelligence Research 8 (January 1, 1998): 23–37. http://dx.doi.org/10.1613/jair.380.

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Approximating a general formula from above and below by Horn formulas (its Horn envelope and Horn core, respectively) was proposed by Selman and Kautz (1991, 1996) as a form of ``knowledge compilation,'' supporting rapid approximate reasoning; on the negative side, this scheme is static in that it supports no updates, and has certain complexity drawbacks pointed out by Kavvadias, Papadimitriou and Sideri (1993). On the other hand, the many frameworks and schemes proposed in the literature for theory update and revision are plagued by serious complexity-theoretic impediments, even in the Horn c

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Hu, Sheng, Yuqing Ma, Xianglong Liu, Yanlu Wei, and Shihao Bai. "Stratified Rule-Aware Network for Abstract Visual Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1567–74. http://dx.doi.org/10.1609/aaai.v35i2.16248.

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Abstract reasoning refers to the ability to analyze information, discover rules at an intangible level, and solve problems in innovative ways. Raven's Progressive Matrices (RPM) test is typically used to examine the capability of abstract reasoning. The subject is asked to identify the correct choice from the answer set to fill the missing panel at the bottom right of RPM (e.g., a 3×3 matrix), following the underlying rules inside the matrix. Recent studies, taking advantage of Convolutional Neural Networks (CNNs), have achieved encouraging progress to accomplish the RPM test. However, they pa

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Gochev, Kalin, Alla Safonova, and Maxim Likhachev. "Incremental Planning with Adaptive Dimensionality." Proceedings of the International Conference on Automated Planning and Scheduling 23 (June 2, 2013): 82–90. http://dx.doi.org/10.1609/icaps.v23i1.13569.

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Path planning is often a high-dimensional computationally-expensive planning problem as it requires reasoning about the kinodynamic constraints of the robot and collisions of the robot with the environment. However, large regions of the environment are typically benign enough that a much faster low-dimensional planning combined with a local path following controller suffice. Planning with Adaptive Dimensionality that was recently developed makes use of this observation and iteratively constructs and searches a state-space consisting of mainly low-dimensional states. It only introduces regions

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Zhang, Yihong, Yisu Remy Wang, Oliver Flatt, et al. "Better Together: Unifying Datalog and Equality Saturation." Proceedings of the ACM on Programming Languages 7, PLDI (2023): 468–92. http://dx.doi.org/10.1145/3591239.

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We present egglog, a fixpoint reasoning system that unifies Datalog and equality saturation (EqSat). Like Datalog, egglog supports efficient incremental execution, cooperating analyses, and lattice-based reasoning. Like EqSat, egglog supports term rewriting, efficient congruence closure, and extraction of optimized terms. We identify two recent applications -- a unification-based pointer analysis in Datalog and an EqSat-based floating-point term rewriter -- that have been hampered by features missing from Datalog but found in EqSat or vice-versa. We evaluate our system by reimplementing those

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32

Wu, Bo, Hong Yan Zheng, and Yan Peng Feng. "A Novel Point-Based Incremental Pruning Algorithm for POMDP." Applied Mechanics and Materials 513-517 (February 2014): 1088–91. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1088.

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Partially Observable Markov Decision Processes (POMDP) provides piecewise-linear a natural and principled framework for sequential decision-making under uncertainty. However, large-scale POMDP suffers from the exponential growth of the belief points and policy trees space. We present a new point-based incremental pruning algorithm based on the piecewise linearity and convexity of the value function. Instead of reasoning about the whole belief space when pruning the cross-sums in POMDP policy construction, our algorithm uses belief points to perform approximate pruning by generating policy tree

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Gerards, Janneke. "The prism of fundamental rights." European Constitutional Law Review 8, no. 2 (2012): 173–202. http://dx.doi.org/10.1017/s1574019612000144.

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European Court of Human Rights – Suggestions for reducing case backlog and national criticism of the Court – Alternative to incremental case law and reasoning by analogy – Greater deference to national courts where individual interests, rather than fundamental rights are at stake – Guidelines to find objective criteria for the definition of fundamental rights – Sharper delineation of Convention rights – Procedural review preceding substantive review

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Goswami, Usha. "Analogy and the brain: A new perspective on relational primacy." Behavioral and Brain Sciences 31, no. 4 (2008): 387–88. http://dx.doi.org/10.1017/s0140525x08004561.

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AbstractLeech et al.'s demonstration that analogical reasoning can be an emergent property of low-level incremental learning processes is critical for analogical theory. Along with insights into neural learning based on the salience of dynamic spatio-temporal structure, and the neural priming mechanism of repetition suppression, it establishes relational primacy as a plausible theoretical description of how brains make analogies.

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KAWEWONG, Aram, Yutaro HONDA, Manabu TSUBOYAMA, and Osamu HASEGAWA. "Reasoning on the Self-Organizing Incremental Associative Memory for Online Robot Path Planning." IEICE Transactions on Information and Systems E93-D, no. 3 (2010): 569–82. http://dx.doi.org/10.1587/transinf.e93.d.569.

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Dehbi, Youness, Julius Knechtel, Benjamin Niedermann, and Jan-Henrik Haunert. "Incremental constraint-based reasoning for estimating as-built electric line routing in buildings." Automation in Construction 143 (November 2022): 104571. http://dx.doi.org/10.1016/j.autcon.2022.104571.

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Antonietti, Alessandro, and Michela Balconi. "Analogical reasoning: An incremental or insightful process? What cognitive and cortical evidence suggests." Cognitive Neuroscience 1, no. 2 (2010): 137–38. http://dx.doi.org/10.1080/17588921003786606.

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Kim, Dong-Il, and Jin-Won Lee. "Commutation of permanent magnet A.C. servo motors with incremental encoders via fuzzy reasoning." Mechatronics 4, no. 5 (1994): 455–69. http://dx.doi.org/10.1016/0957-4158(94)90010-8.

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Alexandropoulou, Stavroula, Jakub Dotlačil, and Rick Nouwen. "Pragmatic effects of `more than' and `at least' in incremental interpretation." Semantics and Linguistic Theory 27 (October 19, 2017): 680. http://dx.doi.org/10.3765/salt.v27i0.4186.

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We investigate the incremental interpretation of comparative and superlative numeral modifiers by manipulating the speaker’s epistemic state in an eye-tracking reading experiment. The results reveal a different processing profile for two types of numeral modifiers. We take this difference to point to a difference in the source and nature of the attested effects (e.g., Quantity- vs. Manner-based pragmatic reasoning). Our findings inform the existing theoretical landscape, invalidating a number of accounts of speaker ignorance effects with numeral modifiers and giving support to Quantity-based a

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Borgida, A. "Extensible Knowledge Representation: the Case of Description Reasoners." Journal of Artificial Intelligence Research 10 (June 1, 1999): 399–434. http://dx.doi.org/10.1613/jair.584.

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This paper offers an approach to extensible knowledge representation and reasoning for a family of formalisms known as Description Logics. The approach is based on the notion of adding new concept constructors, and includes a heuristic methodology for specifying the desired extensions, as well as a modularized software architecture that supports implementing extensions. The architecture detailed here falls in the normalize-compared paradigm, and supports both intentional reasoning (subsumption) involving concepts, and extensional reasoning involving individuals after incremental updates to the

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Le-Phuoc, Danh, Thomas Eiter, and Anh Le-Tuan. "A Scalable Reasoning and Learning Approach for Neural-Symbolic Stream Fusion." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (2021): 4996–5005. http://dx.doi.org/10.1609/aaai.v35i6.16633.

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Driven by deep neural networks (DNN), the recent development of computer vision makes vision sensors such as stereo cameras and Lidars ubiquitous in autonomous cars, robotics and traffic monitoring. However, a traditional DNN-based data fusion pipeline like object tracking has to hard-wire an engineered set of DNN models to a fixed processing logic, which makes it difficult to infuse new models to that pipeline. To overcome this, we propose a novel neural-symbolic stream reasoning approach realised by semantic stream reasoning programs which specify DNN-based data fusion pipelines via logic ru

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KERKEZ, BORIS, and MICHAEL T. COX. "INCREMENTAL CASE-BASED PLAN RECOGNITION WITH LOCAL PREDICTIONS." International Journal on Artificial Intelligence Tools 12, no. 04 (2003): 413–63. http://dx.doi.org/10.1142/s0218213003001307.

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We present a novel case-based plan recognition method that interprets observations of plan behavior using an incrementally constructed case library of past observations. The technique is novel in several ways. It combines plan recognition with case-based reasoning and leverages the strengths of both. The representation of a plan is a sequence of action-state pairs rather than only the actions. The technique compensates for the additional complexity with a unique abstraction scheme augmented by pseudo-isomorphic similarity relations to represent indices into the case base. Past cases are used t

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Nihad, El Ghouch, Kouissi Mohamed, and En-Naimi El Mokhtar. "Designing and modeling of a multi-agent adaptive learning system (MAALS) using incremental hybrid case-based reasoning (IHCBR)." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (2020): 1980. http://dx.doi.org/10.11591/ijece.v10i2.pp1980-1992.

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Several researches in the field of adaptive learning systems has developed systems and techniques to guide the learner and reduce cognitive overload, making learning adaptation essential to better understand preferences, the constraints and learning habits of the learner. Thus, it is particularly advisable to propose online learning systems that are able to collect and detect information describing the learning process in an automatic and deductive way, and to rely on this information to follow the learner in real time and offer him training according to his dynamic learning pace. This article

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El, Ghouch Nihad, Mohamed Kouissi, and El Mokhtar En-Naimi. "Designing and modeling of a multi-agent adaptive learning system (MAALS) using incremental hybrid case-based reasoning (IHCBR)." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (2020): 1980–92. https://doi.org/10.11591/ijece.v10i2.pp1980-1992.

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Several researches in the field of adaptive learning systems has developed systems and techniques to guide the learner and reduce cognitive overload, making learning adaptation essential to better understand preferences, the constraints and learning habits of the learner. Thus, it is particularly advisable to propose online learning systems that are able to collect and detect information describing the learning process in an automatic and deductive way, and to rely on this information to follow the learner in real time and offer him training according to his dynamic learning pace. This article

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Gerevini, Alfonso. "Incremental qualitative temporal reasoning: Algorithms for the Point Algebra and the ORD-Horn class." Artificial Intelligence 166, no. 1-2 (2005): 37–80. http://dx.doi.org/10.1016/j.artint.2005.04.005.

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Chen, Zhen, Denghui Xie, Xiaolong Wang, Dianlong You, and Limin Shen. "High-order complementary cloud application programming interface recommendation with logical reasoning for incremental development." Engineering Applications of Artificial Intelligence 140 (January 2025): 109698. http://dx.doi.org/10.1016/j.engappai.2024.109698.

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Toroghi, Armin, and Scott Sanner. "Bayesian Inference with Complex Knowledge Graph Evidence." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 18 (2024): 20550–58. http://dx.doi.org/10.1609/aaai.v38i18.30040.

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Knowledge Graphs (KGs) provide a widely used format for representing entities and their relationships and have found use in diverse applications including question answering and recommendation. A majority of current research on KG inference has focused on reasoning with atomic facts (triples) and has disregarded the possibility of making complex evidential observations involving logical operators (negation, conjunction, disjunction) and quantifiers (existential, universal). Further, while the application of complex evidence has been explored in KG-based query answering (KGQA) research, in many

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Khan, Abdus Salam, and Achim Hoffmann. "An Advanced Artificial Intelligence Tool for Menu Design." Nutrition and Health 17, no. 1 (2003): 43–53. http://dx.doi.org/10.1177/026010600301700105.

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The computer-assisted menu design still remains a difficult task. Usually knowledge that aids in menu design by a computer is hard-coded and because of that a computerised menu planner cannot handle the menu design problem for an unanticipated client. To address this problem we developed a menu design tool, MIKAS (menu construction using incremental knowledge acquisition system), an artificial intelligence system that allows the incremental development of a knowledge-base for menu design. We allow an incremental knowledge acquisition process in which the expert is only required to provide hint

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Earley, Christine E. "Knowledge Acquisition in Auditing: Training Novice Auditors to Recognize Cue Relationships in Real Estate Valuation." Accounting Review 76, no. 1 (2001): 81–97. http://dx.doi.org/10.2308/accr.2001.76.1.81.

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This study examines novice auditors' knowledge acquisition. I investigate whether low-cost, informal training interventions improve knowledge acquisition in complex auditing tasks. I designed an experiment that tests the effect of two alternative training approaches on audit judgment performance: explanatory feedback (a replication and extension of Bonner and Walker [1994]), and self-explanation of the rationale underlying a judgment of reasonableness. The results suggest that each approach promotes procedural knowledge acquisition, and combining the two approaches provides more benefit than e

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Hoeks, Morwenna, Maziar Toosarvandani, and Amanda Rysling. "How to compute a focus: Evidence from incremental processing." Experiments in Linguistic Meaning 3 (January 24, 2025): 188–200. https://doi.org/10.3765/elm.3.5831.

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The global interpretation of a focus marked sentence with a particle like only arises due to the interplay of several formal components: F-marking, the semantics of the particle, the nature of contrastive alternatives, and a dependence on context. In this paper, we argue that on-line reading measures can be used to probe which of these com- ponents are computed when. In order to isolate which components give rise to reading slowdowns typically observed on foci (Birch & Rayner 1997, Benatar & Clifton 2014, Lowder & Gordon 2015, Hoeks et al. 2023), a Maze reading study tested whether

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