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Journal articles on the topic 'Abstraction'

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Published: 4 June 2021

Last updated: 30 July 2024

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1

Bonet, Blai. "Abstraction Heuristics Extended with Counting Abstractions." Proceedings of the International Conference on Automated Planning and Scheduling 21 (March 22, 2011): 311–14. http://dx.doi.org/10.1609/icaps.v21i1.13480.

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State-of-the-art abstraction heuristics are those constructed by the merge-and-shrink approach in which an abstraction consists of a labeled transition system, and the composition of abstractions correspond to the synchronized product of transition systems. Merge-and-shrink heuristics build a composite abstraction from atomic abstractions that are directly associated with the variables of the planning problem. In this paper, we show that the framework of labeled transition systems is more general, and propose a new type of abstraction called the counting abstraction. Counting abstractions can be transparently combined with other type of abstractions to get more informative heuristics. We show how to effectively construct the counting abstractions and presents preliminary experiments over benchmark problems.

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2

Backstrom, Christer, and Peter Jonsson. "Abstracting Abstraction in Search II: Complexity Analysis." Proceedings of the International Symposium on Combinatorial Search 3, no. 1 (August 20, 2021): 10–17. http://dx.doi.org/10.1609/socs.v3i1.18240.

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Modelling abstraction as a function from the original state space to an abstract state space is a common approach in combinatorial search. Sometimes this is too restricted, though, and we have previously proposed a framework using a more flexible concept of transformations between labelled graphs. We also proposed a number of properties to describe and classify such transformations. This framework enabled the modelling of a number of different abstraction methods in a way that facilitated comparative analyses. It is of particular interest that these properties can be used to capture the concept of refinement without backtracking between levels; how to do this has been an open question for at least twenty years. In this paper, we continue our previous research by analysing the complexity of testing the various transformation properties for both explicit and implicit graph~representations.

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3

Landim Filho, Raul. "A Questão dos Universais Segundo a Teoria Tomista da Abstração." Analytica - Revista de Filosofia 12, no. 2 (August 1, 2013): 11–33. http://dx.doi.org/10.35920/arf.v12i2.540.

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O artigo pretende mostrar que a teoria da abstração de Tomás de Aquino justifica a consistência das seguintes teses: [i] os universais só existem na mente; [ii] fora da mente só existem os singulares e [iii] que tudo no singular é singularizado. O artigo analisa, então, os modos de abstração tomásica: a abstração do todo (ou a do universal a partir do particular), a abstração da forma da matéria sensível e, ainda, o que alguns tomistas consideram também como um modo de abstração: a abstração precisiva. Finalmente, o artigo contrapõe a interpretação de Cajetano da abstração à teoria de Tomás de Aquino e procura mostrar suas semelhanças e suas diferenças. AbstractIn this article, I intend to show that AquinasÂ´s theory of abstraction explains the consistency of the following theses: [a] universals as such exist only in the soul; [b], only particulars (singulars) exist in nature [c] in particulars everything is singularized. I analyze the two modes of abstraction on Aquinas: abstraction of a whole, that is, abstraction of the universal from the particular (abstraction totius) and abstraction of the form from the sensible matter (abstractio formae). Besides that, I consider another operation of the intellect that some thomists also consider as a mode of abstraction, that is, the operation of precision. At the end of the article I analyze Cajetan's theory of abstraction - total and formal abstraction - aiming to show the differences and similitude between this theory and AquinasÂ´s.

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4

Odilovich, Bazarov Oybek. "Abstraction And Language Model." American Journal of Social Science and Education Innovations 02, no. 08 (August 11, 2020): 51–54. http://dx.doi.org/10.37547/tajssei/volume02issue08-08.

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5

Ganascia, Jean-Gabriel. "Abstraction of levels of abstraction." Journal of Experimental & Theoretical Artificial Intelligence 27, no. 1 (August 26, 2014): 23–35. http://dx.doi.org/10.1080/0952813x.2014.940685.

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6

KUNZ, THOMAS. "REVERSE ENGINEERING DISTRIBUTED APPLICATIONS: AN EVENT ABSTRACTION TOOL." International Journal of Software Engineering and Knowledge Engineering 04, no. 03 (September 1994): 303–23. http://dx.doi.org/10.1142/s0218194094000155.

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Understanding the behavior of distributed applications is a very challenging task due to the complexity of these applications. To manage complexity, the top-down use of suitable abstraction hierarchies is frequently proposed. Given the complexity of distributed applications, manually deriving such abstraction hierarchies is not realistic. The execution of distributed applications is typically analyzed using an event-based approach. This paper discusses one tool that groups more primitive events into abstract events to derive a hierarchy of abstract events automatically. Ideally, these abstractions should reveal logical units of an application and their relations. To explore the abstraction hierarchies derived, an existing prototype visualization tool was modified to provide abstract visualizations. A user can navigate through these abstraction hierarchies, displaying an execution at various levels of abstraction. Examples of such abstract visualizations are given and discussed. In general, the abstractions derived automatically represent meaningful parts of the application: they can be interpreted in terms of the application domain. While the abstraction tool does not necessarily derive the best possible abstraction hierarchies in all cases, it performs the bulk of the work and provides good initial abstractions which can subsequently be refined manually.

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7

Bleeker, Maaike. "Abstraction." Philosophy Today 63, no. 4 (2019): 845–58. http://dx.doi.org/10.5840/philtoday202013296.

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This text elaborates an understanding of abstraction as fundamental to how we think from a closer look at relationships between abstraction, movement, materiality and lived experience. Starting from Whitehead-inspired reflections on abstraction by Alberto Toscano and Brian Massumi, the differences between their respective readings of his work are shown to be indicative for their different conceptions of the relationships between abstraction, the concrete, and lived experience. The text then continues to elaborate how Alva Noë’s enactive approach to perception illuminates the central role of movement and sensorimotor skills in the emergence of abstractions from the continuity of process that is reality, and could contribute to further understanding of the relationship between movement and abstraction as what Massumi describes as the incorporeal dimension of the real. Finally, this text reflects on the potential of movement practices (including dance) and technology to become part of how abstraction is achieved.

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8

Volsik, Paul. "Abstraction." Word & Image 11, no. 2 (April 1995): 120–28. http://dx.doi.org/10.1080/02666286.1995.10435907.

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9

Men, Chunlin. "Abstraction." Victorian Literature and Culture 51, no. 3 (2023): 355–58. http://dx.doi.org/10.1017/s1060150323000153.

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This essay focuses on “abstraction” as an underresearched keyword in Victorian studies. I argue that the productive ambiguity of abstraction indexes contradictions and tensions in capitalist modernity, statistical thinking, and interdisciplinary mediations that trace significant parts of their histories to the nineteenth century and still heavily inform our current Victorian scholarships.

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10

Fox, Ian A., and Susan Walker. "Abstraction and abstraction control in Scotland." Science of The Total Environment 294, no. 1-3 (July 2002): 201–11. http://dx.doi.org/10.1016/s0048-9697(02)00068-2.

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11

Abel, David. "A Theory of State Abstraction for Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9876–77. http://dx.doi.org/10.1609/aaai.v33i01.33019876.

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Reinforcement learning presents a challenging problem: agents must generalize experiences, efficiently explore the world, and learn from feedback that is delayed and often sparse, all while making use of a limited computational budget. Abstraction is essential to all of these endeavors. Through abstraction, agents can form concise models of both their surroundings and behavior, supporting effective decision making in diverse and complex environments. To this end, the goal of my doctoral research is to characterize the role abstraction plays in reinforcement learning, with a focus on state abstraction. I offer three desiderata articulating what it means for a state abstraction to be useful, and introduce classes of state abstractions that provide a partial path toward satisfying these desiderata. Collectively, I develop theory for state abstractions that can 1) preserve near-optimal behavior, 2) be learned and computed efficiently, and 3) can lower the time or data needed to make effective decisions. I close by discussing extensions of these results to an information theoretic paradigm of abstraction, and an extension to hierarchical abstraction that enjoys the same desirable properties.

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12

Delarue, Henrique Carvalho. "Da Referência à Abstração: um ensaio sobre o diagrama como ponto de inflexão no projeto arquitetônico." Revista Prumo 5, no. 8 (April 23, 2020): 46–59. http://dx.doi.org/10.24168/revistaprumo.v0i8.1175.

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Starting from the principle that the diagram functions by abstraction and that, in most cases, its use today is limited to an explanation, this essay explores some of its aspects that make possible its use as an instrument for the architectural design project. By doing a brief analysis on how architecture used to take onto itself a symbolic value engendered in Antiquity and how it came to be seen in an abstract manner, a parallel is drawn between modes of thinking architecture and how abstraction moves away from the dependency of a reference to construct meaning. Whilst abstraction is intensified, architectural elements carry less meaning within a referential system and are seen more according to how they relate to each other. It is by considering the relations that can be absorbed by an architecture that the diagram finds its potential to precede form and reshape how we think the design process.

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13

Büchner, Clemens, Patrick Ferber, Jendrik Seipp, and Malte Helmert. "Abstraction Heuristics for Factored Tasks." Proceedings of the International Conference on Automated Planning and Scheduling 34 (May 30, 2024): 40–49. http://dx.doi.org/10.1609/icaps.v34i1.31459.

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One of the strongest approaches for optimal classical planning is A* search with heuristics based on abstractions of the planning task. Abstraction heuristics are well studied in planning formalisms without conditional effects such as SAS+. However, conditional effects are crucial to model many planning tasks compactly. In this paper, we focus on *factored* tasks which allow a specific form of conditional effect, where effects on variable x can only depend on the value of x. We generalize projections, domain abstractions, Cartesian abstractions and the counterexample-guided abstraction refinement method to this formalism. While merge-and-shrink already covers factored task in theory, we provide an implementation that does so. In our experiments, we compare these abstraction-based heuristics to other heuristics supporting conditional effects, as well as symbolic search. On our new benchmark set of factored tasks, pattern database heuristics solve the most problems, followed by symbolic approaches on par with domain abstractions. The more general Cartesian abstractions fall behind in terms of coverage but usually solve problems the fastest among all tested approaches. The generality of merge-and-shrink abstractions does not seem to be beneficial for these factored tasks.

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14

Wang, Wendy, and Drew Hwang. "Abstraction Assistant: An automatic text abstraction system." Journal of the American Society for Information Science and Technology 61, no. 9 (August 25, 2010): 1790–99. http://dx.doi.org/10.1002/asi.21341.

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15

Hostetler, Jesse, Alan Fern, and Thomas Dietterich. "Sample-Based Tree Search with Fixed and Adaptive State Abstractions." Journal of Artificial Intelligence Research 60 (December 14, 2017): 717–77. http://dx.doi.org/10.1613/jair.5483.

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Sample-based tree search (SBTS) is an approach to solving Markov decision problems based on constructing a lookahead search tree using random samples from a generative model of the MDP. It encompasses Monte Carlo tree search (MCTS) algorithms like UCT as well as algorithms such as sparse sampling. SBTS is well-suited to solving MDPs with large state spaces due to the relative insensitivity of SBTS algorithms to the size of the state space. The limiting factor in the performance of SBTS tends to be the exponential dependence of sample complexity on the depth of the search tree. The number of samples required to build a search tree is O((|A|B)^d), where |A| is the number of available actions, B is the number of possible random outcomes of taking an action, and d is the depth of the tree. State abstraction can be used to reduce B by aggregating random outcomes together into abstract states. Recent work has shown that abstract tree search often performs substantially better than tree search conducted in the ground state space. This paper presents a theoretical and empirical evaluation of tree search with both fixed and adaptive state abstractions. We derive a bound on regret due to state abstraction in tree search that decomposes abstraction error into three components arising from properties of the abstraction and the search algorithm. We describe versions of popular SBTS algorithms that use fixed state abstractions, and we introduce the Progressive Abstraction Refinement in Sparse Sampling (PARSS) algorithm, which adapts its abstraction during search. We evaluate PARSS as well as sparse sampling with fixed abstractions on 12 experimental problems, and find that PARSS outperforms search with a fixed abstraction and that search with even highly inaccurate fixed abstractions outperforms search without abstraction. These results establish progressive abstraction refinement as a promising basis for new tree search algorithms, and we propose directions for future work within the progressive refinement framework.

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16

Domshlak, Carmel, Michael Katz, and Sagi Lefler. "When Abstractions Met Landmarks." Proceedings of the International Conference on Automated Planning and Scheduling 20 (May 25, 2021): 50–56. http://dx.doi.org/10.1609/icaps.v20i1.13409.

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Abstractions and landmarks are two powerful mechanisms for devising admissible heuristics for classical planning. Here we aim at putting them together by integrating landmark information into abstractions, and propose a concrete realization of this direction suitable for structural-pattern abstractions, as well as for other abstraction heuristics. Our empirical evaluation shows that landmark information can substantially improve the quality of abstraction heuristic estimates.

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17

Seipp, Jendrik, and Malte Helmert. "Counterexample-Guided Cartesian Abstraction Refinement for Classical Planning." Journal of Artificial Intelligence Research 62 (July 25, 2018): 535–77. http://dx.doi.org/10.1613/jair.1.11217.

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Counterexample-guided abstraction refinement (CEGAR) is a method for incrementally computing abstractions of transition systems. We propose a CEGAR algorithm for computing abstraction heuristics for optimal classical planning. Starting from a coarse abstraction of the planning task, we iteratively compute an optimal abstract solution, check if and why it fails for the concrete planning task and refine the abstraction so that the same failure cannot occur in future iterations. A key ingredient of our approach is a novel class of abstractions for classical planning tasks that admits efficient and very fine-grained refinement. Since a single abstraction usually cannot capture enough details of the planning task, we also introduce two methods for producing diverse sets of heuristics within this framework, one based on goal atoms, the other based on landmarks. In order to sum their heuristic estimates admissibly we introduce a new cost partitioning algorithm called saturated cost partitioning. We show that the resulting heuristics outperform other state-of-the-art abstraction heuristics in many benchmark domains.

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18

Seipp, Jendrik, and Malte Helmert. "Counterexample-Guided Cartesian Abstraction Refinement." Proceedings of the International Conference on Automated Planning and Scheduling 23 (June 2, 2013): 347–51. http://dx.doi.org/10.1609/icaps.v23i1.13605.

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Counterexample-guided abstraction refinement (CEGAR) is a method for incrementally computing abstractions of transition systems. We propose a CEGAR algorithm for computing abstraction heuristics for optimal classical planning. Starting from a coarse abstraction of the planning task, we iteratively compute an optimal abstract solution, check if and why it fails for the concrete planning task and refine the abstraction so that the same failure cannot occur in future iterations. A key ingredient of our approach is a novel class of abstractions for classical planning tasks that admits efficient and very fine-grained refinement. Our implementation performs tens of thousands of refinement steps in a few minutes and produces heuristics that are often significantly more accurate than pattern database heuristics of the same size.

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19

Anand, Ankit, Ritesh Noothigattu, Mausam ., and Parag Singla. "OGA-UCT: On-the-Go Abstractions in UCT." Proceedings of the International Conference on Automated Planning and Scheduling 26 (March 30, 2016): 29–37. http://dx.doi.org/10.1609/icaps.v26i1.13745.

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Recent work has begun exploring the value of domain abstractions in Monte-Carlo Tree Search (MCTS) algorithms for probabilistic planning. These algorithms automatically aggregate symmetric search nodes (states or state-action pairs) saving valuable planning time. Existing algorithms alternate between two phases: (1) abstraction computation forcomputing node aggregations, and (2) modified MCTS that use aggregate nodes. We believe that these algorithms do not achieve the full potential of abstractions because of disjoint phases – e.g., it can take a while to recover from erroneous abstractions, or compute better abstractions based on newly found knowledge.In response, we propose On-the-Go Abstractions (OGA), a novel approach in which abstraction computation is tightlyintegrated into the MCTS algorithm. We implement these on top of UCT and name the resulting algorithm OGA-UCT.It has several desirable properties, including (1) rapid use of new information in modifying existing abstractions, (2) elimination of the expensive batch abstraction computationphase, and (3) focusing abstraction computation on important part of the sampled search space. We experimentally compare OGA-UCT against ASAP-UCT, a recent state-of-the-art MDP algorithm as well as vanilla UCT algorithm. We find that OGA-UCT is robust across a suite of planning competition and other MDP domains, and obtains up to 28 % quality improvements.

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20

Barsalou, Lawrence W. "Abstraction in perceptual symbol systems." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 358, no. 1435 (July 29, 2003): 1177–87. http://dx.doi.org/10.1098/rstb.2003.1319.

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After reviewing six senses of abstraction, this article focuses on abstractions that take the form of summary representations. Three central properties of these abstractions are established: (i) type–token interpretation; (ii) structured representation; and (iii) dynamic realization. Traditional theories of representation handle interpretation and structure well but are not sufficiently dynamical. Conversely, connectionist theories are exquisitely dynamic but have problems with structure. Perceptual symbol systems offer an approach that implements all three properties naturally. Within this framework, a loose collection of property and relation simulators develops to represent abstractions. Type–token interpretation results from binding a property simulator to a region of a perceived or simulated category member. Structured representation results from binding a configuration of property and relation simulators to multiple regions in an integrated manner. Dynamic realization results from applying different subsets of property and relation simulators to category members on different occasions. From this standpoint, there are no permanent or complete abstractions of a category in memory. Instead, abstraction is the skill to construct temporary online interpretations of a category's members. Although an infinite number of abstractions are possible, attractors develop for habitual approaches to interpretation. This approach provides new ways of thinking about abstraction phenomena in categorization, inference, background knowledge and learning.

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21

Turner, Raymond. "Computational Abstraction." Entropy 23, no. 2 (February 10, 2021): 213. http://dx.doi.org/10.3390/e23020213.

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Representation and abstraction are two of the fundamental concepts of computer science. Together they enable “high-level” programming: without abstraction programming would be tied to machine code; without a machine representation, it would be a pure mathematical exercise. Representation begins with an abstract structure and seeks to find a more concrete one. Abstraction does the reverse: it starts with concrete structures and abstracts away. While formal accounts of representation are easy to find, abstraction is a different matter. In this paper, we provide an analysis of data abstraction based upon some contemporary work in the philosophy of mathematics. The paper contains a mathematical account of how Frege’s approach to abstraction may be interpreted, modified, extended and imported into type theory. We argue that representation and abstraction, while mathematical siblings, are philosophically quite different. A case of special interest concerns the abstract/physical interface which houses both the physical representation of abstract structures and the abstraction of physical systems.

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22

Ackerman, Bruce. "Liberating Abstraction." University of Chicago Law Review 59, no. 1 (1992): 317. http://dx.doi.org/10.2307/1599939.

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23

Loughery, John. "Archival Abstraction." Hudson Review 54, no. 1 (2001): 117. http://dx.doi.org/10.2307/3852830.

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24

Teskey, Gordon. "Bent Abstraction." ELH 88, no. 2 (2021): 315–41. http://dx.doi.org/10.1353/elh.2021.0014.

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25

Fine, Kit. "Cantorian Abstraction." Journal of Philosophy 95, no. 12 (1998): 599–634. http://dx.doi.org/10.5840/jphil1998951230.

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26

MARCEAU, Jean-Claude. "Abstraction etEinfühlung." Pleine Marge 38 (December 1, 2003): 27–44. http://dx.doi.org/10.2143/pm.38.0.565746.

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27

Pappas, Andrea. "HAUNTED ABSTRACTION." Journal of Modern Jewish Studies 6, no. 2 (July 2007): 167–83. http://dx.doi.org/10.1080/14725880701423055.

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28

WOOTTON, DAVID. "Beyond abstraction." Art Book 1, no. 4 (September 1994): 5. http://dx.doi.org/10.1111/j.1467-8357.1994.tb00168.x.

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29

Overy, Paul. "PURE ABSTRACTION." Art History 16, no. 1 (March 1993): 190–95. http://dx.doi.org/10.1111/j.1467-8365.1993.tb00520.x.

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30

Abbott, Russell J. "Knowledge abstraction." Communications of the ACM 30, no. 8 (August 1987): 664–71. http://dx.doi.org/10.1145/27651.27652.

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31

Franco, Ana M. "Geometric Abstraction." American Art 26, no. 2 (June 2012): 34–41. http://dx.doi.org/10.1086/667949.

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32

MLINKO, ANGE. "Corporate Abstraction." Critical Quarterly 51, no. 3 (October 2009): 117. http://dx.doi.org/10.1111/j.1467-8705.2009.01886.x.

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33

Bernstein, Charles. "Disfiguring Abstraction." Critical Inquiry 39, no. 3 (March 2013): 486–97. http://dx.doi.org/10.1086/670042.

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34

Singh, Gary. "Pure Abstraction." IEEE Computer Graphics and Applications 36, no. 6 (November 2016): 4–5. http://dx.doi.org/10.1109/mcg.2016.111.

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35

Boone, Worth, and Gualtiero Piccinini. "Mechanistic Abstraction." Philosophy of Science 83, no. 5 (December 2016): 686–97. http://dx.doi.org/10.1086/687855.

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36

Shields, Rob. "Liminal abstraction." Distinktion: Journal of Social Theory 20, no. 3 (May 27, 2019): 342–52. http://dx.doi.org/10.1080/1600910x.2019.1618358.

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Studd, J. P. "Abstraction Reconceived." British Journal for the Philosophy of Science 67, no. 2 (June 1, 2016): 579–615. http://dx.doi.org/10.1093/bjps/axu035.

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Stewart, Susan. "Abstraction set." Textual Practice 28, no. 7 (November 10, 2014): 1245–48. http://dx.doi.org/10.1080/0950236x.2014.965470.

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Henzinger, Thomas A., Ranjit Jhala, Rupak Majumdar, and Grégoire Sutre. "Lazy abstraction." ACM SIGPLAN Notices 37, no. 1 (January 2002): 58–70. http://dx.doi.org/10.1145/565816.503279.

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40

Cunningham, David. "Spacing Abstraction." Griffith Law Review 17, no. 2 (January 2008): 454–69. http://dx.doi.org/10.1080/10383619.2008.10854619.

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41

Truong, Ba Tu, and Svetha Venkatesh. "Video abstraction." ACM Transactions on Multimedia Computing, Communications, and Applications 3, no. 1 (February 2007): 3. http://dx.doi.org/10.1145/1198302.1198305.

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42

Christopoulos, Arthur, Marianne K. O. Grant, and Esam E. El-Fakahany. "Transducer abstraction." Journal of Pharmacological and Toxicological Methods 43, no. 1 (February 2000): 55–67. http://dx.doi.org/10.1016/s1056-8719(00)00078-2.

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43

Hazzan, Orit. "Reducing abstraction." Journal of Mathematical Behavior 20, no. 2 (April 2001): 163–72. http://dx.doi.org/10.1016/s0732-3123(01)00067-0.

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44

Neumann, Gustaf. "Interpretational abstraction." Computers & Mathematics with Applications 21, no. 8 (1991): 65–78. http://dx.doi.org/10.1016/0898-1221(91)90052-6.

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45

Gacek, Andrew, Dale Miller, and Gopalan Nadathur. "Nominal abstraction." Information and Computation 209, no. 1 (January 2011): 48–73. http://dx.doi.org/10.1016/j.ic.2010.09.004.

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46

van Haeften, Chris. "Abstraction Revisited." Process Studies 34, no. 1 (2005): 81–97. http://dx.doi.org/10.5840/process200534124.

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47

Bate, David. "Daguerre’s Abstraction." Photographies 9, no. 2 (May 3, 2016): 135–46. http://dx.doi.org/10.1080/17540763.2016.1185881.

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48

van Haeften, Chris. "Abstraction Revisited." Process Studies 34, no. 1 (April 1, 2005): 81–97. http://dx.doi.org/10.2307/44797645.

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49

Litland, Jon Erling. "Collective Abstraction." Philosophical Review 131, no. 4 (October 1, 2022): 453–97. http://dx.doi.org/10.1215/00318108-10136830.

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This paper develops a novel theory of abstraction—what we call collective abstraction. The theory solves a notorious problem for noneliminative structuralism. The noneliminative structuralist holds that in addition to various isomorphic systems there is a pure structure that can be abstracted from each of these systems; but existing accounts of abstraction fail for nonrigid systems like the complex numbers. The problem with the existing accounts is that they attempt to define a unique abstraction operation. The theory of collective abstraction instead simultaneously defines a collection of distinct abstraction operations, each of which maps a system to its corresponding pure structure. The theory is precisely formulated in an essentialist language. This allows us to throw new light on the question to what extent structuralists are committed to symmetric dependence. Finally, we apply the theory of collective abstraction to solve a problem about converse relations.

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50

Smith, Jeanette M. "Abstraction: Windows." JAMA 313, no. 13 (April 7, 2015): 1298. http://dx.doi.org/10.1001/jama.2014.11667.

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