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Journal articles on the topic 'Computational reasoning'

Author: Grafiati

Published: 31 August 2024

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1

Dixon, Lucas, Ross Duncan, and Aleks Kissinger. "Open Graphs and Computational Reasoning." Electronic Proceedings in Theoretical Computer Science 26 (June 9, 2010): 169–80. http://dx.doi.org/10.4204/eptcs.26.16.

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2

Thompson, Errol. "Teaching Computational Reasoning Through Construals." Education & Self Development 13, no. 3 (September 30, 2018): 40–52. http://dx.doi.org/10.26907/esd13.3.05.

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3

Rayward-Smith, V. J., and A. Gammerman. "Computational Learning and Probabilistic Reasoning." Journal of the Operational Research Society 48, no. 7 (July 1997): 756. http://dx.doi.org/10.2307/3010065.

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4

Tsafnat, G., and E. W. Coiera. "Computational Reasoning across Multiple Models." Journal of the American Medical Informatics Association 16, no. 6 (August 28, 2009): 768–74. http://dx.doi.org/10.1197/jamia.m3023.

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5

Brown, A. G. P., and F. P. Coenen. "Spatial reasoning: improving computational efficiency." Automation in Construction 9, no. 4 (July 2000): 361–67. http://dx.doi.org/10.1016/s0926-5805(99)00019-9.

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6

Bliss, Joan, Jon Ogborn, Richard Boohan, Jonathan Briggs, Tim Brosnan, Derek Brough, Harvey Mellar, et al. "Reasoning supported by computational tools." Computers & Education 18, no. 1-3 (January 1992): 1–9. http://dx.doi.org/10.1016/0360-1315(92)90030-9.

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7

Guan, J. W., D. A. Bell, and Z. Guan. "Computational methods for evidential reasoning." Irish Journal of Psychology 14, no. 3 (January 1993): 508–9. http://dx.doi.org/10.1080/03033910.1993.10557960.

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8

Grass, Joshua. "Reasoning about computational resource allocation." XRDS: Crossroads, The ACM Magazine for Students 3, no. 1 (September 1996): 16–20. http://dx.doi.org/10.1145/332148.332154.

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9

Gammerman, A. "Computational Learning and Probabilistic Reasoning." Journal of the Operational Research Society 48, no. 7 (July 1997): 756–57. http://dx.doi.org/10.1057/palgrave.jors.2600381.

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Gammerman, A. "Computational Learning and Probabilistic Reasoning." Journal of the Operational Research Society 48, no. 7 (1997): 756. http://dx.doi.org/10.1038/sj.jors.2600381.

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11

Hall, Rogers P. "Computational approaches to analogical reasoning." Artificial Intelligence 39, no. 1 (May 1989): 39–120. http://dx.doi.org/10.1016/0004-3702(89)90003-9.

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12

ZHANG, YU. "The computational SLR: a logic for reasoning about computational indistinguishability." Mathematical Structures in Computer Science 20, no. 5 (October 2010): 951–75. http://dx.doi.org/10.1017/s0960129510000265.

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Computational indistinguishability is a notion in complexity-theoretic cryptography and is used to define many security criteria. However, in traditional cryptography, proving computational indistinguishability is usually informal and becomes error-prone when cryptographic constructions are complex. This paper presents a formal proof system based on an extension of Hofmann's SLR language, which can capture probabilistic polynomial-time computations through typing and is sufficient for expressing cryptographic constructions. In particular, we define rules that directly justify the computational indistinguishability between programs, and then prove that these rules are sound with respect to the set-theoretic semantics, and thus the standard definition of security. We also show that it is applicable in cryptography by verifying, in our proof system, Goldreich and Micali's construction of a pseudorandom generator, and the equivalence between next-bit unpredictability and pseudorandomness.

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13

Jha, Kunal, Tuan Anh Le, Chuanyang Jin, Yen-Ling Kuo, Joshua B. Tenenbaum, and Tianmin Shu. "Neural Amortized Inference for Nested Multi-Agent Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 1 (March 24, 2024): 530–37. http://dx.doi.org/10.1609/aaai.v38i1.27808.

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Multi-agent interactions, such as communication, teaching, and bluffing, often rely on higher-order social inference, i.e., understanding how others infer oneself. Such intricate reasoning can be effectively modeled through nested multi-agent reasoning. Nonetheless, the computational complexity escalates exponentially with each level of reasoning, posing a significant challenge. However, humans effortlessly perform complex social inferences as part of their daily lives. To bridge the gap between human-like inference capabilities and computational limitations, we propose a novel approach: leveraging neural networks to amortize high-order social inference, thereby expediting nested multi-agent reasoning. We evaluate our method in two challenging multi-agent interaction domains. The experimental results demonstrate that our method is computationally efficient while exhibiting minimal degradation in accuracy.

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14

Li, Yao, Li-yao Xia, and Stephanie Weirich. "Reasoning about the garden of forking paths." Proceedings of the ACM on Programming Languages 5, ICFP (August 22, 2021): 1–28. http://dx.doi.org/10.1145/3473585.

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Lazy evaluation is a powerful tool for functional programmers. It enables the concise expression of on-demand computation and a form of compositionality not available under other evaluation strategies. However, the stateful nature of lazy evaluation makes it hard to analyze a program's computational cost, either informally or formally. In this work, we present a novel and simple framework for formally reasoning about lazy computation costs based on a recent model of lazy evaluation: clairvoyant call-by-value. The key feature of our framework is its simplicity, as expressed by our definition of the clairvoyance monad. This monad is both simple to define (around 20 lines of Coq) and simple to reason about. We show that this monad can be effectively used to mechanically reason about the computational cost of lazy functional programs written in Coq.

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Mohammed, Abdul-Wahid, Yang Xu, Ming Liu, and Haixiao Hu. "Semantical Markov Logic Network for Distributed Reasoning in Cyber-Physical Systems." Journal of Sensors 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/4259652.

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The challenges associated with developing accurate models for cyber-physical systems are attributable to the intrinsic concurrent and heterogeneous computations of these systems. Even though reasoning based on interconnected domain specific ontologies shows promise in enhancing modularity and joint functionality modelling, it has become necessary to build interoperable cyber-physical systems due to the growing pervasiveness of these systems. In this paper, we propose a semantically oriented distributed reasoning architecture for cyber-physical systems. This model accomplishes reasoning through a combination of heterogeneous models of computation. Using the flexibility of semantic agents as a formal representation for heterogeneous computational platforms, we define autonomous and intelligent agent-based reasoning procedure for distributed cyber-physical systems. Sensor networks underpin the semantic capabilities of this architecture, and semantic reasoning based on Markov logic networks is adopted to address uncertainty in modelling. To illustrate feasibility of this approach, we present a Markov logic based semantic event model for cyber-physical systems and discuss a case study of event handling and processing in a smart home.

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16

Li, Bo, and Qinping Zhao. "A computational model of analogical reasoning." Science in China Series E: Technological Sciences 40, no. 2 (April 1997): 214–24. http://dx.doi.org/10.1007/bf02916955.

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17

Barker-Plummer, Dave, and John Etchemendy. "A computational architecture for heterogeneous reasoning." Journal of Experimental & Theoretical Artificial Intelligence 19, no. 3 (September 2007): 195–225. http://dx.doi.org/10.1080/09528130701475401.

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18

Gopal, Тadepalli. "Learning Computational Logic through Geometric Reasoning." Innovative STEM Education 5, no. 1 (July 24, 2023): 7–12. http://dx.doi.org/10.55630/stem.2023.0501.

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Computers control everyday things ranging from the heart pacemakers to voice controlled devices that form an integral part of many appliances. Failures related to computers regularly cause disruption, damage and occasionally death. Computational logic establishes the facts in a logical formalism. It attempts to understand the nature of mathematical reasoning with a wide variety of formalisms, techniques and technologies. Formal verification uses mathematical and logical formalisms to prove the correctness of designs. Formal methods provide the maturity and agility to assimilate the future concepts, languages, techniques and tools for computational methods and models. The quest for simplification of formal verification is never ending. This summary report advocates the use of geometry to construct quick conclusions by the human mind that can be formally verified if necessary.

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19

Yang, Yuan. "Visual Abstract Reasoning in Computational Imagery." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 2024): 23431–32. http://dx.doi.org/10.1609/aaai.v38i21.30416.

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Despite current AI’s human-like behavior, super efficiency, and unbelievable ability to handle complex games, we still complain that it shows no sign of creativity, originality, or novelty outside its training set, and that it fails to develop new insights into old experience or establish understanding of new experience. In short, it generates content from its training set, but does not invent content. A fundamental reason for this is that current AI is incapable of abstraction and reasoning in an abstract, generalizable, and systematic way. Think, for instance, of what AI systems we can build if we have a base system that can answer this simple question—when two things are the same. Instead of studying these high-level questions, I put my thesis in the context of visual abstract reasoning (VAR), a task widely used in human intelligence tests. A classical example of this task is Raven’s Progressive Matrices (RPM, see Figure 1), a family of intelligence tests that was designed to measure eductive ability, i.e., the ability to make meaning out of confusion and generate high-level, usually nonverbal, schemata which make it easy to handle complexity. A similar concept to eductive ability is fluid intelligence, or the ability to discriminate and perceive complex relationships when no recourse to answers is stored in memory. Whether eductive ability or fluid intelligence, RPM points to the qualities that have been lacking in AI. To explore these qualities in AI, I propose the following research questions.

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20

Eger, Markus, Camille Barot, and R. Young. "Merits of a Temporal Modal Logic for Narrative Discourse Generation." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 11, no. 4 (June 24, 2021): 23–29. http://dx.doi.org/10.1609/aiide.v11i4.12836.

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Just as there exists varied uses for computational models of narrative, there exists a wide variety of languages aimed at representing stories. A number of them have historic roots in automated generation, for which these languages have to be limited in order to make the generation process computationally feasible. Other are focused on story understanding, with close ties to natural language making many reasoning processes computationally intractable. In this paper, we discuss the trade-off between expressivity and computational complexity of the reasoning process and argue that Impulse, a temporal, modal logic provides more expressivity than languages historically associated with story generation, while still affording reasoning capabilities. We show that these properties enable certain aspects of narrative discourse generation by using two examples from different genres, and claim that this generalizes to a broader class of problems.

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21

Zhang, Chi, Yilun Wang, Lili Zhang, and Huicheng Zhou. "A fuzzy inference method based on association rule analysis with application to river flood forecasting." Water Science and Technology 66, no. 10 (November 1, 2012): 2090–98. http://dx.doi.org/10.2166/wst.2012.420.

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In this paper, a computationally efficient version of the widely used Takagi-Sugeno (T-S) fuzzy reasoning method is proposed, and applied to river flood forecasting. It is well known that the number of fuzzy rules of traditional fuzzy reasoning methods exponentially increases as the number of input parameters increases, often causing prohibitive computational burden. The proposed method greatly reduces the number of fuzzy rules by making use of the association rule analysis on historical data, and therefore achieves computational efficiency for the cases of a large number of input parameters. In the end, we apply this new method to a case study of river flood forecasting, which demonstrates that the proposed fuzzy reasoning engine can achieve better prediction accuracy than the widely used Muskingum–Cunge scheme.

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22

ANTONIOU, GRIGORIS, and NEIL V. MURRAY. "Logical methods for computational intelligence." Knowledge Engineering Review 12, no. 4 (December 1997): 407–9. http://dx.doi.org/10.1017/s0269888997003056.

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Over the past years, two main approaches to computational intelligence have emerged: the symbolic and the non-symbolic approach. The perhaps most prominent methods of the symbolic approach are based on logic. Logical methods exhibit a series of desirable properties:[bull ] Transparent representation of meaning[bull ] Precise understanding of the meaning of statements (semantics).[bull ] Sound reasoning methods.[bull ] Explanation capabilities.A special session on logical methods for computational intelligence was held at the 3rd Joint Conference on Information Sciences. The field of computational logic is so broad that it is impossible to review the main developments in an article. Therefore, in the following we will restrict attention to two areas that turned out to be the focus of the special session: automated reasoning, and reasoning with incomplete and changing information.

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23

Wallace, Iain, and Michael Rovatsos. "A Computational Framework for Practical Social Reasoning." Computational Intelligence 31, no. 1 (August 16, 2013): 69–105. http://dx.doi.org/10.1111/coin.12014.

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24

Shu-Rong, Jiang, Mi Ju-Sheng, and Ma Li. "Computational reasoning based on complemented distributive lattices." International Journal of Machine Learning and Cybernetics 6, no. 3 (June 17, 2014): 475–78. http://dx.doi.org/10.1007/s13042-014-0274-9.

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25

Nebel, Bernhard. "Computational complexity of terminological reasoning in BACK." Artificial Intelligence 34, no. 3 (April 1988): 371–83. http://dx.doi.org/10.1016/0004-3702(88)90066-5.

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26

SCHUT, MARTIJN, and MICHAEL WOOLDRIDGE. "The control of reasoning in resource-bounded agents." Knowledge Engineering Review 16, no. 3 (September 2001): 215–40. http://dx.doi.org/10.1017/s0269888901000157.

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Autonomous agents are systems capable of autonomous decision-making in real-time environments. Computation is a valuable resource for such decision-making, and yet the amount of computation that an autonomous agent may carry out will be limited. It follows that an agent must be equipped with a mechanism that enables it to make the best possible use of the computational resources at its disposal. In this paper we review three approaches to the control of computation in resource-bounded agents. In addition to a detailed description of each framework, this paper compares and contrasts the approaches, and lists the advantages and disadvantages of each.

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27

Yip, K., and F. Zhao. "Spatial Aggregation: Theory and Applications." Journal of Artificial Intelligence Research 5 (August 1, 1996): 1–26. http://dx.doi.org/10.1613/jair.315.

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Visual thinking plays an important role in scientific reasoning. Based on the research in automating diverse reasoning tasks about dynamical systems, nonlinear controllers, kinematic mechanisms, and fluid motion, we have identified a style of visual thinking, imagistic reasoning. Imagistic reasoning organizes computations around image-like, analogue representations so that perceptual and symbolic operations can be brought to bear to infer structure and behavior. Programs incorporating imagistic reasoning have been shown to perform at an expert level in domains that defy current analytic or numerical methods. We have developed a computational paradigm, spatial aggregation, to unify the description of a class of imagistic problem solvers. A program written in this paradigm has the following properties. It takes a continuous field and optional objective functions as input, and produces high-level descriptions of structure, behavior, or control actions. It computes a multi-layer of intermediate representations, called spatial aggregates, by forming equivalence classes and adjacency relations. It employs a small set of generic operators such as aggregation, classification, and localization to perform bidirectional mapping between the information-rich field and successively more abstract spatial aggregates. It uses a data structure, the neighborhood graph, as a common interface to modularize computations. To illustrate our theory, we describe the computational structure of three implemented problem solvers -- KAM, MAPS, and HIPAIR --- in terms of the spatial aggregation generic operators by mixing and matching a library of commonly used routines.

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28

Jenkins, Craig W. "Classroom Talk and Computational Thinking." International Journal of Computer Science Education in Schools 1, no. 4 (October 31, 2017): 3–13. http://dx.doi.org/10.21585/ijcses.v1i4.15.

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This paper is part of a wider action research project taking place at a secondary school in South Wales, UK.Â The overarching aim of the project is to examine the potential for aspects of literacy and computational thinking to be developed using extensible â€˜build your own blockâ€™ programming activities.Â This paper examines classroom talk at an extracurricular programming club and focuses in particular on dialogue relating to computational thinking.Â Learners spent a number of weeks carrying out an activity designed using the Snap programming tool.Â The activity was themed around language and the task was to devise a collection of fixed-form poetry.The findings are in two parts.Â First is a dialogue analysis using the SEDA coding scheme.Â This analysis revealed a number of learner interactions showing evidence of reasoning.Â Second, examples of talk sequences are provided in order to examine how the reasoning identified in the interactions relate to what we may recognise as computational thinking.Â The paper concludes by considering how dialogic approaches in the classroom potentially have an important role to play in the process of teaching young people to think computationally.

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29

Sadiku, Matthew N. O., Justin Foreman, and Sarhan M. Musa. "Computational Intelligence." European Scientific Journal, ESJ 14, no. 21 (July 31, 2018): 56. http://dx.doi.org/10.19044/esj.2018.v14n21p56.

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Computational intelligence (CI) refers to recreating human-like intelligence in a computing machine. It consists of a set of computing systems with the ability to learn and deal with new situations such that the systems are perceived to have some attributes of intelligence. It is efficient in solving realworld problems which require reasoning and decision-making. It produces more robust, simpler, and tractable solutions than the traditional techniques. This paper provides a brief introduction to computational intelligence.

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Lehtonen, Tuomo, Johannes P. Wallner, and Matti Järvisalo. "Declarative Algorithms and Complexity Results for Assumption-Based Argumentation." Journal of Artificial Intelligence Research 71 (June 23, 2021): 265–318. http://dx.doi.org/10.1613/jair.1.12479.

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The study of computational models for argumentation is a vibrant area of artificial intelligence and, in particular, knowledge representation and reasoning research. Arguments most often have an intrinsic structure made explicit through derivations from more basic structures. Computational models for structured argumentation enable making the internal structure of arguments explicit. Assumption-based argumentation (ABA) is a central structured formalism for argumentation in AI. In this article, we make both algorithmic and complexity-theoretic advances in the study of ABA. In terms of algorithms, we propose a new approach to reasoning in a commonly studied fragment of ABA (namely the logic programming fragment) with and without preferences. While previous approaches to reasoning over ABA frameworks apply either specialized algorithms or translate ABA reasoning to reasoning over abstract argumentation frameworks, we develop a direct declarative approach to ABA reasoning by encoding ABA reasoning tasks in answer set programming. We show via an extensive empirical evaluation that our approach significantly improves on the empirical performance of current ABA reasoning systems. In terms of computational complexity, while the complexity of reasoning over ABA frameworks is well-understood, the complexity of reasoning in the ABA+ formalism integrating preferences into ABA is currently not fully established. Towards bridging this gap, our results suggest that the integration of preferential information into ABA via so-called reverse attacks results in increased problem complexity for several central argumentation semantics.

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31

Badillo Romero, Jeisson Hernan. "Ludic-pedagogical strategies for the strengthening of mathematical logical reasoning." Universidad Ciencia y Tecnología 26, no. 114 (June 30, 2022): 162–69. http://dx.doi.org/10.47460/uct.v26i114.601.

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Currently, the branches of mathematics are in the worst performance and more in the logical reasoning part, being a fundamental part of computational development. This work focuses on intertwined activities that are part of a deep and detailed process that aims to strengthen logical and mathematical reasoning through the development of computational thinking among primary school teachers. The methodology used has an ethnographic design with aqualitative approach, which is based on a diagnosis through a survey and an interview that help plan training workshops in basic computational thinking skills through digital platforms. The main results show that it is necessary to create transversality in the area of mathematics and computer science, to create the need to strengthen mathematical logical reasoning in the different grades of primary school, and to improve the teaching processes in terms of technological tools within the classroom. Keywords: computational thinking, mathematical logical reasoning, problem-solving

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32

Skowron, Andrzej, Andrzej Jankowski, and Soma Dutta. "Interactive granular computing." Granular Computing 1, no. 2 (January 5, 2016): 95–113. http://dx.doi.org/10.1007/s41066-015-0002-1.

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Abstract Decision support in solving problems related to complex systems requires relevant computation models for the agents as well as methods for reasoning on properties of computations performed by agents. Agents are performing computations on complex objects [e.g., (behavioral) patterns, classifiers, clusters, structural objects, sets of rules, aggregation operations, (approximate) reasoning schemes]. In Granular Computing (GrC), all such constructed and/or induced objects are called granules. To model interactive computations performed by agents, crucial for the complex systems, we extend the existing GrC approach to Interactive Granular Computing (IGrC) approach by introducing complex granules (c-granules or granules, for short). Many advanced tasks, concerning complex systems, may be classified as control tasks performed by agents aiming at achieving the high-quality computational trajectories relative to the considered quality measures defined over the trajectories. Here, new challenges are to develop strategies to control, predict, and bound the behavior of the system. We propose to investigate these challenges using the IGrC framework. The reasoning, which aims at controlling of computations, to achieve the required targets, is called an adaptive judgement. This reasoning deals with granules and computations over them. Adaptive judgement is more than a mixture of reasoning based on deduction, induction and abduction. Due to the uncertainty the agents generally cannot predict exactly the results of actions (or plans). Moreover, the approximations of the complex vague concepts initiating actions (or plans) are drifting with time. Hence, adaptive strategies for evolving approximations of concepts are needed. In particular, the adaptive judgement is very much needed in the efficiency management of granular computations, carried out by agents, for risk assessment, risk treatment, and cost/benefit analysis. In the paper, we emphasize the role of the rough set-based methods in IGrC. The discussed approach is a step towards realization of the Wisdom Technology (WisTech) program, and is developed over years, based on the work experience on different real-life projects.

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33

Ball, Linden J., and Jeremy D. Quayle. "Alternative task construals, computational escape hatches, and dual-system theories of reasoning." Behavioral and Brain Sciences 23, no. 5 (October 2000): 667–68. http://dx.doi.org/10.1017/s0140525x00243434.

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Stanovich & West's dual-system represents a major development in an understanding of reasoning and rationality. Their notion of System 1 functioning as a computational escape hatch during the processing of complex tasks may deserve a more central role in explanations of reasoning performance. We describe examples of apparent escape-hatch processing from the reasoning and judgement literature.

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34

Panisson, Alison R., Peter McBurney, and Rafael H. Bordini. "A computational model of argumentation schemes for multi-agent systems." Argument & Computation 12, no. 3 (November 10, 2021): 357–95. http://dx.doi.org/10.3233/aac-210555.

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There are many benefits of using argumentation-based techniques in multi-agent systems, as clearly shown in the literature. Such benefits come not only from the expressiveness that argumentation-based techniques bring to agent communication but also from the reasoning and decision-making capabilities under conditions of conflicting and uncertain information that argumentation enables for autonomous agents. When developing multi-agent applications in which argumentation will be used to improve agent communication and reasoning, argumentation schemes (reasoning patterns for argumentation) are useful in addressing the requirements of the application domain in regards to argumentation (e.g., defining the scope in which argumentation will be used by agents in that particular application). In this work, we propose an argumentation framework that takes into account the particular structure of argumentation schemes at its core. This paper formally defines such a framework and experimentally evaluates its implementation for both argumentation-based reasoning and dialogues.

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35

Atkey, Robert. "Polynomial Time and Dependent Types." Proceedings of the ACM on Programming Languages 8, POPL (January 5, 2024): 2288–317. http://dx.doi.org/10.1145/3632918.

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We combine dependent types with linear type systems that soundly and completely capture polynomial time computation. We explore two systems for capturing polynomial time: one system that disallows construction of iterable data, and one, based on the LFPL system of Martin Hofmann, that controls construction via a payment method. Both of these are extended to full dependent types via Quantitative Type Theory, allowing for arbitrary computation in types alongside guaranteed polynomial time computation in terms. We prove the soundness of the systems using a realisability technique due to Dal Lago and Hofmann. Our long-term goal is to combine the extensional reasoning of type theory with intensional reasoning about the resources intrinsically consumed by programs. This paper is a step along this path, which we hope will lead both to practical systems for reasoning about programs’ resource usage, and to theoretical use as a form of synthetic computational complexity theory .

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36

Jianhua, Mao, Guo Qingsheng, and Wang Tao. "Computational complexity of spatial reasoning with directional relationship." Geo-spatial Information Science 5, no. 3 (January 2002): 53–57. http://dx.doi.org/10.1007/bf02826389.

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37

Baldwin, J. F. "Computational models of uncertainty reasoning in expert systems." Computers & Mathematics with Applications 19, no. 11 (1990): 105–19. http://dx.doi.org/10.1016/0898-1221(90)90153-b.

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38

Vreeswijk, Gerard A. W. "The computational value of debate in defeasible reasoning." Argumentation 9, no. 2 (May 1995): 305–42. http://dx.doi.org/10.1007/bf00721964.

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39

Parnafes, Orit, and Andrea Disessa. "Relations between Types of Reasoning and Computational Representations." International Journal of Computers for Mathematical Learning 9, no. 3 (September 2004): 251–80. http://dx.doi.org/10.1007/s10758-004-3794-7.

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40

Vardi, M. Y., and P. Wolper. "Reasoning about Infinite Computations." Information and Computation 115, no. 1 (November 1994): 1–37. http://dx.doi.org/10.1006/inco.1994.1092.

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41

Aliseda, Atocha. "Abductive Reasoning: Challenges Ahead." THEORIA 22, no. 3 (December 18, 2009): 261–70. http://dx.doi.org/10.1387/theoria.446.

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The purpose of this piece is to provide a critical analysis on some key aspects of abduction, as conceived by several researchers through my book Abductive Reasoning. These contributions raise fundamental questions concerning the conjectural character of abduction, its psychological status, its logical and computational structure as well as its role as inference to the best explanation and as a process of epistemic change.

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42

Kakas, A., P. Mancarella, F. Sadri, K. Stathis, and F. Toni. "Computational Logic Foundations of KGP Agents." Journal of Artificial Intelligence Research 33 (November 10, 2008): 285–348. http://dx.doi.org/10.1613/jair.2596.

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This paper presents the computational logic foundations of a model of agency called the KGP (Knowledge, Goals and Plan model. This model allows the specification of heterogeneous agents that can interact with each other, and can exhibit both proactive and reactive behaviour allowing them to function in dynamic environments by adjusting their goals and plans when changes happen in such environments. KGP provides a highly modular agent architecture that integrates a collection of reasoning and physical capabilities, synthesised within transitions that update the agent's state in response to reasoning, sensing and acting. Transitions are orchestrated by cycle theories that specify the order in which transitions are executed while taking into account the dynamic context and agent preferences, as well as selection operators for providing inputs to transitions.

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43

Wang, Kai, Yuwei Xu, and Siqiang Luo. "TIGER: Training Inductive Graph Neural Network for Large-Scale Knowledge Graph Reasoning." Proceedings of the VLDB Endowment 17, no. 10 (June 2024): 2459–72. http://dx.doi.org/10.14778/3675034.3675039.

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Knowledge Graph (KG) Reasoning plays a vital role in various applications by predicting missing facts from existing knowledge. Inductive KG reasoning approaches based on Graph Neural Networks (GNNs) have shown impressive performance, particularly when reasoning with unseen entities and dynamic KGs. However, such state-of-the-art KG reasoning approaches encounter efficiency and scalability challenges on large-scale KGs due to the high computational costs associated with subgraph extraction - a key component in inductive KG reasoning. To address the computational challenge, we introduce TIGER, an inductive GNN training framework tailored for large-scale KG reasoning. TIGER employs a novel, efficient streaming procedure that facilitates rapid subgraph slicing and dynamic subgraph caching to minimize the cost of subgraph extraction. The fundamental challenge in TIGER lies in the optimal subgraph slicing problem, which we prove to be NP-hard. We propose a novel two-stage algorithm SiGMa to solve the problem practically. By decoupling the complicated problem into two classical ones, SiGMa achieves low computational complexity and high slice reuse. We also propose four new benchmarks for robust evaluation of large-scale inductive KG reasoning, the biggest of which performs on the Freebase KG (encompassing 86M entities, 285M edges). Through comprehensive experiments on state-of-the-art GNN-based KG reasoning models, we demonstrate that TIGER significantly reduces the running time of subgraph extraction, achieving an average 3.7× speedup relative to the basic training procedure.

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ASCIA, G., and V. CATANIA. "AN EFFICIENT HARDWARE ARCHITECTURE TO SUPPORT COMPLEX FUZZY REASONING." International Journal on Artificial Intelligence Tools 05, no. 01n02 (June 1996): 41–60. http://dx.doi.org/10.1142/s0218213096000043.

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The paper presents the design of a VLSI fuzzy processor which is capable of supporting complex fuzzy reasoning. The architecture of the processor is based on a appropriate computational model, whose main features are: capability to cope with rule chaining; pre-processing of inferences to reduce the number of rules to be processed; parallel computation of the degree of activation of the rules; optimized representation of membership function. The processor performance is in the order of 1.5 MFLIPS (256 rule, 8 Fuzzy inputs, 4 output).

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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 (March 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 interleaved with belief updating. Our technique invalidates the posterior beliefs of those nodes that depend probabilistically on the new evidence or the revised part of the model and focuses the subsequent belief updating on the invalidated beliefs rather than on all beliefs. Very often observations and model updating invalidate only a small fraction of the beliefs and our scheme can then lead to sub stantial savings in computation. We report results of empirical tests for incremental belief updating when the evidence gathering is interleaved with reasoning. These tests demonstrate the practical significance of our approach.

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Maes, Pattie. "Computational reflection." Knowledge Engineering Review 3, no. 1 (March 1988): 1–19. http://dx.doi.org/10.1017/s0269888900004355.

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AbstractComputational reflection is the activity performed by a computational System when reasoning about (and by that possibly affecting) itself. This paper presents an introduction to computational reflection (thereafter called reflection). A definition of reflection is presented, its utility for knowledge engineering is discussed and architectures of languages that support it are studied. Examples of such procedural, logic-based, rule-based and object-oriented languages are presented. The paper elaborates on the design of these languages and the reflective functionality that results, elucidating concepts such as procedural reflection, declarative reflection, theory relativity of reflection, etc. The paper concludes with an assessment of outstanding problems and future developments in the area.

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TIN, ERKAN, and VAROL AKMAN. "COMPUTING WITH CAUSAL THEORIES." International Journal of Pattern Recognition and Artificial Intelligence 06, no. 04 (October 1992): 699–730. http://dx.doi.org/10.1142/s0218001492000369.

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Formalizing commonsense knowledge for reasoning about time has long been a central issue in AI. It has been recognized that the existing formalisms do not provide satisfactory solutions to some fundamental problems, viz. the frame problem. Moreover, it has turned out that the inferences drawn do not always coincide with those one had intended when one wrote the axioms. These issues call for a well-defined formalism and useful computational utilities for reasoning about time and change. Yoav Shoham of Stanford University introduced in his 1986 Yale doctoral thesis an appealing temporal nonmonotonic logic and identified a class of theories, causal theories, which have computationally simple model-theoretic properties. This paper is a study towards building upon Shoham's work on causal theories. We concentrate on improving computational aspects of causal theories while preserving their model-theoretic properties.

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Eiter, Thomas, and Rafael Kiesel. "Semiring Reasoning Frameworks in AI and Their Computational Complexity." Journal of Artificial Intelligence Research 77 (May 31, 2023): 207–93. http://dx.doi.org/10.1613/jair.1.13970.

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Many important problems in AI, among them #SAT, parameter learning and probabilistic inference go beyond the classical satisfiability problem. Here, instead of finding a solution we are interested in a quantity associated with the set of solutions, such as the number of solutions, the optimal solution or the probability that a query holds in a solution. To model such quantitative problems in a uniform manner, a number of frameworks, e.g. Algebraic Model Counting and Semiring-based Constraint Satisfaction Problems, employ what we call the semiring paradigm. In the latter the abstract algebraic structure of the semiring serves as a means of parameterizing the problem definition, thus allowing for different modes of quantitative computations by choosing different semirings. While efficiently solvable cases have been widely studied, a systematic study of the computational complexity of such problems depending on the semiring parameter is missing. In this work, we characterize the latter by NP(R), a novel generalization of NP over semiring R, and obtain NP(R)-completeness results for a selection of semiring frameworks. To obtain more tangible insights into the hardness of NP(R), we link it to well-known complexity classes from the literature. Interestingly, we manage to connect the computational hardness to properties of the semiring. Using this insight, we see that, on the one hand, NP(R) is always at least as hard as NP or ModpP depending on the semiring R and in general unlikely to be in FPSPACEpoly. On the other hand, for broad subclasses of semirings relevant in practice we can employ reductions to NP, ModpP and #P. These results show that in many cases solutions are only mildly harder to compute than functions in NP, ModpP and #P, give us new insights into how problems that involve counting on semirings can be approached, and provide a means of assessing whether an algorithm is appropriate for a given class of problems.

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Alberti, Marco, Massimiliano Cattafi, Federico Chesani, Marco Gavanelli, Evelina Lamma, Paola Mello, Marco Montali, and Paolo Torroni. "A Computational Logic Application Framework for Service Discovery and Contracting." International Journal of Web Services Research 8, no. 3 (July 2011): 1–25. http://dx.doi.org/10.4018/jwsr.2011070101.

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In Semantic Web technologies, searching for a service means identifying components that can potentially satisfy user needs in terms of inputs and outputs (discovery) and devise a fruitful interaction with the customer (contracting). In this paper, the authors present an application framework that encompasses both the discovery and the contracting steps in a unified search process. In particular, the authors accommodate service discovery by ontology-based reasoning and contracting by reasoning about behavioural interfaces, published in a formal language. To this purpose, the authors consider a formal approach grounded on Computational Logic. They define, illustrate, and evaluate a framework, called SCIFF Reasoning Engine (SRE), which can establish if a Semantic Web Service and a requester can fruitfully inter-operate, by computing a possible interaction plan based on the behavioural interfaces of both. The same operational machinery used for contracting can be used for runtime verification.

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Sciavicco, Guido. "Reasoning with Time Intervals: A Logical and Computational Perspective." ISRN Artificial Intelligence 2012 (October 14, 2012): 1–19. http://dx.doi.org/10.5402/2012/616087.

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The role of time in artificial intelligence is extremely important. Interval-based temporal reasoning can be seen as a generalization of the classical point-based one, and the first results in this field date back to Hamblin (1972) and Benhtem (1991) from the philosophical point of view, to Allen (1983) from the algebraic and first-order one, and to Halpern and Shoham (1991) from the modal logic one. Without purporting to provide a comprehensive survey of the field, we take the reader to a journey through the main developments in modal and first-order interval temporal reasoning over the past ten years and outline some landmark results on expressiveness and (un)decidability of the satisfiability problem for the family of modal interval logics.

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