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Academic literature on the topic 'Generative Reasoning'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 June 2025

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Journal articles on the topic "Generative Reasoning"

1

Jacobson, Maxwell, and Yexiang Xue. "Integrating Symbolic Reasoning into Neural Generative Models for Design Generation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 27 (2025): 28741. https://doi.org/10.1609/aaai.v39i27.35132.

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Design generation requires tight integration of neural and symbolic reasoning, as good design must meet explicit user needs and honor implicit rules for aesthetics, utility, and convenience. Current automated design tools driven by neural networks produce appealing designs, but cannot satisfy user specifications and utility requirements. Symbolic reasoning tools, such as constraint programming, cannot perceive low-level visual information in images or capture subtle aspects such as aesthetics. We introduce Spatial Reasoning Integrated Generator (SPRING) for design generation. SPRING embeds a neural and symbolic integrated spatial reasoning module inside the deep generative network. The spatial reasoning module samples the set of locations of objects to be generated from a backtrack-free distribution. This distribution modifies the implicit preference distribution, which is learned by a recursive neural network to capture utility and aesthetics. Sampling from the backtrack-free distribution is accomplished by a symbolic reasoning approach, SampleSearch, which zeros out the probability of sampling spatial locations violating explicit user specifications. Embedding symbolic reasoning into neural generation guarantees that the output of SPRING satisfies user requirements. Furthermore, SPRING offers interpretability, allowing users to visualize and diagnose the generation process through the bounding boxes. SPRING also handles novel user specifications not encountered during its training with zero-shot constraint transfer. Quantitative evaluations and a human study show that SPRING outperforms baseline generative models, delivering high design quality and better meeting user specifications.

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

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

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Nafar, Aliakbar, Kristen Brent Venable, and Parisa Kordjamshidi. "Reasoning over Uncertain Text by Generative Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 24911–20. https://doi.org/10.1609/aaai.v39i23.34674.

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This paper considers the challenges Large Language Models (LLMs) face when reasoning over text that includes information involving uncertainty explicitly quantified via probability values. This type of reasoning is relevant to a variety of contexts ranging from everyday conversations to medical decision-making. Despite improvements in the mathematical reasoning capabilities of LLMs, they still exhibit significant difficulties when it comes to probabilistic reasoning. To deal with this problem, we introduce the Bayesian Linguistic Inference Dataset (BLInD), a new dataset specifically designed to test the probabilistic reasoning capabilities of LLMs. We use BLInD to find out the limitations of LLMs for tasks involving probabilistic reasoning. In addition, we present several prompting strategies that map the problem to different formal representations, including Python code, probabilistic algorithms, and probabilistic logical programming. We conclude by providing an evaluation of our methods on BLInD and an adaptation of a causal reasoning question-answering dataset. Our empirical results highlight the effectiveness of our proposed strategies for multiple LLMs.

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Fashali, Agju Jihan Indri, Agus Susanta, and Saleh Haji. "PENGARUH PENGGUNAAN MODEL GENERATIVE LEARNING TERHADAP PEMAHAMAN KONSEP DAN PENALARAN MATEMATIKA PESERTA DIDIK KELAS VIII SMP 14 BENGKULU TENGAH." Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika 5, no. 3 (2024): 1883–97. https://doi.org/10.46306/lb.v5i3.779.

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The capacity to comprehend concepts and reasoning in students when addressing mathematical problems is notably low, resulting in a significant number of students who struggle to independently tackle practice problems.This study aims to examine 1) the effect of the Generative Learning Model on the conceptual understanding abilities of eighth-grade students at SMPN 14 Bengkulu Tengah, 2) the effect of the Generative Learning Model on the mathematical reasoning abilities of eighth-grade students at SMPN 14 Bengkulu Tengah, and 3) the effect of the Generative Learning Model on both the conceptual understanding and mathematical reasoning abilities of eighth-grade students at SMPN 14 Bengkulu Tengah. The sampling technique employed is nonprobability sampling, specifically utilizing saturated sampling (census). The data analysis method applied in this study is the MANOVA technique. The findings indicate that 1) the Generative Learning Model significantly influences the conceptual understanding abilities of eighth-grade students at SMPN 14 Bengkulu Tengah, with a significance value of > 0.05, 2) the Generative Learning Model significantly influences the mathematical reasoning abilities of eighth-grade students at SMPN 14 Bengkulu Tengah, with a significance value of > 0.05, and 3) the Generative Learning Model significantly influences both the conceptual understanding and mathematical reasoning abilities of eighth-grade students at SMPN 14 Bengkulu Tengah, with a significance value of > 0.05

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Mo, Zhenchong, Lin Gong, Mingren Zhu, and Junde Lan. "The Generative Generic-Field Design Method Based on Design Cognition and Knowledge Reasoning." Sustainability 16, no. 22 (2024): 9841. http://dx.doi.org/10.3390/su16229841.

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Large language model (LLM) and Crowd Intelligent Innovation (CII) are reshaping the field of engineering design and becoming a new design context. Generative generic-field design can solve more general design problems innovatively by integrating multi-domain design knowledge. However, there is a lack of knowledge representation and design process model in line with the design cognition of the new context. It is urgent to develop generative generic-field design methods to improve the feasibility, innovation, and empathy of design results. This study proposes a method based on design cognition and knowledge reasoning. Firstly, through the problem formulation, a generative universal domain design framework and knowledge base are constructed. Secondly, the knowledge-based discrete physical structure set generation method and system architecture generation method are proposed. Finally, the application tool Intelligent Design Assistant (IDA) is developed, verified, and discussed through an engineering design case. According to the design results and discussion, the design scheme is feasible and reflects empathy for the fuzzy original design requirements. Therefore, the method proposed in this paper is an effective technical scheme of generative generic-field engineering design in line with the design cognition in the new context.

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Liu, Ye, Yao Wan, Lifang He, Hao Peng, and Philip S. Yu. "KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 7 (2021): 6418–25. http://dx.doi.org/10.1609/aaai.v35i7.16796.

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Generative commonsense reasoning which aims to empower machines to generate sentences with the capacity of reasoning over a set of concepts is a critical bottleneck for text generation. Even the state-of-the-art pre-trained language generation models struggle at this task and often produce implausible and anomalous sentences. One reason is that they rarely consider incorporating the knowledge graph which can provide rich relational information among the commonsense concepts. To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graph augmented pre-trained language generation model KG-BART, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output. Moreover, KG-BART can leverage the graph attention to aggregate the rich concept semantics that enhances the model generalization on unseen concept sets. Experiments on benchmark CommonGen dataset verify the effectiveness of our proposed approach by comparing with several strong pre-trained language generation models, particularly KG-BART outperforms BART by 5.80, 4.60, in terms of BLEU-3, 4. Moreover, we also show that the generated context by our model can work as background scenarios to benefit downstream commonsense QA tasks.

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Knollmüller, Jakob, and Torsten A. Enßlin. "Bayesian Reasoning with Trained Neural Networks." Entropy 23, no. 6 (2021): 693. http://dx.doi.org/10.3390/e23060693.

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We showed how to use trained neural networks to perform Bayesian reasoning in order to solve tasks outside their initial scope. Deep generative models provide prior knowledge, and classification/regression networks impose constraints. The tasks at hand were formulated as Bayesian inference problems, which we approximately solved through variational or sampling techniques. The approach built on top of already trained networks, and the addressable questions grew super-exponentially with the number of available networks. In its simplest form, the approach yielded conditional generative models. However, multiple simultaneous constraints constitute elaborate questions. We compared the approach to specifically trained generators, showed how to solve riddles, and demonstrated its compatibility with state-of-the-art architectures.

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Mulyanti, Yanti. "Korelasi antara Kemampuan Pemahaman Konsep dan Penalaran Induktif Siswa melalui Pendekatan Generatif." Journal of Mathematics Learning 1, no. 2 (2018): 37–41. http://dx.doi.org/10.30653/004.201812.20.

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The ability of students’ conceptual understanding and inductive reasoning in mathematics learning needs to be considered, because conceptual understanding can organize and consolidate mathematical thinking, as well as reasoning will gain meaningful knowledge for students. This can be done through learning by using generative approach. This research is a correlational research to know the correlation between the ability of conceptual understanding and inductive reasoning of VII grade students at SMP Negeri 3 Cugenang-Cianjur in 2009/2010 Academic Year on triangle and quadrilateral materials, using generative approach. Based on the results of correlation analysis, it was obtained rxy = 0.618 in moderate correlation, and it can be concluded that there is a positive correlation between the ability ofstudents’ conceptual understanding and inductive reasoning.

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Rodman, Adam, and Eric J. Topol. "Is generative artificial intelligence capable of clinical reasoning?" Lancet 405, no. 10480 (2025): 689. https://doi.org/10.1016/s0140-6736(25)00348-4.

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Komanduri, Aneesh. "Toward Causal Generative Modeling: From Representation to Generation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29275–76. https://doi.org/10.1609/aaai.v39i28.35215.

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Deep learning has given rise to the field of representation learning, which aims to automatically extract rich semantics from data. However, there have been several challenges in the generalization capabilities of deep learning models. Recent works have highlighted beneficial properties of causal models that are desirable for learning robust models under distribution shifts. Thus, there has been a growing interest in causal representation learning for achieving generalizability in tasks involving reasoning and planning. The goal of my dissertation is to develop theoretical intuitions and practical algorithms that uncover the nature of causal representations and their applications. In my work, I focus on causal generative modeling with an emphasis on either representation or generation. For representation learning, I investigate the disentanglement of causal representations through the lens of independent causal mechanisms. For generation tasks, I develop algorithms for counterfactual generation under weak supervision settings by leveraging recent advances in generative modeling. The proposed approaches have been empirically shown to be effective in achieving disentanglement and generating counterfactuals.

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Dissertations / Theses on the topic "Generative Reasoning"

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Griffith, Todd W. "A computational theory of generative modeling in scientific reasoning." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/8177.

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Zhang, Yu. "Topological reasoning using a generative representation and a genetic algorithm." Thesis, Cardiff University, 2009. http://orca.cf.ac.uk/54999/.

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This thesis studies the use of a generative representation with a genetic algorithm (GA) to solve topological reasoning problems. Literature review indicates that generative representations outperform the non-generative ones for certain design optimisation and automation problems. However, it also indicates a lack of understanding of this relatively new class of representations. Many problems and questions about the implementation of generative representations are still to be addressed and answered. The results and findings presented in this thesis contribute to the knowledge of generative representations by: 1. explaining why genotype formatting is important for the representation and how it influences the performance of both the representation and the algorithm 2. providing different crossover and mutation methods, including both existing and newly developed ones, that are available to GA when used with the presentation and, more importantly, revealing their different properties in generating new individuals 3. providing alternative ways to map turtle graphs into the design space to form the actual designs and showing the properties of these different mapping methods and how they influence the outcome of the search. In general, this thesis examines the key issues in setting up and implementing generative representations with genetic algorithms. It improves the understanding of generative representations and contributes to the knowledge that is required to further develop them for real-world use. Based on the results and findings of this study, directions for future work are also provided.

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Misino, Eleonora. "Deep Generative Models with Probabilistic Logic Priors." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24058/.

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Many different extensions of the VAE framework have been introduced in the past. How ever, the vast majority of them focused on pure sub-symbolic approaches that are not sufficient for solving generative tasks that require a form of reasoning. In this thesis, we propose the probabilistic logic VAE (PLVAE), a neuro-symbolic deep generative model that combines the representational power of VAEs with the reasoning ability of probabilistic logic programming. The strength of PLVAE resides in its probabilistic logic prior, which provides an interpretable structure to the latent space that can be easily changed in order to apply the model to different scenarios. We provide empirical results of our approach by training PLVAE on a base task and then using the same model to generalize to novel tasks that involve reasoning with the same set of symbols.

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Southard, Katelyn M. "Exploring Features of Expertise and Knowledge Building among Undergraduate Students in Molecular and Cellular Biology." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/612137.

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Experts in the field of molecular and cellular biology (MCB) use domain-specific reasoning strategies to navigate the unique complexities of the phenomena they study and creatively explore problems in their fields. One primary goal of instruction in undergraduate MCB is to foster the development of these domain-specific reasoning strategies among students. However, decades of evidence-based research and many national calls for undergraduate instructional reform have demonstrated that teaching and learning complex fields like MCB is difficult for instructors and learners alike. Therefore, how do students develop rich understandings of biological mechanisms? It is the aim of this dissertation work to explore features of expertise and knowledge building in undergraduate MCB by investigating knowledge organization and problem-solving strategies. Semi-structured clinical think-aloud interviews were conducted with introductory and upper-division students in MCB. Results suggest that students must sort ideas about molecular mechanism into appropriate mental categories, create connections using function-driven and mechanistic rather than associative reasoning, and create nested and overlapping ideas in order to build a nuanced network of biological ideas. Additionally, I characterize the observable components of generative multi-level mechanistic reasoning among undergraduate MCB students constructing explanations about in two novel problem-solving contexts. Results indicate that like MCB experts, students are functionally subdividing the overarching mechanism into functional modules, hypothesizing and instantiating plausible schema, and even flexibly consider the impact of mutations across ontological and biophysical levels. However "filling in" these more abstract schema with molecular mechanisms remains problematic for many students, with students instead employing a range of developing mechanistic strategies. Through this investigation of expertise and knowledge building, I characterize several of the ways in which knowledge integration and generative explanation building are productively constrained by domain-specific features, expand on several discovered barriers to productive knowledge organization and mechanistic explanation building, and suggest instructional implications for undergraduate learning.

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Langer, Tomáš. "Metafory a analogie v ekonomické vědě a vzdělávání." Doctoral thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-264278.

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Presented thesis explores the ground at the intersection of three topics: education, relational thinking and economics. Within the sphere of economic education it investigates the use of concepts known from (i) conceptual metaphor theory, (ii) psychological model of analogical reasoning, (iii) model of generative learning from educational psychology and (iv) existing research on use of metaphors and analogies in natural sciences education. Thesis shows the potential of metaphorical origin of economic terminology for teaching economic concepts and educational use of economic media content. At the same time it proposes a notation for visualizing metaphorical mappings between domains. It addresses economic interpretation from the viewpoint of the relationship between theoretical economic models and actual economic situations, as well as from the viewpoint of the relationship between mathematical structure of the model and its economic meaning. In the first case, it shows interpretative skill, being framed within the revised Bloom taxonomy, as a complex cognitive task, in the second case it develops model of economic interpretation of mathematical structures on the basis of psychological model of analogical reasoning. In both cases it formulates highlights students should know about the analogical nature of economic models. On the basis of the model of generative learning it develops a set of visual methaphors applicable in the introductory topics of microeconomics and macroeconomics and examines effects of their use in the classes of economics. By undertaking such research it initiates the exploration of paths leading in the directions suggested by the theoretical analysis.

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Nyrup, Rune. "Hypothesis generation and pursuit in scientific reasoning." Thesis, Durham University, 2017. http://etheses.dur.ac.uk/12200/.

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This thesis draws a distinction between (i) reasoning about which scientific hypothesis to accept, (ii) reasoning concerned with generating new hypotheses and (iii) reasoning about which hypothesis to pursue. I argue that (ii) and (iii) should be evaluated according to the same normative standard, namely whether the hypotheses generated/selected are pursuit worthy. A consequentialist account of pursuit worthiness is defended, based on C. S. Peirce’s notion of ‘abduction’ and the ‘economy of research’, and developed as a family of formal, decision-theoretic models. This account is then deployed to discuss four more specific topics concerning scientific reasoning. First, I defend an account according to which explanatory reasoning (including the ‘inference to the best explanation’) mainly provides reasons for pursuing hypotheses, and criticise empirical arguments for the view that it also provides reasons for acceptance. Second, I discuss a number of pursuit worthiness accounts of analogical reasoning in science, arguing that, in some cases, analogies allow scientists to transfer an already well-understood modelling framework to a new domain. Third, I discuss the use of analogies within archaeological theorising, arguing that the distinction between using analogies for acceptance, generation and pursuit is implicit in methodological discussions in archaeology. A philosophical analysis of these uses is presented. Fourth, diagnostic reasoning in medicine is analysed from the perspective of Peircean abduction, where the conception of abduction as strategic reasoning is shown to be particularly important.

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Townsend, Joseph Paul. "Artificial development of neural-symbolic networks." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15162.

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Artificial neural networks (ANNs) and logic programs have both been suggested as means of modelling human cognition. While ANNs are adaptable and relatively noise resistant, the information they represent is distributed across various neurons and is therefore difficult to interpret. On the contrary, symbolic systems such as logic programs are interpretable but less adaptable. Human cognition is performed in a network of biological neurons and yet is capable of representing symbols, and therefore an ideal model would combine the strengths of the two approaches. This is the goal of Neural-Symbolic Integration [4, 16, 21, 40], in which ANNs are used to produce interpretable, adaptable representations of logic programs and other symbolic models. One neural-symbolic model of reasoning is SHRUTI [89, 95], argued to exhibit biological plausibility in that it captures some aspects of real biological processes. SHRUTI's original developers also suggest that further biological plausibility can be ascribed to the fact that SHRUTI networks can be represented by a model of genetic development [96, 120]. The aims of this thesis are to support the claims of SHRUTI's developers by producing the first such genetic representation for SHRUTI networks and to explore biological plausibility further by investigating the evolvability of the proposed SHRUTI genome. The SHRUTI genome is developed and evolved using principles from Generative and Developmental Systems and Artificial Development [13, 105], in which genomes use indirect encoding to provide a set of instructions for the gradual development of the phenotype just as DNA does for biological organisms. This thesis presents genomes that develop SHRUTI representations of logical relations and episodic facts so that they are able to correctly answer questions on the knowledge they represent. The evolvability of the SHRUTI genomes is limited in that an evolutionary search was able to discover genomes for simple relational structures that did not include conjunction, but could not discover structures that enabled conjunctive relations or episodic facts to be learned. Experiments were performed to understand the SHRUTI fitness landscape and demonstrated that this landscape is unsuitable for navigation using an evolutionary search. Complex SHRUTI structures require that necessary substructures must be discovered in unison and not individually in order to yield a positive change in objective fitness that informs the evolutionary search of their discovery. The requirement for multiple substructures to be in place before fitness can be improved is probably owed to the localist representation of concepts and relations in SHRUTI. Therefore this thesis concludes by making a case for switching to more distributed representations as a possible means of improving evolvability in the future.

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Papacchini, Fabio. "Minimal model reasoning for modal logic." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/minimal-model-reasoning-for-modal-logic(dbfeb158-f719-4640-9cc9-92abd26bd83e).html.

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Model generation and minimal model generation are useful for tasks such as model checking, query answering and for debugging of logical specifications. Due to this variety of applications, several minimality criteria and model generation methods for classical logics have been studied. Minimal model generation for modal logics how ever did not receive the same attention from the research community. This thesis aims to fill this gap by investigating minimality criteria and designing minimal model generation procedures for all the sublogics of the multi-modal logic S5(m) and their extensions with universal modalities. All the procedures are minimal model sound and complete, in the sense that they generate all and only minimal models. The starting point of the investigation is the definition of a Herbrand semantics for modal logics on which a syntactic minimality criterion is devised. The syntactic nature of the minimality criterion allows for an efficient minimal model generation procedure, but, on the other hand, the resulting minimal models can be redundant or semantically non minimal with respect to each other. To overcome the syntactic limitations of the first minimality criterion, the thesis moves from minimal modal Herbrand models to semantic minimality criteria based on subset-simulation. At first, theoretical procedures for the generation of models minimal modulo subset-simulation are presented. These procedures for the generation of models minimal modulo subset-simulation are minimal model sound and complete, but they might not terminate. The minimality criterion and the procedures are then refined in such a way that termination can be ensured while preserving minimal model soundness and completeness.

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de, Leng Daniel. "Spatio-Temporal Stream Reasoning with Adaptive State Stream Generation." Licentiate thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138645.

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A lot of today's data is generated incrementally over time by a large variety of producers. This data ranges from quantitative sensor observations produced by robot systems to complex unstructured human-generated texts on social media. With data being so abundant, making sense of these streams of data through reasoning is challenging. Reasoning over streams is particularly relevant for autonomous robotic systems that operate in a physical environment. They commonly observe this environment through incremental observations, gradually refining information about their surroundings. This makes robust management of streaming data and its refinement an important problem. Many contemporary approaches to stream reasoning focus on the issue of querying data streams in order to generate higher-level information by relying on well-known database approaches. Other approaches apply logic-based reasoning techniques, which rarely consider the provenance of their symbolic interpretations. In this thesis, we integrate techniques for logic-based spatio-temporal stream reasoning with the adaptive generation of the state streams needed to do the reasoning over. This combination deals with both the challenge of reasoning over streaming data and the problem of robustly managing streaming data and its refinement. The main contributions of this thesis are (1) a logic-based spatio-temporal reasoning technique that combines temporal reasoning with qualitative spatial reasoning; (2) an adaptive reconfiguration procedure for generating and maintaining a data stream required to perform spatio-temporal stream reasoning over; and (3) integration of these two techniques into a stream reasoning framework. The proposed spatio-temporal stream reasoning technique is able to reason with intertemporal spatial relations by leveraging landmarks. Adaptive state stream generation allows the framework to adapt in situations in which the set of available streaming resources changes. Management of streaming resources is formalised in the DyKnow model, which introduces a configuration life-cycle to adaptively generate state streams. The DyKnow-ROS stream reasoning framework is a concrete realisation of this model that extends the Robot Operating System (ROS). DyKnow-ROS has been deployed on the SoftBank Robotics NAO platform to demonstrate the system's capabilities in the context of a case study on run-time adaptive reconfiguration. The results show that the proposed system – by combining reasoning over and reasoning about streams – can robustly perform spatio-temporal stream reasoning, even when the availability of streaming resources changes.<br><p>The series name <em>Linköping Studies in Science and Technology Licentiate Thesis</em> is inocorrect. The correct series name is <em>Linköping Studies in Science and Technology Thesis</em>.</p><br>NFFP6<br>CENIIT

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Ma, Liangjun, and Shouchuan Zhang. "Generating Fuzzy Rules For Case-based Classification." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-16444.

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As a technique to solve new problems based on previous successful cases, CBR represents significant prospects for improving the accuracy and effectiveness of unstructured decision-making problems. Similar problems have similar solutions is the main assumption. Utility oriented similarity modeling is gradually becoming an important direction for Case-based reasoning research. In this thesis, we propose a new way to represent the utility of case by using fuzzy rules. Our method could be considered as a new way to estimate case utility based on fuzzy rule based reasoning. We use modified WANG’s algorithm to generate a fuzzy if-then rule from a case pair instead of a single case. The fuzzy if-then rules have been identified as a powerful means to capture domain information for case utility approximation than traditional similarity measures based on feature weighting. The reason why we choose the WANG algorithm as the foundation is that it is a simpler and faster algorithm to generate if-then rules from examples. The generated fuzzy rules are utilized as a case matching mechanism to estimate the utility of the cases for a given problem. The given problem will be formed with each case in the case library into pairs which are treated as the inputs of fuzzy rules to determine whether or to which extent a known case is useful to the problem. One case has an estimated utility score to the given problem to help our system to make decision. The experiments on several data sets have showed the superiority of our method over traditional schemes, as well as the feasibility of learning fuzzy if-then rules from a small number of cases while still having good performances.

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Books on the topic "Generative Reasoning"

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Werthner, H. Qualitative reasoning: Modeling and the generation of behavior. Springer-Verlag, 1994.

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Werthner, Hannes. Qualitative Reasoning: Modeling and the Generation of Behavior. Springer Vienna, 1994.

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United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Knowledge-based reasoning in the Paladin tactical decision generation system. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1993.

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Chappell, Alan R. Knowledge-based reasoning in the Paladin Tactical Decision Generation System. Langley Research Center, 1993.

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United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Knowledge-based reasoning in the Paladin tactical decision generation system. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1993.

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United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Knowledge-based reasoning in the Paladin tactical decision generation system. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1993.

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Cornelissen, Joep Paul. Teleological reasoning and knowledge generation in marketing theory: Observations and recommendations. Business School, 2001.

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Cornelissen, Joep Paul. Teleological reasoning and knowledge generation in marketing theory: Observations and recommendations. Manchester Metropolitan University Business School, 2001.

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Group, IRIS, ed. Neural and intelligent systems integration: Fifth and sixth generation integrated reasoning information systems. Wiley, 1991.

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Sirotkin, Sergey, and Natal'ya Kel'chevskaya. Economic evaluation of investment projects. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1014648.

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The tutorial focuses on challenges of economic evaluation of investment projects. It provides both theoretical and methodological foundations of economic evaluation of investment projects and required a substantial mathematical reasoning. Lighted the economic substance of the investment structure of the investment project, commercial efficiency and financial marketability, and methods of evaluation of investment project risks. The material is presented using the normative legal documents, in particular the Tax code of the Russian Federation, Federal laws, accounting regulations and other sources and meets the requirements of Federal state educational standards of higher education of the last generation. For students, postgraduates and teachers of economic universities (departments), researchers and practitioners, experts in the field of investment activities of organizations.

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Book chapters on the topic "Generative Reasoning"

1

Jakobsen, David. "Reasoning with Generative AI." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-84457-7_10.

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Pattinson, Dirk, and Cláudia Nalon. "Non-iterative Modal Resolution Calculi." In Automated Reasoning. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63501-4_6.

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AbstractNon-monotonic modal logics are typically interpreted over neighbourhood frames. For unary operators, this is just a set of worlds, together with an endofunction on predicates (subsets of worlds). It is known that all systems of not necessarily monotonic modal logics that are axiomatised by formulae of modal rank at most one (non-iterative modal logics) are Kripke-complete over neighbourhood semantics. In this paper, we give a uniform construction to obtain complete resolution calculi for all non-iterative logics. We show completeness for generative calculi (where new clauses with new literals are added to the clause set) by means of a canonical model construction. We then define absorptive calculi (where new clauses are generated by generalised resolution rules) and establish completeness by translating between generative and absorptive calculi. Instances of our construction re-prove completeness for already known calculi, but also give rise to a number of previously unknown complete calculi.

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Bertolotti, Tommaso. "Generative and Demonstrative Experiments." In Model-Based Reasoning in Science and Technology. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37428-9_27.

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Wu, Pengcheng, and Karl Lieberherr. "Shadow Programming: Reasoning About Programs Using Lexical Join Point Information." In Generative Programming and Component Engineering. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11561347_11.

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Choi, Hyunhee, Hayun Lee, and Minjeong Lee. "Enhancement of Knowledge Concept Maps Using Deductive Reasoning with Educational Data." In Generative Intelligence and Intelligent Tutoring Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63028-6_9.

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Kucharavy, Andrei. "Fundamental Limitations of Generative LLMs." In Large Language Models in Cybersecurity. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54827-7_5.

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AbstractGiven the impressive performances of LLM-derived tools across a range of tasks considered all but impossible for computers until recently, the capabilities of LLMs seem limitless. However, there are some fundamental limitations to what they can or cannot do inherent to the current architecture of LLMs. I will attempt to review the most notable of them to give the reader an understanding of what architectural modifications will need to take place before a given problem is solved. Specifically, I discuss counterfactual generation, private information leakage, reasoning, limited attention span, dependence on the training dataset, bias, and non-normative language.

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Klauer, Karl Christoph, Thorsten Meiser, and Birgit Naumer. "Extending the Theory of Reasoning by Mental Models: Tests of New Predictions." In Generative Mental Processes and Cognitive Resources. Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-011-4373-8_2.

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Andrus, Brian, David Bar-El, Camille Msall, David Uttal, and Marcelo Worsley. "Minecraft as a Generative Platform for Analyzing and Practicing Spatial Reasoning." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57983-8_22.

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Marcu, Daniel, and Ana-Maria Popescu. "Towards Developing Probabilistic Generative Models for Reasoning with Natural Language Representations." In Computational Linguistics and Intelligent Text Processing. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30586-6_8.

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Wang, Sheng, Xiaoyin Chen, and Shengwu Xiong. "Rule Injection-Based Generative Adversarial Imitation Learning for Knowledge Graph Reasoning." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75768-7_27.

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Conference papers on the topic "Generative Reasoning"

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Cui, Wanqing, Keping Bi, Jiafeng Guo, and Xueqi Cheng. "MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning." In Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.69.

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Fu, Xiyan, and Anette Frank. "The Mystery of Compositional Generalization in Graph-based Generative Commonsense Reasoning." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.492.

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Xie, Yijie, Xiangfeng Luo, and Xinzhi Wang. "Combing formalized temporal knowledge and generative background knowledge for temporal knowledge graph reasoning." In Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024), edited by Feng Yin and Zehui Zhan. SPIE, 2024. http://dx.doi.org/10.1117/12.3045266.

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Li, Rongjie, Yu Wu, and Xuming He. "Learning by Correction: Efficient Tuning Task for Zero-Shot Generative Vision-Language Reasoning." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01275.

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Basu, Samyadeep, Shell Xu Hu, Maziar Sanjabi, Daniela Massiceti, and Soheil Feizi. "Distilling Knowledge from Text-to-Image Generative Models Improves Visio-Linguistic Reasoning in CLIP." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.351.

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Arora, Manali, Chirag Garg, and Deepanshu Mangla. "Validation of Generative Visual Solutions Using Prompt Engineering and Caption Based Visual Reasoning Models." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10968513.

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Grey, Stuart. "Enhancing Ethical Reasoning in Engineering Education through Student-Created Interactive Ethical Scenarios Using Generative AI." In 2025 IEEE Global Engineering Education Conference (EDUCON). IEEE, 2025. https://doi.org/10.1109/educon62633.2025.11016531.

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Shengyuan, Yan, and Zhong Xiuqin. "Geo-Qwen: A Geometry Problem-Solving Method Based on Generative Large Language Models and Heuristic Reasoning." In 2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2024. https://doi.org/10.1109/iccwamtip64812.2024.10873683.

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Coppolillo, Erica, Francesco Calimeri, Giuseppe Manco, Simona Perri, and Francesco Ricca. "LLASP: Fine-tuning Large Language Models for Answer Set Programming." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/78.

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Recently, Large Language Models (LLMs) have showcased their potential in various natural language processing tasks, including code generation. However, while significant progress has been made in adapting LLMs to generate code for several imperative programming languages and tasks, there remains a notable gap in their application to declarative formalisms, such as Answer Set Programming (ASP). In this paper, we move a step towards exploring the capabilities of LLMs for ASP code generation. First, we perform a systematic evaluation of several state-of-the-art LLMs. Despite their power in terms of number of parameters, training data and computational resources, empirical results demonstrate inadequate performances in generating correct ASP programs. Therefore, we propose LLASP, a fine-tuned lightweight model specifically trained to encode fundamental ASP program patterns. To this aim, we create an ad-hoc dataset covering a wide variety of fundamental problem specifications that can be encoded in ASP. Our experiments demonstrate that the quality of ASP programs generated by LLASP is remarkable. This holds true not only when compared to the non-fine-tuned counterpart but also when compared to the majority of eager LLM candidates, particularly from a semantic perspective. All the code and data used to perform the experiments are publicly available: https://github.com/EricaCoppolillo/LLASP.

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Lee, Kang-il, Hyukhun Koh, Dongryeol Lee, Seunghyun Yoon, Minsung Kim, and Kyomin Jung. "Generating Diverse Hypotheses for Inductive Reasoning." In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.naacl-long.429.

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Reports on the topic "Generative Reasoning"

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Coombs, Michael J., Roger T. Hartley, and Heather D. Pfeiffer. Assessment of Model Generative Reasoning for Use in the Intelligence Production Performance Model. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada238834.

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