Relevant bibliographies by topics / Disjunctive inference

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Academic literature on the topic 'Disjunctive inference'

Author: Grafiati
Published: 14 March 2023

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Journal articles on the topic "Disjunctive inference"

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Milne, Peter. "Disjunction and Disjunctive Syllogism." Canadian Journal of Philosophy 28, no. 1 (March 1998): 21–32. http://dx.doi.org/10.1080/00455091.1998.10715969.

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The validity of argument by disjunctive syllogism (henceforth, DS) has been denied by proponents of relevant and paraconsistent logic (who are sometimes one and the same). DS is stigmatised for its role in inferences — most notably C.I. Lewis's derivation of that fallacy of irrelevance ex falso quodlibet (EFQ) — that involve both it and other rules of inference governing disjunction, or, to speak more precisely, other rules of inference taken to apply to the very same disjunction that obeys DS. In avoiding these inferences the road less travelled is to deny the identity rather than to deny DS: what follows is, then, an exercise in disjoining disjunctions.
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Booth, Richard, and Ivan Varzinczak. "Conditional Inference under Disjunctive Rationality." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 7 (May 18, 2021): 6227–34. http://dx.doi.org/10.1609/aaai.v35i7.16774.

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The question of conditional inference, i.e., of which conditional sentences of the form ``if A then, normally, B'' should follow from a set KB of such sentences, has been one of the classic questions of AI, with several well-known solutions proposed. Perhaps the most notable is the rational closure construction of Lehmann and Magidor, under which the set of inferred conditionals forms a rational consequence relation, i.e., satisfies all the rules of preferential reasoning, *plus* Rational Monotonicity. However, this last named rule is not universally accepted, and other researchers have advocated working within the larger class of *disjunctive* consequence relations, which satisfy the weaker requirement of Disjunctive Rationality. While there are convincing arguments that the rational closure forms the ``simplest'' rational consequence relation extending a given set of conditionals, the question of what is the simplest *disjunctive* consequence relation has not been explored. In this paper, we propose a solution to this question and explore some of its properties.
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Ekramnia, Milad, Jacques Mehler, and Ghislaine Dehaene-Lambertz. "Disjunctive inference in preverbal infants." iScience 25, no. 2 (February 2022): 103817. http://dx.doi.org/10.1016/j.isci.2022.103817.

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Ekramnia, Milad, Jacques Mehler, and Ghislaine Dehaene-Lambertz. "Disjunctive inference in preverbal infants." iScience 24, no. 10 (October 2021): 103203. http://dx.doi.org/10.1016/j.isci.2021.103203.

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Santorio, Paolo. "Simplification is not Scalar Strengthening." Semantics and Linguistic Theory 30 (March 2, 2021): 624. http://dx.doi.org/10.3765/salt.v30i0.4856.

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We show that Simplification of Disjunctive antecedents is not a scalar inference. The argument exploits information-sensitive modals, like epistemic "probably" and deliberative "ought". When items of this sort are the main modal of a conditional, we can have that: (i) If A or B, Mod C is true; (ii) the basic meaning computed via classical semantics for conditionals and disjunction is false. This combination is impossible on any scalar account of Simplification: scalar inferences are strengthenings, hence the output of scalar inferences must entail the basic meaning of a sentence. We suggest an account of Simplification based on alternative semantics, and show how this account can be made compatible with old and new counterexamples to Simplification.
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Ferrigno, Stephen, Yiyun Huang, and Jessica F. Cantlon. "Reasoning Through the Disjunctive Syllogism in Monkeys." Psychological Science 32, no. 2 (January 25, 2021): 292–300. http://dx.doi.org/10.1177/0956797620971653.

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The capacity for logical inference is a critical aspect of human learning, reasoning, and decision-making. One important logical inference is the disjunctive syllogism: given A or B, if not A, then B. Although the explicit formation of this logic requires symbolic thought, previous work has shown that nonhuman animals are capable of reasoning by exclusion, one aspect of the disjunctive syllogism (e.g., not A = avoid empty). However, it is unknown whether nonhuman animals are capable of the deductive aspects of a disjunctive syllogism (the dependent relation between A and B and the inference that “if not A, then B” must be true). Here, we used a food-choice task to test whether monkeys can reason through an entire disjunctive syllogism. Our results show that monkeys do have this capacity. Therefore, the capacity is not unique to humans and does not require language.
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Shen, Yi-Dong, and Thomas Eiter. "Determining inference semantics for disjunctive logic programs." Artificial Intelligence 277 (December 2019): 103165. http://dx.doi.org/10.1016/j.artint.2019.103165.

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Wong, K. "Sound and Complete Inference Rules for SE-Consequence." Journal of Artificial Intelligence Research 31 (January 31, 2008): 205–16. http://dx.doi.org/10.1613/jair.2472.

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The notion of strong equivalence on logic programs with answer set semantics gives rise to a consequence relation on logic program rules, called SE-consequence. We present a sound and complete set of inference rules for SE-consequence on disjunctive logic programs.
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ROBLES, GEMMA, and JOSÉ M. MÉNDEZ. "PARACONSISTENT LOGICS INCLUDED IN LEWIS’ S4." Review of Symbolic Logic 3, no. 3 (July 23, 2010): 442–66. http://dx.doi.org/10.1017/s1755020310000109.

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As is known, a logic S is paraconsistent if the rule ECQ (E contradictione quodlibet) is not a rule of S. Not less well known is the fact that Lewis’ modal logics are not paraconsistent. Actually, Lewis vindicates the validity of ECQ in a famous proof currently known as the “Lewis’ proof” or “Lewis’ argument.” This proof essentially leans on the Disjunctive Syllogism as a rule of inference. The aim of this paper is to define a series of paraconsistent logics included in S4 where the Disjunctive Syllogism is valid only as a rule of proof.
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GRECO, GIANLUIGI, SERGIO GRECO, IRINA TRUBITSYNA, and ESTER ZUMPANO. "Optimization of bound disjunctive queries with constraints." Theory and Practice of Logic Programming 5, no. 6 (October 31, 2005): 713–45. http://dx.doi.org/10.1017/s1471068404002273.

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This paper presents a technique for the optimization of bound queries over disjunctive deductive databases with constraints. The proposed approach is an extension of the well-known Magic-Set technique and is well-suited for being integrated in current bottom-up (stable) model inference engines. More specifically, it is based on the exploitation of binding propagation techniques which reduce the size of the data relevant to answer the query and, consequently, reduces both the complexity of computing a single model and the number of models to be considered. The motivation of this work stems from the observation that traditional binding propagation optimization techniques for bottom-up model generator systems, simulating the goal driven evaluation of top-down engines, are only suitable for positive (disjunctive) queries, while hard problems are expressed using unstratified negation. The main contribution of the paper consists in the extension of a previous technique, defined for positive disjunctive queries, to queries containing both disjunctive heads and constraints (a simple and expressive form of unstratified negation). As the usual way of expressing declaratively hard problems is based on the guess-and-check technique, where the guess part is expressed by means of disjunctive rules and the check part is expressed by means of constraints, the technique proposed here is highly relevant for the optimization of queries expressing hard problems. The value of the technique has been proved by several experiments.
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Dissertations / Theses on the topic "Disjunctive inference"

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Wu, Chuang. "Phenotype Inference from Genotype in RNA Viruses." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/457.

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The phenotype inference from genotype in RNA viruses maps the viral genome/protein sequences to the molecular functions in order to understand the underlying molecular mechanisms that are responsible for the function changes. The inference is currently done through a laborious experimental process which is arguably inefficient, incomplete, and unreliable. The wealth of RNA virus sequence data in the presence of different phenotypes promotes the rise of computational approaches to aid the inference. Key residue identification and genotype-phenotype mapping function learning are two approaches to identify the critical positions out of hitchhikers and elucidate the relations among them. The existing computational approaches in this area focus on prediction accuracy, yet a number of fundamental problems have not been considered: the scalability of the data, the capability to suggest informative biological experiments, and the interpretability of the inferences. A common scenario of inference done by biologists with mutagenesis experiments usually involves a small number of available sequences, which is very likely to be inadequate for the inference in most setups. Accordingly biologists desire models that are capable of inferring from such limited data, and algorithms that are capable of suggesting new experiments when more data is needed. Another important but always been neglected property of the models is the interpretability of the mapping, since most existing models behave as ’black boxes’. To address these issues, in the thesis I design a supervised combinatorial filtering algorithm that systematically and efficiently infers the correct set of key residue positions from available labeled data. For cases where more data is needed to fully converge to an answer, I introduce an active learning algorithm to help choose the most informative experiment from a set of unlabeled candidate strains or mutagenesis experiments to minimize the expected total laboratory time or financial cost. I also propose Disjunctive Normal Form (DNF) as an appropriate assumption over the hypothesis space to learn interpretable genotype-phenotype functions. The challenges of these approaches are the computational efficiency due to the combinatorial nature of our algorithms. The solution is to explore biological plausible assumptions to constrain the solution space and efficiently find the optimal solutions under the assumptions. The algorithms were validated in two ways: 1) prediction quality in a cross-validation manner, and 2) consistency with the domain experts’ conclusions. The algorithms also suggested new discoveries that have not been discussed yet. I applied these approaches to a variety of RNA virus datasets covering the majority of interesting RNA phenotypes, including drug resistance, Antigenicity shift, Antibody neutralization and so on to demonstrate the prediction power, and suggest new discoveries of Influenza drug resistance and Antigenicity. I also prove the extension of the approaches in the area of severe acute community disease.
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Ekramnia, Milad. "Investigating Two Domain-General Processes in Early Infancy: Disjunctive Inference and Reorientation of Attention." Doctoral thesis, SISSA, 2016. http://hdl.handle.net/20.500.11767/3918.

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A characteristic of the more evolved nervous systems is the ability to process information in an abstract amodal domain. The existence of this capability, necessitates the presence of mental processes that are amodal and therefore, can act on a broad range of internal and external stimuli. Investigating the early development of the interaction between the amodal mental processes and their domain of action on mental representations, can shed light on the extents of the computations that can be accommodated by these processes. In this thesis through a series of eye-tracking studies in pre-verbal infants, we attempted to investigate the early development of some of these interactions from two different domains. In one domain, we addressed if logical operators, as a subset of the mental processes, are available to pre-verbal infants; so they can be utilized in combining and assessing the several mental images involved in an inference process. For this purpose, we introduced a face-voice association paradigm, in which infants could potentially use disjunctive inference to disambiguate the context and make the right face-voice pairings. We showed that the performance of the 10-month-old infants suggests that they might be able to perform this association through the process of disjunctive inference based on the elimination of the incorrect alternative. We furthermore, used the pupillometry data and results from an adult control group to suggest a time-frame for the steps of this process. In another domain, we studied the integration of abstract visual icons with attentional shift. In one hand we showed that arrows can trigger an attentional shift in the 4-month-old infants but not 8-month-olds. We further showed that this reorientation of attention might be due to the triangular area of the icon. These striking results, although should await further confirmations, suggest an early sensitivity to the features of these icons, which can trigger a top-down reorientation of attention (as we tried to eliminate the possibility of a bottom-up process). A sensitivity that possibly disappears later in the development. On the other hand, we showed that 8-month-olds and not 4-month-olds can assign an attentional shift to an arbitrary icon in a very few number of trials. These results together suggest a mixed picture for attribution of attentional shift to the icons; however indicating that a volitional attribution of attention to arbitrary icons can be carried out by infants as young as 8 months of age.
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Books on the topic "Disjunctive inference"

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Lassiter, Daniel. Scalar goodness. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198701347.003.0007.

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This chapter turns to deontic concepts. I argue that goodness is an interval scale, and consider two interactions with disjunction that would enforce the validity of the Disjunctive Inference: maximality (à la Lewis and Kratzer) and intermediacy. I identify a number of empirically problematic consequences of maximality, concluding that goodness is intermediate: a disjunction can be strictly worse than one of the disjuncts. I propose, as one way to flesh out the scale further, that goodness has the formal structure of expected value, and show that this proposal makes intuitively reasonable predictions about the puzzle cases for maximality as well as a wide variety of instances in which probabilistic information influences the relative goodness of outcomes. Finally, I discuss several possible schemata for the interpretation of the positive form good in light of the sensitivity of this item to prosodic focus and the non-synonymy of its positive and superlative forms.
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How to Learn Natural Deduction Inference Rules : Biconditional Elimination and Disjunction Introduction: Workbooklet 3. 2. Independently Published, 2022.

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Book chapters on the topic "Disjunctive inference"

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Hughes, G. E., and D. G. Londey. "Disjunction and Disjunctive Inference." In The Elements of Formal Logic, 32–36. Routledge, 2019. http://dx.doi.org/10.4324/9780367854126-5.

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Williamson, Timothy. "7. Deducing." In Philosophical Method: A Very Short Introduction, 74–88. Oxford University Press, 2020. http://dx.doi.org/10.1093/actrade/9780198810001.003.0007.

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Detective work is an important tool in philosophy. ‘Deducing’ explains the difference between valid and sound arguments. An argument is valid if its premises are true but is only sound if the conclusion is true. The Greek philosophers identified disjunctive syllogism—the idea that if something is not one thing, it must be another. This relates to another philosophical concept, the ‘law of the excluded middle’. An abduction is a form of logical inference which attempts to find the most likely explanation. Modal logic, an extension of classical logic, is a popular branch of logic for philosophical arguments.
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Zakrevskij, Arkadij. "Integrated Model of Inductive-Deductive Inference Based on Finite Predicates and Implicative Regularities." In Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems, 1–12. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-1900-5.ch001.

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The theory of Boolean functions, especially in respect to representing these functions in the disjunctive or conjunctive normal forms, is extended in this chapter onto the case of finite predicates. Finite predicates are decomposed by that into some binary units, which will correspond to components of Boolean vectors and matrices and are represented as combinations of these units. Further, the main concepts used for solving pattern recognition problems are defined, namely world model, data, and knowledge. The data presenting information about the existence of some objects with definite combinations of properties is considered, as well as the knowledge presenting information about the existence of regular relationships between attributes. These relationships prohibit some combinations of properties. In this way, the knowledge gives the information about the non-existence of objects with some definite (prohibited) combinations of attribute values. A special form of regularity representation, called implicative regularities, is introduced. Any implicative regularity generates an empty interval in the Boolean space of object descriptions, which do not contradict the data. The problem of plausibility evaluation of induced implicative regularities should be solved by that. The pattern recognition problem is solved by two steps. First, regularities are extracted from the database (inductive inference); second, the obtained knowledge is used for the object recognition (deductive inference).
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"Disjunctive Logic Programming as Constrained Inferences." In Logic Programming. The MIT Press, 1997. http://dx.doi.org/10.7551/mitpress/4299.003.0031.

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Wolf, A. "Alternative (or Disjunctive) Propositions and Inferences." In Textbook of Logic, 109–14. Routledge, 2019. http://dx.doi.org/10.4324/9780429030970-14.

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Wolf, A. "Alternative (or Disjunctive) Propositions and Inferences." In Essentials of Logic, 113–20. Routledge, 2019. http://dx.doi.org/10.4324/9780429054389-13.

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JOFFE, MICHAEL. "What Would a Scientific Economics Look Like?" In Evidence, Inference and Enquiry. British Academy, 2011. http://dx.doi.org/10.5871/bacad/9780197264843.003.0016.

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This chapter compares biology with the practices of economics to determine the extent to which mainstream economic theory can be regarded as ‘scientific’. This survey of practice in the two disciplines shows that biological theory is derived from description and experimentation. In contrast, the ideal in mainstream economics is to derive theory from axioms. When economic theory is compared with the available evidence, a disjunction is found between the empirical findings and conventional theory. The disjunction is explained by a fundamental mismatch between the evidence and the basic theoretical categories and structure. The central issue is the relationship of evidence to theory: to put it simply, which should come first? The conclusion is that regularities that emerge from a comparative historical perspective, including use of econometric, statistical, and qualitative studies, could provide the type of evidence that could form a secure foundation for theorising in economics.
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Berto, Francesco, and Mark Jago. "The Logic of Imagination." In Impossible Worlds, 141–58. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198812791.003.0007.

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Imagination seems to have a logic, albeit one which is hyperintensional and sensitive to context. This chapter offers a semantics of imagination, with operators expressing ‘imaginative acts’ of mental simulation. A number of conditions that could be imposed on the semantics are then discussed, in order to validate certain inferences. One important issue is how acts of imagination interact with disjunction: one can imagine some disjunction as obtaining without being imaginatively specific about which disjunction obtains. This chapter subsequently turns to non-monotonicity: how B may follow from imagining that A, but not from imagining that A ∧ C. Finally, the Principle of Imaginative Equivalents is discussed.
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Berto, Francesco. "Topic-Sensitive Intentional Modals." In Topics of Thought, 60–84. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192857491.003.0003.

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Abstract This chapter introduces two-place Topic-Sensitive Intentional Modals (TSIMs): variably strict, topic-sensitive modal operators of the form ‘Xφψ’, which are to represent attitude ascriptions—generic reading: ‘One Xs (thinks, believes, imagines, etc.) that ψ, given φ’. It presents a basic possible worlds semantics for a simple propositional language including them. The key twofold clause, giving the truth conditions for the TSIMs, has it that, for Xφψ to be true, we require (1) that ψ be true throughout a set of worlds, selected via an accessibility relation or function indexed to φ; and (2) that ψ be fully on-topic with respect to φ. (1) makes of the TSIM variably strict modal operators, that is, variably strict quantifiers over possible worlds. (2) makes them topic-sensitive. The semantics validates various inferences involving conjunction; invalidates various inferences involving disjunction; and makes the TSIM operators non-monotonic (in particular, Xφψ does not entail Xφ&Xψ) and hyperintensional: capable of differentiating between necessarily or intensionally equivalent contents. Such validities and invalidities are defended for interpretations of X that range across a variety of attitudes. Finally, the chapter introduces a number of constraints on the selection functions. These will give us stronger logics for the Xφψs, as well as different plausible readings for them, to be explored in the three following chapters.
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Conference papers on the topic "Disjunctive inference"

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Shen, Yi-Dong, and Thomas Eiter. "Determining Inference Semantics for Disjunctive Logic Programs (Extended Abstract)." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/703.

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[Gelfond and Lifschitz, 1991] introduced simple disjunctive logic programs and defined the answer set semantics called GL-semantics. We observed that the requirement of GL-semantics, i.e., an answer set should be a minimal model of the GL-reduct may be too strong and exclude some answer sets that would be reasonably acceptable. To address this, we present a novel and more permissive semantics, called determining inference semantics.
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Takai, Shigemasa, and Ratnesh Kumar. "A generalized inference-based diagnosis framework for discrete event systems capturing both disjunctive and conjunctive decision-making." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7402764.

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Xu, Zhe, and Ufuk Topcu. "Transfer of Temporal Logic Formulas in Reinforcement Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/557.

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Transferring high-level knowledge from a source task to a target task is an effective way to expedite reinforcement learning (RL). For example, propositional logic and first-order logic have been used as representations of such knowledge. We study the transfer of knowledge between tasks in which the timing of the events matters. We call such tasks temporal tasks. We concretize similarity between temporal tasks through a notion of logical transferability, and develop a transfer learning approach between different yet similar temporal tasks. We first propose an inference technique to extract metric interval temporal logic (MITL) formulas in sequential disjunctive normal form from labeled trajectories collected in RL of the two tasks. If logical transferability is identified through this inference, we construct a timed automaton for each sequential conjunctive subformula of the inferred MITL formulas from both tasks. We perform RL on the extended state which includes the locations and clock valuations of the timed automata for the source task. We then establish mappings between the corresponding components (clocks, locations, etc.) of the timed automata from the two tasks, and transfer the extended Q-functions based on the established mappings. Finally, we perform RL on the extended state for the target task, starting with the transferred extended Q-functions. Our implementation results show, depending on how similar the source task and the target task are, that the sampling efficiency for the target task can be improved by up to one order of magnitude by performing RL in the extended state space, and further improved by up to another order of magnitude using the transferred extended Q-functions.
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