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Showing papers on "Abductive reasoning published in 2020"


Proceedings Article
30 Apr 2020
TL;DR: For example, the authors investigate the feasibility of abductive reasoning in natural language inference and generation and show that the best model achieves 68.9% accuracy, well below human performance of 91.4%.
Abstract: Abductive reasoning is inference to the most plausible explanation. For example, if Jenny finds her house in a mess when she returns from work, and remembers that she left a window open, she can hypothesize that a thief broke into her house and caused the mess, as the most plausible explanation. While abduction has long been considered to be at the core of how people interpret and read between the lines in natural language (Hobbs et al., 1988), there has been relatively little research in support of abductive natural language inference and generation. We present the first study that investigates the viability of language-based abductive reasoning. We introduce a challenge dataset, ART, that consists of over 20k commonsense narrative contexts and 200k explanations. Based on this dataset, we conceptualize two new tasks – (i) Abductive NLI: a multiple-choice question answering task for choosing the more likely explanation, and (ii) Abductive NLG: a conditional generation task for explaining given observations in natural language. On Abductive NLI, the best model achieves 68.9% accuracy, well below human performance of 91.4%. On Abductive NLG, the current best language generators struggle even more, as they lack reasoning capabilities that are trivial for humans. Our analysis leads to new insights into the types of reasoning that deep pre-trained language models fail to perform—despite their strong performance on the related but more narrowly defined task of entailment NLI—pointing to interesting avenues for future research.

145 citations


Journal ArticleDOI
TL;DR: In this article, the authors explore how abductive reasoning is central to Mode 2 organization development and change (ODC) research, despite the rarity with which it is made explicit, and how it differentiates between P...
Abstract: This article explores how abductive reasoning is central to Mode 2 organization development and change (ODC) research, despite the rarity with which it is made explicit. It differentiates between P...

28 citations


Journal ArticleDOI
TL;DR: In this paper, the authors explore the potential of big data, such as those compiled by the Google Books project, to inform the dominant theories of the firm that tend to be grounded on strong assumptions about the capitalist nature of the modern society.

28 citations


Journal ArticleDOI
TL;DR: In this paper, the authors provide guidelines to guide the theorizing process that integrates general theoretic perspectives and contextual research to develop a midrange theory for service research, which is based on the philosophical foundations of pragmatism and abductive reasoning.
Abstract: For service research to develop as an applied social science there is the need to refresh the process of theorizing so it focuses not only on increasing new academic knowledge but also on knowledge that is managerially relevant. This paper aims to provide guidelines to achieve this.,A theorizing process that integrates general theoretic perspectives and contextual research to develop midrange theory is developed. The process is based on the philosophical foundations of pragmatism and abductive reasoning, which has the origins in the 1950s when the management sciences were being established.,A recent research stream that develops midrange theory about customer and actor engagement is used to illustrate the theorizing process.,Practicing managers, customers and other stakeholders in a service system use theory, so there is a need to focus on how theory is used in specific service contexts and how this research leads to academic knowledge that is managerially relevant. Thus, as applied social science, service research needs to explicitly focus on bridging the theory–praxis gap with midrange theory by incorporating a general theoretic perspective and contextual research.,The contribution comes from providing a broader framework to guide the theorizing process that integrates general theoretic perspectives and applied research to develop midrange theory. While general theories operate at the most abstract level of conceptualization, midrange theories are context-specific and applied theory (theories-in-use) is embedded in empirical research.

26 citations


Journal ArticleDOI
01 Jun 2020-Synthese
TL;DR: An enrichment of the Gabbay–Woods schema of Peirce’s 1903 logical form of abduction with illocutionary acts is presented, drawing from logic for pragmatics and its resources to model justified assertions.
Abstract: This paper presents an enrichment of the Gabbay–Woods schema of Peirce’s 1903 logical form of abduction with illocutionary acts, drawing from logic for pragmatics and its resources to model justified assertions. It analyses the enriched schema and puts it into the perspective of Peirce’s logic and philosophy.

25 citations


Journal ArticleDOI
TL;DR: This paper proposes a new model discovery framework that more fully captures the needs of realist explanation and is successful in identifying three competing explanations of these alcohol use patterns, using novel integrations of social role theory not previously considered by the human modeler.
Abstract: The generative approach to social science, in which agent-based simulations (or other complex systems models) are executed to reproduce a known social phenomenon, is an important tool for realist explanation. However, a generative model, when suitably calibrated and validated using empirical data, represents just one viable candidate set of entities and mechanisms. The model only partially addresses the needs of an abductive reasoning process - specifically it does not provide insight into other viable sets of entities or mechanisms, nor suggest which of these are fundamentally constitutive for the phenomenon to exist. In this paper, we propose a new model discovery framework that more fully captures the needs of realist explanation. The framework exploits the implicit ontology of an existing human-built generative model to propose and test a plurality of new candidate model structures. Genetic programming is used to automate this search process. A multi-objective approach is used, which enables multiple perspectives on the value of any particular generative model - such as goodness-of-fit, parsimony, and interpretability - to be represented simultaneously. We demonstrate this new framework using a complex systems modeling case study of change and stasis in societal alcohol use patterns in the US over the period 1980-2010. The framework is successful in identifying three competing explanations of these alcohol use patterns, using novel integrations of social role theory not previously considered by the human modeler. Practitioners in complex systems modeling should use model discovery to improve the explanatory utility of the generative approach to realist social science.

16 citations


Journal ArticleDOI
TL;DR: This paper presents two worked examples of using the MBSSM architecture for modelling individual behaviour mechanisms that give rise to the dynamics of population-level alcohol use: a single-theory model of norm theory and a multi- theory model that combines norm theory with role theory.
Abstract: This paper introduces the MBSSM (Mechanism-Based Social Systems Modelling) software architecture that is designed for expressing mechanisms of social theories with individual behaviour components in a unified way and implementing these mechanisms in an agent-based simulation model. The MBSSM architecture is based on a middle-range theory approach most recently expounded by analytical sociology and is designed in the object-oriented programming paradigm with Unified Modelling Language diagrams. This paper presents two worked examples of using the architecture for modelling individual behaviour mechanisms that give rise to the dynamics of population-level alcohol use: a single-theory model of norm theory and a multi-theory model that combines norm theory with role theory. The MBSSM architecture provides a computational environment within which theories based on social mechanisms can be represented, compared, and integrated. The architecture plays a fundamental enabling role within a wider simulation model-based framework of abductive reasoning in which families of theories are tested for their ability to explain concrete social phenomena.

15 citations


Journal ArticleDOI
TL;DR: In this paper, the characteristics of some types of abductive reasoning used by mathematics education students in problem-solving related to using facts on the problems were explored, and it was found that the student's solutions could be grouped into four types of abduction reasoning: creative conjectures, fact optimization, factual error, mistaken fact, and factual error.
Abstract: When students solve an algebra problem, students try to deduce the facts in the problem. This step is imperative, students can draw conclusions from the facts and devise a plan to solve the problem. Drawing conclusions from facts is called reasoning. Some kinds of reasoning are deductive, inductive, and abductive. This article explores the characteristics of some types of abductive reasoning used by mathematics education students in problem-solving related to using facts on the problems. Fifty-eight students were asked to solve an algebra problem. It was found that the student’s solutions could be grouped into four types of abductive reasoning. From each group, one student was interviewed to have more details on the types. First, the creative conjectures type, the students can solve the problems and develop new ideas related to the problems; second, fact optimization type, the students make conjecture of the answer, then confirm it by deductive reasoning; third, factual error type, students use facts outside of the problems to solve it, but the facts are wrong; and fourth, mistaken fact type, the students assume the questionable thing as a given fact. Therefore, teachers should encourage the students to use creative conjectures and fact optimization when learning mathematics.

15 citations


Journal ArticleDOI
TL;DR: A new tool is presented that provides a methodological context to observe and analyze, both qualitatively and quantitatively, manifestations of abductive reasoning in empirical research and enables both qualitative and quantitative analyses on gathered data to be conducted.
Abstract: This article presents a new tool that provides a methodological context to observe and analyze, both qualitatively and quantitatively, manifestations of abductive reasoning in empirical research. A...

12 citations


Journal ArticleDOI
TL;DR: In this article, the authors argue that mental health professionals tasked to evaluate why a child is resisting/refusing contact with one parent must approach each family the way that Holmes approached each case, without a presumed suspect, moving systematically from detail to hypothesis, well-versed in the full range of dynamics that may be at play, and erring in favor of parsimony rather than pathology.
Abstract: Had Sir Arthur Conan Doyle’s fictional detective, the great Sherlock Holmes, actually engaged in deductive reasoning, he would have solved many fewer crimes. In fact, Holmes’ logical progression from astute observation to hypotheses is a model of a type of inductive reasoning. This paper argues that mental health professionals tasked to evaluate why a child is resisting/refusing contact with one parent must approach each family the way that Holmes approached each case, without a presumed suspect, moving systematically from detail to hypothesis, well-versed in the full range of dynamics that may be at play, and erring in favor of parsimony rather than pathology. By contrast, the custody evaluator who approaches these matters through a deductive process, seeking data that support an a priori theory, is vulnerable to confirmatory bias and doing harm to the child whose interests are paramount. The literature concerned with resist/refuse dynamics is reviewed, yielding 13 non-mutually exclusive variables that evaluators must consider so as to more fully identify why a particular child is resisting or refusing contact with one parent. On this basis, the hybrid model is expanded to include the full spectrum of contributing dynamics. Specific recommendations are made for judicial officers in the interest of writing orders for custody evaluations that minimize the risk of confirmatory bias.

11 citations


Proceedings ArticleDOI
01 Nov 2020
TL;DR: This article propose a multi-head knowledge attention model that encodes semi-structured commonsense inference rules and learns to incorporate them in a transformer-based reasoning cell, which improves performance over strong state-of-the-art models.
Abstract: Social Commonsense Reasoning requires understanding of text, knowledge about social events and their pragmatic implications, as well as commonsense reasoning skills. In this work we propose a novel multi-head knowledge attention model that encodes semi-structured commonsense inference rules and learns to incorporate them in a transformer-based reasoning cell.We assess the model’s performance on two tasks that require different reasoning skills: Abductive Natural Language Inference and Counterfactual Invariance Prediction as a new task. We show that our proposed model improves performance over strong state-of-the-art models (i.e., RoBERTa) across both reasoning tasks. Notably we are, to the best of our knowledge, the first to demonstrate that a model that learns to perform counterfactual reasoning helps predicting the best explanation in an abductive reasoning task. We validate the robustness of the model’s reasoning capabilities by perturbing the knowledge and provide qualitative analysis on the model’s knowledge incorporation capabilities.

Journal ArticleDOI
TL;DR: The concept of abduction was extensively analysed by the pragmatist philosopher Charles Sanders Peirce (1839-1914) more than a century ago as mentioned in this paper, who was particularly concerned with how people respond to experiences they were not expecting by acquiring new beliefs which would make such experiences expected.
Abstract: The concept of abduction was extensively analysed by the pragmatist philosopher Charles Sanders Peirce (1839–1914) more than a century ago. Modern philosophers typically treat abduction as being the same as “inference to the best explanation”, and often even attribute this position to Peirce. But this was not his position. For him, abduction involved inference to any possible explanation. He was particularly concerned with how people respond to experiences they were not expecting by acquiring new beliefs which would make such experiences expected. We spell out the eight cognitive steps from unexpected experience to new belief that are implicit in Peirce’s work on abduction and, using a particular historical example, we show how promising this theory of belief acquisition is. We identify two lacunae in this theory which will need to be filled in if we are to have a complete theory of how unexpected experiences (“surprising facts”) give rise to new beliefs.

Posted Content
TL;DR: The authors investigate whether contemporary NLG models can function as behavioral priors for systems deployed in social settings by generating action hypotheses that achieve predefined goals under moral constraints and examine if models can anticipate likely consequences of (im)moral actions or explain why certain actions are preferable by generating relevant norms.
Abstract: In social settings, much of human behavior is governed by unspoken rules of conduct. For artificial systems to be fully integrated into social environments, adherence to such norms is a central prerequisite. We investigate whether contemporary NLG models can function as behavioral priors for systems deployed in social settings by generating action hypotheses that achieve predefined goals under moral constraints. Moreover, we examine if models can anticipate likely consequences of (im)moral actions, or explain why certain actions are preferable by generating relevant norms. For this purpose, we introduce 'Moral Stories', a crowd-sourced dataset of structured, branching narratives for the study of grounded, goal-oriented social reasoning. Finally, we propose decoding strategies that effectively combine multiple expert models to significantly improve the quality of generated actions, consequences, and norms compared to strong baselines, e.g. though abductive reasoning.

Journal ArticleDOI
19 Jun 2020
TL;DR: In this paper, the authors propose an analysis of deception as the activity of intentionally misleading other agents' hypothetical inferences, which has the advantages of clarifying the epistemological and cognitive dynamics involved in deception.
Abstract: I propose an analysis of deception as the activity of intentionally misleading other agents’ hypothetical inferences Understanding deception in this way has the advantages of clarifying the epistemological and cognitive dynamics involved in deception Indeed, if deception can be framed as the intentional manipulation of others’ hypothetical inferences so that they will accept the false or disadvantageous hypotheses, then a better understanding of the epistemological and cognitive dynamics involved in deception will emerge by clarifying how abduction works Tracing it back to Peirce’s analysis, I will focus on recent perspectives on abduction, which stress the inherent strategic character of abductive cognition and offer a realistic description of the reasoners’ capabilities and their scant resources, both internal (computational power) and external (time and information available) To support and substantiate my thesis, I will examine psychological analyses of military deception I will conclude by remarking the advantages of the thesis here presented to better understand the epistemological dynamics of deception and by highlighting the questions it leaves open for further investigations

DOI
11 Sep 2020
TL;DR: In this paper, the authors discuss the education of inclusive urbanism as the inclusion of the environmental awareness genesis, attitudes toward urban design, cognitive biases and the acceptance of contingency in a spatialisable tabular fashion.
Abstract: In this paper, I discuss the education of inclusive urbanism as the inclusion of the environmental awareness genesis, attitudes toward urban design, cognitive biases and the acceptance of contingency. How do places ‘happen’ to become what they are? What characterises their potentially unreducible singularity in light of general planning laws? I suggest educating along a didactic triangle of rules, novelty and singularity in a spatialisable tabular fashion. In addition to using the methods presented here in teaching, these approaches can also be used to create more inclusion in urban development processes as a whole. With a 3D visualisation matrix of analogue, hybrid and digital methods, I proceed to four exemplary multi-methodological teaching modes to tackle the ever-bygone status quo, to introduce research methodology and, thereby, the defeasibility of both the premises and the conclusions in all-too traditional urban design. I focus on abductive reasoning between the unique locality and the general space of possibility as trial acting and “plan-b thinking” to dialectically shuttle within didactic triangle and visualisation matrix. The curriculum allows for principal and exemplary multi-methodological cross-linkage. Open projects serve as stepping stones into the broad variety of non-algorithmic human occupations in 21st century urban planning. Let us understand our own multiple personal urbanites way beyond professional applicability.

Posted Content
TL;DR: This analysis provides a theoretical framework for understanding what the XAI researchers are already doing, it explains why some XAI projects are succeeding (or might succeed), and it leads to design advice.
Abstract: Current discussions of "Explainable AI" (XAI) do not much consider the role of abduction in explanatory reasoning (see Mueller, et al., 2018). It might be worthwhile to pursue this, to develop intelligent systems that allow for the observation and analysis of abductive reasoning and the assessment of abductive reasoning as a learnable skill. Abductive inference has been defined in many ways. For example, it has been defined as the achievement of insight. Most often abduction is taken as a single, punctuated act of syllogistic reasoning, like making a deductive or inductive inference from given premises. In contrast, the originator of the concept of abduction---the American scientist/philosopher Charles Sanders Peirce---regarded abduction as an exploratory activity. In this regard, Peirce's insights about reasoning align with conclusions from modern psychological research. Since abduction is often defined as "inferring the best explanation," the challenge of implementing abductive reasoning and the challenge of automating the explanation process are closely linked. We explore these linkages in this report. This analysis provides a theoretical framework for understanding what the XAI researchers are already doing, it explains why some XAI projects are succeeding (or might succeed), and it leads to design advice.

Posted Content
TL;DR: A novel approach for answering and explaining multiple-choice science questions by reasoning on grounding and abstract inference chains that elicits explanations by constructing a weighted graph of relevant facts for each candidate answer and extracting the facts that satisfy certain structural and semantic constraints.
Abstract: We propose a novel approach for answering and explaining multiple-choice science questions by reasoning on grounding and abstract inference chains. This paper frames question answering as an abductive reasoning problem, constructing plausible explanations for each choice and then selecting the candidate with the best explanation as the final answer. Our system, ExplanationLP, elicits explanations by constructing a weighted graph of relevant facts for each candidate answer and extracting the facts that satisfy certain structural and semantic constraints. To extract the explanations, we employ a linear programming formalism designed to select the optimal subgraph. The graphs' weighting function is composed of a set of parameters, which we fine-tune to optimize answer selection performance. We carry out our experiments on the WorldTree and ARC-Challenge corpus to empirically demonstrate the following conclusions: (1) Grounding-Abstract inference chains provides the semantic control to perform explainable abductive reasoning (2) Efficiency and robustness in learning with a fewer number of parameters by outperforming contemporary explainable and transformer-based approaches in a similar setting (3) Generalisability by outperforming SOTA explainable approaches on general science question sets.

Posted Content
TL;DR: It is shown that under a cognitively-plausible belief formation mechanism that combines deductive and abductive reasoning, mathematical arguments can undergo what is called an epistemic phase transition: a dramatic and rapidly-propagating jump from uncertainty to near-complete confidence at reasonable levels of claim-to-claim error rates.
Abstract: Mathematical proofs are both paradigms of certainty and some of the most explicitly-justified arguments that we have in the cultural record. Their very explicitness, however, leads to a paradox, because their probability of error grows exponentially as the argument expands. Here we show that under a cognitively-plausible belief formation mechanism that combines deductive and abductive reasoning, mathematical arguments can undergo what we call an epistemic phase transition: a dramatic and rapidly-propagating jump from uncertainty to near-complete confidence at reasonable levels of claim-to-claim error rates. To show this, we analyze an unusual dataset of forty-eight machine-aided proofs from the formalized reasoning system Coq, including major theorems ranging from ancient to 21st Century mathematics, along with four hand-constructed cases from Euclid, Apollonius, Spinoza, and Andrew Wiles. Our results bear both on recent work in the history and philosophy of mathematics, and on a question, basic to cognitive science, of how we form beliefs, and justify them to others.

Proceedings ArticleDOI
25 Jul 2020
TL;DR: In this paper, a learning-to-rank framework was proposed to evaluate the abductive reasoning ability of a learning system, where two observations are given and the most plausible hypothesis is asked to pick out from the candidates.
Abstract: The abductive natural language inference task (αNLI) is proposed to evaluate the abductive reasoning ability of a learning system. In the αNLI task, two observations are given and the most plausible hypothesis is asked to pick out from the candidates. Existing methods simply formulate it as a classification problem, thus a cross-entropy log-loss objective is used during training. However, discriminating true from false does not measure the plausibility of a hypothesis, for all the hypotheses have a chance to happen, only the probabilities are different. To fill this gap, we switch to a ranking perspective that sorts the hypotheses in order of their plausibilities. With this new perspective, a novel L2R2 approach is proposed under the learning-to-rank framework. Firstly, training samples are reorganized into a ranking form, where two observations and their hypotheses are treated as the query and a set of candidate documents respectively. Then, an ESIM model or pre-trained language model, e.g. BERT or RoBERTa, is obtained as the scoring function. Finally, the loss functions for the ranking task can be either pair-wise or list-wise for training. The experimental results on the ART dataset reach the state-of-the-art in the public leaderboard.

Journal ArticleDOI
TL;DR: In this article, the authors present a specialized mode of reasoning, transepistemic abduction (TeA), which establishes how two agents, in order to satisfactorily explain a phenomenon, reason across two epistemic domains despite each agent being ignorant of the other's domain knowledge.
Abstract: Frequently people draw on different domains of knowledge to reach a conclusion that seems reasonable despite being difficult to justify from the perspective of a single domain. For example, there appears to be no reason for ethics to involve mathematics, nor is there a mechanism in mathematics to embrace moral questions; however, both ethics and mathematics are likely to be involved in resolving questions about how an autonomous vehicle should make decisions in a social context. In this paper, we present a specialized mode of reasoning, transepistemic abduction (TeA), which establishes how two agents, in order to satisfactorily explain a phenomenon, reason across two epistemic domains despite each agent being ignorant of the other’s domain knowledge. We formalize TeA with epistemic logic and provide a naturalized example that brings together a psychosocial agent and a computational agent for the analysis of subjective text. We find that interaction between agents is critical to the process of TeA and that there are limits to the formalization while remaining true to naturalized reasoning. We conclude with some important implications of these findings for future work in this area.

Journal ArticleDOI
01 May 2020
TL;DR: In this paper, the authors propose an approach that focuses on the individual and the underlying thinking which bases its foundations on ambidextrous leadership, abductive reasoning and strategic fit, which can facilitate firms to effectively manage incremental and radical innovation alike.
Abstract: The concepts of high-velocity, complexity and interdependency are nowadays vividly discussed in design-led innovation management. Design organisations seek to manage innovation in a more dynamic way to ensure competitive advantage and long-term competitiveness. Contextual ambidexterity is advised to be a dynamic capability that can facilitate firms to effectively manage incremental and radical innovation alike. This paper proposes an approach that focuses on the individual and the underlying thinking which bases its foundations on ambidextrous leadership, abductive reasoning and strategic fit.

Posted Content
TL;DR: It is demonstrated that the Step-wise Conceptual Unification can be effective for unsupervised question answering, and as an explanation extractor in combination with state-of-the-art Transformers.
Abstract: This paper presents an abductive framework for multi-hop and interpretable textual inference. The reasoning process is guided by the notions unification power and plausibility of an explanation, computed through the interaction of two major architectural components: (a) An analogical reasoning model that ranks explanatory facts by leveraging unification patterns in a corpus of explanations; (b) An abductive reasoning model that performs a search for the best explanation, which is realised via conceptual abstraction and subsequent unification. We demonstrate that the Step-wise Conceptual Unification can be effective for unsupervised question answering, and as an explanation extractor in combination with state-of-the-art Transformers. An empirical evaluation on the Worldtree corpus and the ARC Challenge resulted in the following conclusions: (1) The question answering model outperforms competitive neural and multi-hop baselines without requiring any explicit training on answer prediction; (2) When used as an explanation extractor, the proposed model significantly improves the performance of Transformers, leading to state-of-the-art results on the Worldtree corpus; (3) Analogical and abductive reasoning are highly complementary for achieving sound explanatory inference, a feature that demonstrates the impact of the unification patterns on performance and interpretability.

Journal ArticleDOI
TL;DR: This paper compares publicly available general purpose tools, established Horn reasoning engines, as well as new variations of known methods as a means for abduction, and focuses on Horn representations, which provide a suitable language to describe most diagnostic scenarios.
Abstract: Abductive inference derives explanations for encountered anomalies and thus embodies a natural approach for diagnostic reasoning. Yet its computational complexity, which is inherent to the expressiveness of the underlying theory, remains a disadvantage. Even when restricting the representation to Horn formulae the problem is NP-complete. Hence, finding procedures that can efficiently solve abductive diagnosis problems is of particular interest from a research as well as practical point of view. In this paper, we aim at providing guidance on choosing an algorithm or tool when confronted with the issue of computing explanations in propositional logic-based abduction. Our focus lies on Horn representations, which provide a suitable language to describe most diagnostic scenarios. We illustrate abduction via two contrasting problem formulations: direct proof methods and conflict-driven techniques. While the former is based on determining logical consequences, the later searches for suitable refutations involving possible causes. To reveal runtime performance trends we conducted a case study, in which we compared publicly available general purpose tools, established Horn reasoning engines, as well as new variations of known methods as a means for abduction.

01 Jan 2020
TL;DR: The reactions and behaviors of biological entities to biotechnological intervention are considered, and an attempt is made to characterize the degree of freedom of embryos & clones, which show openness to different outcomes when the epigenetic developmental landscape is factored in.
Abstract: This presentation discusses a notion encountered across disciplines, and in different facets of human activity: autonomous activity. We engage it in an interdisciplinary way. We start by considering the reactions and behaviors of biological entities to biotechnological intervention. An attempt is made to characterize the degree of freedom of embryos & clones, which show openness to different outcomes when the epigenetic developmental landscape is factored in. We then consider the claim made in programming and artificial intelligence that automata could show self-directed behavior as to the determination of their step-wise decisions on courses of action. This question remains largely open and calls for some important qualifications. We try to make sense of the presence of claims of freedom in agency, first in common sense, then by ascribing developmental plasticity in biology and biotechnology, and in the mapping of programmed systems in the presence of environmental cues and self-referenced circuits as well as environmental coupling. This is the occasion to recall attempts at working out a logical and methodological approach to the openness of concepts that are still to be found, and assess whether they can operate the structuring intelligibility of a yet undeveloped or underdeveloped field of study, where a “bisociation" and a unification of knowledge might be possible.

Book ChapterDOI
01 Jul 2020
TL;DR: This work proposes an approach for solving the abduction problem in logic that is based on syntax-guided enumeration and uses a novel procedure that incrementally constructs a solution in disjunctive normal form that is built from enumerated formulas.
Abstract: The abduction problem in logic asks whether there exists a formula that is consistent with a given set of axioms and, together with these axioms, suffices to entail a given goal. We propose an approach for solving this problem that is based on syntax-guided enumeration. For scalability, we use a novel procedure that incrementally constructs a solution in disjunctive normal form that is built from enumerated formulas. The procedure can be configured to generate progressively weaker and simpler solutions over the course of a run of the procedure. Our approach is fully general and can be applied over any background logic that is handled by the underlying SMT solver in our approach. Our experiments show our approach compares favorably with other tools for abductive reasoning.

Book ChapterDOI
01 Jan 2020
TL;DR: Abe et al. as discussed by the authors discuss machine learning models based on abductive learning techniques and their implications to artificial reasoning, including the applicability of abductive reasoning to artificial intelligence and machine learning.
Abstract: There has been much research in recent years in the applicability of abductive reasoning to artificial intelligence and machine learning. Abductive learning involves finding the best explanation for a set of observations, based on creating a set of possible explanatory hypotheses. Formal models have been created (Abe, Proceedings of the IJCAI97 Workshop on Induction, 1997), which are utilized to analyze the properties and computational efficiencies of abductive reasoning to various artificial intelligence applications. Here we discuss machine learning models based on abductive learning techniques and their implications to artificial reasoning.

Journal ArticleDOI
TL;DR: In this article, the degree of error of abductive inference differs according to the experience of social exclusion by t-test, and the group who experienced social exclusion had a higher level of abduction error than the group that did not experience it.
Abstract: This study examines cognitive impairment, which is one of the results from social exclusion and leads to various cognitive deficits such as logical reasoning disorders. This study also investigates how cognitive errors called abductive inference error occur due to cognitive impairment. This study was performed with 81 college students. Participants were randomly assigned to a group who experienced social exclusion and a group who did not experience social exclusion. We analyzed how the degree of error of abductive inference differs according to the experience of social exclusion by t-test. The group who experienced social exclusion had a higher level of abductive inference error than the group who did not experience it. The abductive condition inference value of the group who experienced social exclusion was higher in the group with the deduction condition inference value of 90% than in the group with the deduction condition inference value of 10%, and the difference was also significant. This study extended the concepts of cognitive impairments, escape theory, and cognitive narrowing, which are used to explain addiction behavior to human cognitive bias. Also this study confirmed that social exclusion experience increased cognitive impairment and abductive inference error. Implications and future research directions are discussed and suggested.

Posted Content
TL;DR: This work proposes a novel multi-head knowledge attention model that encodes semi-structured commonsense inference rules and learns to incorporate them in a transformer-based reasoning cell, and is the first to demonstrate that a model that learns to perform counterfactual reasoning helps predicting the best explanation in an abductive reasoning task.
Abstract: Social Commonsense Reasoning requires understanding of text, knowledge about social events and their pragmatic implications, as well as commonsense reasoning skills. In this work we propose a novel multi-head knowledge attention model that encodes semi-structured commonsense inference rules and learns to incorporate them in a transformer-based reasoning cell. We assess the model's performance on two tasks that require different reasoning skills: Abductive Natural Language Inference and Counterfactual Invariance Prediction as a new task. We show that our proposed model improves performance over strong state-of-the-art models (i.e., RoBERTa) across both reasoning tasks. Notably we are, to the best of our knowledge, the first to demonstrate that a model that learns to perform counterfactual reasoning helps predicting the best explanation in an abductive reasoning task. We validate the robustness of the model's reasoning capabilities by perturbing the knowledge and provide qualitative analysis on the model's knowledge incorporation capabilities.

Journal ArticleDOI
TL;DR: Abductive reasoning training in nursing education may improve students' hypothesis generation abilities, and posttest scores showed a significant improvement in participants' hypothesisgeneration abilities.
Abstract: BACKGROUND Hypothetico-deductive reasoning used by novice nurses could limit their ability to explain a presenting care situation in its entirety. Hence, scholars recommend the use of abductive reasoning as an alternative approach. PURPOSE This study explored the effects of abductive reasoning training on baccalaureate nursing students' hypothesis generation abilities. METHOD Through a pretest-posttest study, we delivered educational training on abductive reasoning and examined hypothesis accuracy, expertise, and breadth. Participants generated scenario-specific hypotheses before and after the training. Academic content experts validated the scenarios, and 2 independent raters scored participants' hypotheses. RESULTS Twenty first- and second-year nursing students participated in this pilot study. Posttest scores showed a significant improvement in participants' hypothesis generation abilities: accuracy (P < .001), expertise (P < .001), and breadth (P = .006). CONCLUSION Abductive reasoning training in nursing education may improve students' hypothesis generation abilities.

Book ChapterDOI
TL;DR: By embracing a model of mathematics as not perfectly predictable, this work generates a new and fruitful perspective on the epistemology and practice of mathematics.
Abstract: We present a computational model of mathematical reasoning according to which mathematics is a fundamentally stochastic process. That is, in our model, whether or not a given formula is deemed a theorem in some axiomatic system is not a matter of certainty, but is instead governed by a probability distribution. We then show that this framework gives a compelling account of several aspects of mathematical practice. These include: 1) the way in which mathematicians generate research programs, 2) the applicability of Bayesian models of mathematical heuristics, 3) the role of abductive reasoning in mathematics, 4) the way in which multiple proofs of a proposition can strengthen our degree of belief in that proposition, and 5) the nature of the hypothesis that there are multiple formal systems that are isomorphic to physically possible universes. Thus, by embracing a model of mathematics as not perfectly predictable, we generate a new and fruitful perspective on the epistemology and practice of mathematics.