An Evaluation of Dual-Process Theories of Reasoning
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Graph-Based Reasoning in Collaborative Knowledge Management for Industrial Maintenance Bernard Kamsu-Foguem, Daniel Noyes
Graph-based reasoning in collaborative knowledge management for industrial maintenance Bernard Kamsu-Foguem, Daniel Noyes To cite this version: Bernard Kamsu-Foguem, Daniel Noyes. Graph-based reasoning in collaborative knowledge manage- ment for industrial maintenance. Computers in Industry, Elsevier, 2013, Vol. 64, pp. 998-1013. 10.1016/j.compind.2013.06.013. hal-00881052 HAL Id: hal-00881052 https://hal.archives-ouvertes.fr/hal-00881052 Submitted on 7 Nov 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Open Archive Toulouse Archive Ouverte (OATAO) OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in: http://oatao.univ-toulouse.fr/ Eprints ID: 9587 To link to this article: doi.org/10.1016/j.compind.2013.06.013 http://www.sciencedirect.com/science/article/pii/S0166361513001279 To cite this version: Kamsu Foguem, Bernard and Noyes, Daniel Graph-based reasoning in collaborative knowledge management for industrial -
Dennett's Dual-Process Theory of Reasoning ∗
∗∗∗ Dennett’s dual-process theory of reasoning Keith Frankish 1. Introduction Content and Consciousness (hereafter, C&C) 1 outlined an elegant and powerful framework for thinking about the relation between mind and brain and about how science can inform our understanding of the mind. By locating everyday mentalistic explanations at the personal level of whole, environmentally embedded organisms, and related scientific explanations at the subpersonal level of internal informational states and processes, and by judicious reflections on the relations between these levels, Dennett showed how we can avoid the complementary errors of treating mental states as independent of the brain and of projecting mentalistic categories onto the brain. In this way, combining cognitivism with insights from logical behaviourism, we can halt the swinging of the philosophical pendulum between the “ontic bulge” of dualism (C&C, p.5) and the confused or implausible identities posited by some brands of materialism, thereby freeing ourselves to focus on the truly fruitful question of how the brain can perform feats that warrant the ascription of thoughts and experiences to the organism that possesses it. The main themes of the book are, of course, intentionality and experience – content and consciousness. Dennett has substantially expanded and revised his views on these topics over the years, though without abandoning the foundations laid down in C&C, and his views have been voluminously discussed in the associated literature. In the field of intentionality, the major lessons of C&C have been widely accepted – and there can be no higher praise than to say that claims that seemed radical forty-odd years ago now seem obvious. -
Evolutionary Psychology and Human Reasoning: Testing the Domain-Specificity Hypothesis Through Wason Selection Task
Evolutionary Psychology and Human Reasoning: Testing the Domain-Specificity Hypothesis through Wason Selection Task Fabrizio Ferrara ([email protected])* Amedeo Esposito ([email protected])* Barbara Pizzini ([email protected])* Olimpia Matarazzo ([email protected])* *Department of Psychology - Second University of Naples, Viale Ellittico 31, Caserta, 81100 Italy Abstract during phylogenetic development. Both these approaches, albeit based on a different conception of the human mind, The better performance in the selection task with deontic rules, compared to the descriptive version, has been share the assumption that it is better equipped for reasoning interpreted by evolutionary psychologists as the evidence that with general (e.g. Cummins 1996, 2013) or specific (e.g. human reasoning has been shaped to deal with either global or Cosmides, 1989; Cosmides & Tooby, 2013) deontic norms specific deontic norms. An alternative hypothesis is that the rather than with epistemic concepts (i.e. the concepts related two types of rules have been embedded in two different forms to knowledge and belief). of reasoning, about and from a rule, the former demanding Experimental evidence achieved mainly by means of the more complex cognitive processes. In a between-subjects study with 640 participants we manipulated the content of the selection task (Wason, 1966) is thought to support this rule (deontic vs. social contract vs. precaution vs. descriptive) claim. In the original formulation, this task consists in and the type of task (reasoning about, traditionally associated selecting the states of affairs necessary to determine the to indicative tasks, vs. reasoning from, traditionally associated truth-value of a descriptive (or indicative) rule expressed to deontic tasks). -
Next Generation Logic Programming Systems Gopal Gupta
Next Generation Logic Programming Systems Gopal Gupta In the last 30 years logic programming (LP), with Prolog as the most representative logic programming language, has emerged as a powerful paradigm for intelligent reasoning and deductive problem solving. In the last 15 years several powerful and highly successful extensions of logic programming have been proposed and researched. These include constraint logic programming (CLP), tabled logic programming, inductive logic programming, concurrent/parallel logic programming, Andorra Prolog and adaptations for non- monotonic reasoning such as answer set programming. These extensions have led to very powerful applications in reasoning, for example, CLP has been used in industry for intelligently and efficiently solving large scale scheduling and resource allocation problems, tabled logic programming has been used for intelligently solving large verification problems as well as non- monotonic reasoning problems, inductive logic programming for new drug discoveries, and answer set programming for practical planning problems, etc. However, all these powerful extensions of logic programming have been developed by extending standard logic programming (Prolog) systems, and each one has been developed in isolation from others. While each extension has resulted in very powerful applications in reasoning, considerably more powerful applications will become possible if all these extensions were combined into a single system. This power will come about due to search space being dramatically pruned, reducing the time taken to find a solution given a reasoning problem coded as a logic program. Given the current state-of-the-art, even two extensions are not available together in a single system. Realizing multiple or all extensions in a single framework has been rendered difficult by the enormous complexity of implementing reasoning systems in general and logic programming systems in particular. -
Logic-Based Technologies for Intelligent Systems: State of the Art and Perspectives
information Article Logic-Based Technologies for Intelligent Systems: State of the Art and Perspectives Roberta Calegari 1,* , Giovanni Ciatto 2 , Enrico Denti 3 and Andrea Omicini 2 1 Alma AI—Alma Mater Research Institute for Human-Centered Artificial Intelligence, Alma Mater Studiorum–Università di Bologna, 40121 Bologna, Italy 2 Dipartimento di Informatica–Scienza e Ingegneria (DISI), Alma Mater Studiorum–Università di Bologna, 47522 Cesena, Italy; [email protected] (G.C.); [email protected] (A.O.) 3 Dipartimento di Informatica–Scienza e Ingegneria (DISI), Alma Mater Studiorum–Università di Bologna, 40136 Bologna, Italy; [email protected] * Correspondence: [email protected] Received: 25 February 2020; Accepted: 18 March 2020; Published: 22 March 2020 Abstract: Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future. -
Compiling a Default Reasoning System Into Prolog
Compiling A Default Reasoning System into Prolog David Poole Department of Computer Science University of British Columbia Vancouver BC Canada VT W p o olecsub cca July Abstract Articial intelligence researchers have b een designing representa tion systems for default and ab ductive reasoning Logic Programming researchers have b een working on techniques to improve the eciency of Horn Clause deduction systems This pap er describ es how one such default and ab ductive reasoning system namely Theorist can b e translated into Horn clauses with negation as failure so that we can use the clarity of ab ductive reasoning systems and the eciency of Horn clause deduction systems Wethus showhowadvances in expressivepower that articial intelligence workers are working on can directly utilise advances in eciency that logic programming re searchers are working on Actual co de from a running system is given Intro duction Many p eople in Articial Intelligence have b een working on default reasoning and ab ductive diagnosis systems The systems implemented so far eg are only prototyp es or have b een develop ed in A Theorist to Prolog Compiler ways that cannot take full advantage in the advances of logic programming implementation technology Many p eople are working on making logic programming systems more ecient These systems however usually assume that the input is in the form of Horn clauses with negation as failure This pap er shows howto implement the default reasoning system Theorist by compiling its input into Horn clauses -
Reasoning Versus Text Processing in the Wason Selection Task: a Nondeontic Perspective on Perspective Effects
Memory & Cognition 2000,28 (6), 1060-1070 Reasoning versus text processing in the Wason selection task: A nondeontic perspective on perspective effects AMITALMOR University ofSouthern California, Los Angeles, California and STEVEN A. SLOMAN Brown University, Providence, Rhode Island Weargue that perspective effects in the Wason four-card selection task are a product of the linguis tic interpretation of the rule in the context of the problem text and not of the reasoning process un derlying card selection. In three experiments, participants recalled the rule they used in either a se lection or a plausibility rating task. The results showed that (1) participants tended to recall rules compatible with their card selection and not with the rule as stated in the problem and (2) recall was not affected by whether or not participants performed card selection. Weconclude that perspective ef fects in the Wason selection task do not concern how card selection is reasoned about but instead re flect the inferential text processing involved in the comprehension of the problem text. Together with earlier research that showed selection performance in nondeontic contexts to be indistinguishable from selection performance in deontic contexts (Almor & Sloman, 1996; Sperber, Cara, & Girotto, 1995), the present results undermine the claim that reasoning in a deontic context elicits specialized cognitive processes. A central question regarding the nature ofhuman rea 1966) and as a means ofstudying various factors that may soning is whether it operates solely on the basis ofgen help "rehabilitate" people's apparently illogical thinking eral domain-independent principles or whether reason (Griggs & Cox, 1982, 1983; Wason & Shapiro, 1971), the ing is domain-specific. -
Conditional Reasoning: the Importance of Individual Differences*
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Repositorio Institucional da Universidade de Santiago de Compostela CONDITIONAL REASONING: THE IMPORTANCE OF INDIVIDUAL DIFFERENCES* Mª Dolores Valiña, Gloria Seoane, Mª José Ferraces & Montserrat Martín University of Santiago de Compostela (Spain) INTRODUCTION The study of thinking and reasoning is a topic of central interest for economists, anthropologists, logicians, pedagogues and of course for psychologists. A central problem in the experimental investigation in Psychology is to describe how people think and reason deductively and inductively. There are three fundamental theoretical approaches to deductive reasoning in the Cognitive Psychology: mental logic, mental models and pragmatic schemas (see Evans, Newstead & Byrne, 1993, for a detailed review). There are several proponents of a universal mental logic (Inhelder & Piaget, 1958) or natural logics (Braine, 1978, 1990, 1994; Braine & O´Brien, 1991; Braine & Rumain, 1983; Osherson, 1974, 1975; Rips, 1983, 1990, 1994). Other authors propose that reasoning is based on construction and evaluation of mental models (Johnson-Laird, 1983; Johnson-Laird & Byrne, 1991). A third approach asserts that reasoning is not based on general inference rules and assumes that people have domain-specific reasoning mechanisms such as pragmatic reasoning schemas inductively acquired (Cheng & Holyoak, 1985, 1989; Cheng, Holyoak, Nisbett & Oliver, 1986; Holyoak & Cheng, 1995) or innates procedures for identify potential deviations from social contracts (Cosmides 1985, 1989; Cosmides & Tooby, 1992) _______________________ * An extended version of this paper has been published in the chapter: “Conditional reasoning: The importance of individual differences”, of the book: Mental Models in Reasoning, J.A. -
Belief in Psyontology Cumstances (Ross & Schroeder, 2014; Schwitzgebel, 2015)
Philosophers’ volume 20, no. 11 ow are full and partial belief related in psychology’s ontology? Imprint april 2020 H Credence-first philosophers think partial belief is more funda- mental. For example, Lockeans say that to believe P is just to have high credence in P. Whereas categorical-first philosophers make full beliefs fundamental instead. Having credence x in P might just amount to having a categorical belief that P’s probability is x, for example. Work in cognitive psychology supports a different view, however. In humans, beliefs come in both coarse and fine kinds, with neither BELIEF IN more fundamental than the other. Epistemologists who focus on one kind to the exclusion of the other, or who treat one as central and the other as an afterthought, risk toiling at a fiction. This conclusion is necessarily tentative. For one thing, the empir- PSYONTOLOGY ical work is nascent and ongoing, its results subject to revision. But more than that, it’s sometimes unclear what the present results tell us about the ontological and epistemological questions of interest to philosophers. Still, a prima facie case can be made. And making it is an essential step in bringing our best science to bear on what is, at least in part, an empirical question. 1. Background Jonathan Weisberg 1.1 Why Monism? Why think one kind of belief is more fundamental than the other? It’s not just that Anglophone philosophers use ‘belief’ for both kinds, so University of Toronto they must really be the same thing deep down.1 It’s rather what causes us to use ‘belief’ for both attitudes. -
3. Overview of Core Technical Areas of AI Research Roadmap Research
A 20-YEAR COMMUNITY ROADMAP FOR ARTIFICIAL INTELLIGENCE RESEARCH IN THE US of actions.22 Future approaches to cybersecurity and defense will benefit from the powerful capabilities provided by AI systems. This report envisions how future AI systems can aid in responses to other types of threats as well, including natural disasters. 3. Overview of Core Technical Areas of AI Research Roadmap As discussed in section 1.2 the CCC, with the support of AAAI, held three community workshops in order to catalyze discussion and generate this research Roadmap. The three workshops were: ◗ Integrated Intelligence ◗ Meaningful Interactions ◗ Self-Aware Learning The research priorities from the three areas are summarized in Figure 3 below and expanded upon in the following three sections of the report. Research Priorities Integrated Intelligence Meaningful Interaction Self-Aware Learning • Science of integrated intelligence • Collaboration • Robust and trustworthy learning • Contextualized AI • Trust and responsibility • Deeper learning for challenging tasks • Open knowledge repositories • Diversity of interaction channels • Integrating symbolic and numeric representations • Understanding human intelligence • Improving online interaction • Learning in integrated AI/robotic systems Figure 3. Research Priorities. 3.1 A Research Roadmap for Integrated Intelligence 3.1.1 INTRODUCTION AND OVERVIEW The development of integrated intelligent systems will require major research efforts, which we group into three major areas: 1. Integration: Science of Integrated Intelligence will explore how to create intelligent systems that have much broader capabilities than today’s systems. Approaches include finding small sets of primitives out of which a broad range of capabilities can be constructed (like many of today’s cognitive architectures) and composing independently developed AI capabilities (like 22 ibid 16 many of today’s deployed intelligent systems). -
How Emotions Affect Logical Reasoning: Evidence from Experiments with Mood-Manipulated Participants, Spider Phobics, and People with Exam Anxiety
Emotion Science How emotions affect logical reasoning: Evidence from experiments with mood-manipulated participants, spider phobics, and people with exam anxiety Nadine Jung, Christina Wranke, Kai Hamburger and Markus Knauff Journal Name: Frontiers in Psychology ISSN: 1664-1078 Article type: Original Research Article Received on: 29 Oct 2013 Accepted on: 22 May 2014 Provisional PDF published on: 22 May 2014 www.frontiersin.org: www.frontiersin.org Citation: Jung N, Wranke C, Hamburger K and Knauff M(2014) How emotions affect logical reasoning: Evidence from experiments with mood-manipulated participants, spider phobics, and people with exam anxiety. Front. Psychol. 5:570. doi:10.3389/fpsyg.2014.00570 /Journal/Abstract.aspx?s=361& /Journal/Abstract.aspx?s=361&name=emotion%20science& name=emotion%20science& ART_DOI=10.3389/fpsyg.2014.00570 ART_DOI=10.3389 /fpsyg.2014.00570: (If clicking on the link doesn't work, try copying and pasting it into your browser.) Copyright statement: © 2014 Jung, Wranke, Hamburger and Knauff. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. This Provisional PDF corresponds to the article as it appeared upon acceptance, after rigorous -
What Are Dual Process Models? Implications for Cultural Analysis in Sociology1
What Are Dual Process Models? Implications for Cultural Analysis in Sociology1 Omar Lizardo Robert Mowry Brandon Sepulvado [email protected] [email protected] [email protected] Dustin S. Stoltz Marshall A. Taylor Justin Van Ness [email protected] [email protected] [email protected] Michael Wood [email protected] 1 The idea for this paper first emerged in a fruitful discussion in a graduate seminar on “Culture, Cognition, and Society” led by the first author in the Spring of 2015 and in which all other authors were participants. Our names appear in alphabetical order to reflect the fact that this paper has been a collective endeavor through and through, from conception, to writing, to editing each other's words, although it did not took long for one of the authors to note the fact that, conveniently enough, this arbitrary convention still left the first author slot to be occupied by the more (institutionally) senior member of the group. In spite of that, this paper would never had come to fruition if it was not for Dustin Stoltz’s vision, perseverance, and hard work (especially when it comes to assembling the citation data) and as such he deserves special thanks. Dustin was the first one to “see” a paper where the first author just saw a set of smart points usable to impress students in a seminar context. Dustin herded all of the cats, and made the seemingly quixotic attempt to write a seven-authored theory piece seem like a breeze. Of course, it was the intellectual input of all authors that ultimately made the paper more than the sum of its separate parts although we will spare you tired emergence analogies.