A computational model of inference during story comprehension is presented, in which story situations are represented distributively as points in a high‐dimensional “situation‐state space.” This state space organizes itself on the basis of a constructed microworld description. From the same description, causal/temporal world knowledge is extracted. The distributed representation of story situations is more flexible than Golden and Rumelhart's [Discourse Proc 16 (1993) 203] localist representation.A story taking place in the microworld corresponds to a trajectory through situation‐state space. (...) During the inference process, world knowledge is applied to the story trajectory. This results in an adjusted trajectory, reflecting the inference of propositions that are likely to be the case. Although inferences do not result from a search for coherence, they do cause story coherence to increase. The results of simulations correspond to empirical data concerning inference, reading time, and depth of processing.An extension of the model for simulating story retention shows how coherence is preserved during retention without controlling the retention process. Simulation results correspond to empirical data concerning story recall and intrusion. (shrink)

A computational model of inference during story comprehension is presented, in which story situations are represented distributively as points in a high‐dimensional “situation‐state space.” This state space organizes itself on the basis of a constructed microworld description. From the same description, causal/temporal world knowledge is extracted. The distributed representation of story situations is more flexible than Golden and Rumelhart's [Discourse Proc 16 (1993) 203] localist representation.A story taking place in the microworld corresponds to a trajectory through situation‐state space. (...) During the inference process, world knowledge is applied to the story trajectory. This results in an adjusted trajectory, reflecting the inference of propositions that are likely to be the case. Although inferences do not result from a search for coherence, they do cause story coherence to increase. The results of simulations correspond to empirical data concerning inference, reading time, and depth of processing.An extension of the model for simulating story retention shows how coherence is preserved during retention without controlling the retention process. Simulation results correspond to empirical data concerning story recall and intrusion. (shrink)

There are several approaches implementing reasoning based on conditional knowledge bases, one of the most popular being System Z (Pearl, Proceedings of the 3rd conference on theoretical aspects of reasoning about knowledge, TARK ’90, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp. 121–135, 1990). We look at ranking functions (Spohn, The Laws of Belief: Ranking Theory and Its Philosophical Applications, Oxford University Press, Oxford, 2012) in general, conditional structures and c-representations (Kern-Isberner, Conditionals in Nonmonotonic Reasoning and (...) Belief Revision: Considering Conditionals as Agents, vol. 2087 of LNCS, Springer, Berlin, 2001) in order to examine the reasoning strength of the different approaches by learning which of the known calculi of nonmonotonic reasoning (System P and R) and Direct Inference are applicable to these inference relations. Furthermore we use the recently proposed Enforcement-postulate (Kern-Isberner and Krümpelmann, Proceedings of the 22nd international joint conference on artificial intelligence, vol. 2, IJCAI’11, AAAI Press, pp. 937–942, 2011) to show dependencies between these approaches. (shrink)

One of the main challenges for computational legal research is drawing up innovative heuristics to derive actionable knowledge from legal documents. While a large part of the research has been so far devoted to the extraction of purely legal information, less attention has been paid to seeking out in the texts the clues of more complex entities: legally relevant facts whose detection requires to link and interpret, as a unified whole, legal information and results of empirical analyses. This paper (...) presents an ongoing research that points in this direction, trying to devise new ways to support public prosecutors in assessing the dangerousness of individuals and groups under investigation, an activity that precisely relies on the cross-sectional evaluation of legal and empirical data. A knowledge mining strategy will be outlined that lines up, into a single metaheuristic model, information extraction, network-based inference, machine learning and visual analytics. We will focus, in particular, on the integration of graph-based inference and machine learning methods used both to support classification tasks and to explore new forms of man-machine cooperation. Experiments made involving public prosecutors from the Italian Anti-Mafia Investigation Directorate and using data from real investigations have not only shown the potentialities of our approach but also offered an opportunity to reflect on the role we could assign to AI when thinking about the future of legal science and practice. (shrink)

Suppose a speaker S and an audience A are in a communication coordination problem. That is, for some proposition p, they each prefer that S mean that p and that A believe p in response. How do they coordinate their thought and action to solve the problem? The Gricean answer is that they reason their way to the solution. Pragmatics makes a similar assumption. “Pragmatics involves perception augmented by some species of ‘ampliative’ inference … a sort of reasoning”. There (...) are two objections. The first is that it is not plausible to attribute such reasoning to speakers and audiences. The second objection grants, for the sake of argument, that speakers and audiences reason in the required ways. The objection is that this is not sufficient to solve coordination problems since a speaker and an audience may not know how each has reasoned. The problem is well-known in game theory, which typically solves it by assuming the parties’ preferences are common knowledge. Common knowledge is the recursive belief state in which people are know something, know they know it, know they know they know it, ad infinitum. I propose a similar solution. When people interact as speaker and audience, common knowledge arises from their perceptions of each other as fulfilling the roles of speaker and audience. The account of how common knowledge arises entails Grice’s general characterization of conversational implicatures. More precisely, it entails speakers and audiences involved in communication coordination problems typically commonly know that a relevant instance of the conditions holds. The common knowledge approach requires attributing reasoning to speakers and audiences, but it keeps those attributions to a plausible minimum. Thus the common knowledge approach is a viable alternative to current approaches that attribute extensive reasoning to speakers and audiences.A common knowledge-based approach to pragmatics would answer at least the following question: What informative generalizations are possible about the role of contexts in generating common knowledge relevant to conversational implicatures? The question is important. “Much has been learned about these domains of psychology from a focus on the problem of altruistic cooperation and the mechanisms of reciprocity. We hope that comparable insights are waiting to be discovered by psychologists as they investigate the problem of mutualistic cooperation, and the mechanisms of common knowledge are—as we might say—put out there”. (shrink)

This book describes in detail the relationship between symbolic representations of knowledge and abstract states of knowledge, exploring along the way the foundations of knowledge, knowledge bases, knowledge-based systems, and knowledge representation and reasoning. The idea of knowledge bases lies at the heart of symbolic, or "traditional," artificial intelligence. A knowledge-based system decides how to act by running formal reasoning procedures over a body of explicitly represented knowledge—a knowledge (...) base. The system is not programmed for specific tasks; rather, it is told what it needs to know and expected to infer the rest. This book is about the logic of such knowledge bases. It describes in detail the relationship between symbolic representations of knowledge and abstract states of knowledge, exploring along the way the foundations of knowledge, knowledge bases, knowledge-based systems, and knowledge representation and reasoning. Assuming some familiarity with first-order predicate logic, the book offers a new mathematical model of knowledge that is general and expressive yet more workable in practice than previous models. The book presents a style of semantic argument and formal analysis that would be cumbersome or completely impractical with other approaches. It also shows how to treat a knowledge base as an abstract data type, completely specified in an abstract way by the knowledge-level operations defined over it. (shrink)

The logical consequence relations η▹η provide a very attractive way of inferring new facts from inconsistent knowledge bases without compromising standards of credibility. In this short note we provide proof theories and completeness theorems for these consequence relations which may have some applicability in small examples.

Some researchers on binary choice inference have argued that people make inferences based on simple heuristics, such as recognition, fluency, or familiarity. Others have argued that people make inferences based on available knowledge. To examine the boundary between heuristic and knowledge usage, we examine binary choice inference processes in terms of attribute substitution in heuristic use (Kahneman & Frederick, 2005). In this framework, it is predicted that people will rely on heuristic or knowledge‐ (...) class='Hi'>based inference depending on the subjective difficulty of the inference task. We conducted competitive tests of binary choice inference models representing simple heuristics (fluency and familiarity heuristics) and knowledge‐based inference models. We found that a simple heuristic model (especially a familiarity heuristic model) explained inference patterns for subjectively difficult inference tasks, and that a knowledge‐based inference model explained subjectively easy inference tasks. These results were consistent with the predictions of the attribute substitution framework. Issues on usage of simple heuristics and psychological processes are discussed. (shrink)

Nonmonotonic reasoning is a discipline of computer science, epistemology, and cognition: It models inferences where classical logic is inadequate in symbolic AI, defines normative models for reasoning with defeasible information in epistemology, and models human reasoning under information change in cognition. Its building blocks are defeasible rules formalised as DeFinetti conditionals. In this thesis, Christian Eichhorn examines qualitative and semi-quantitative inference relations on top said conditionals, using the conditional structure of the knowledge base and Spohn’s Ordinal Conditional Functions, (...) using established properties. Converting network approaches from probabilistics, he shows how to approach the relations with regard to implementation. (shrink)

According to the received view in epistemology, inferential knowledge from non-knowledge is impossible – that is, in order for a subject to know the conclusion of their inference, they must know the essential premises from which that conclusion is drawn. In this book, Federico Luzzi critically examines this view, arguing that it is less plausible than intuition suggests and that it can be abandoned without substantial cost. In a discussion that ranges across inference, testimony and memory (...) he analyses the full range of challenges to the view, connecting them to epistemological cases that support those challenges. He then proposes a defeater-based framework which allows the phenomenon of knowledge from non-knowledge across these three epistemic areas to be better understood. His book will be of interest to a wide range of readers in epistemology. (shrink)

Watching another person take actions to complete a goal and making inferences about that person's knowledge is a relatively natural task for people. This ability can be especially important in educational settings, where the inferences can be used for assessment, diagnosing misconceptions, and providing informative feedback. In this paper, we develop a general framework for automatically making such inferences based on observed actions; this framework is particularly relevant for inferring student knowledge in educational games and other interactive (...) virtual environments. Our approach relies on modeling action planning: We formalize the problem as a Markov decision process in which one must choose what actions to take to complete a goal, where choices will be dependent on one's beliefs about how actions affect the environment. We use a variation of inverse reinforcement learning to infer these beliefs. Through two lab experiments, we show that this model can recover people's beliefs in a simple environment, with accuracy comparable to that of human observers. We then demonstrate that the model can be used to provide real-time feedback and to model data from an existing educational game. (shrink)

According to inferentialism about self-knowledge of desire, the basic way in which we come to know what we want is through inference. In this paper, I argue that in a wide range of cases of knowing one’s desire, inference is insufficient. In particular, I look at two inferentialist models, one proposed by Krista Lawlor and the other by Alex Byrne and look at the challenges that they face in securing safe self-ascriptions. In response to these difficulties, I (...) argue that we can explain how inferentially based self-ascriptions can be safe when we consider the agent’s role in sustaining their desires through attending and elaborating on the content of desire through imagination. (shrink)

We apply a cognitive modeling approach to the problem of measuring expertise on rank ordering problems. In these problems, people must order a set of items in terms of a given criterion (e.g., ordering American holidays through the calendar year). Using a cognitive model of behavior on this problem that allows for individual differences in knowledge, we are able to infer people's expertise directly from the rankings they provide. We show that our model-based measure of expertise outperforms self-report (...) measures, taken both before and after completing the ordering of items, in terms of correlation with the actual accuracy of the answers. These results apply to six general knowledge tasks, like ordering American holidays, and two prediction tasks, involving sporting and television competitions. Based on these results, we discuss the potential and limitations of using cognitive models in assessing expertise. (shrink)

The mental model theory is an approach with clear psychological, linguistic, and cognitive consequences. This paper delves into some of the epistemological conclusions that can be drawn from it. In particular, it addresses the process why knowledge acquisition can modify the inferences people tend to make. That process is described by means of an example based on a well-known logical schema related to the conditional: Modus Tollendo Tollens.

Kerry et al. criticize our discussion of causal knowledge in evidence-based medicine (EBM) and our assessment of the relevance of their dispositionalist ontology for EBM. Three issues need to be addressed in response: (1) problems concerning transfer of causal knowledge across heterogeneous contexts; (2) how predictions about the effects of individual treatments based on population-level evidence from RCTs are fallible; and (3) the relevance of ontological theories like dispositionalism for EBM.

In this paper, first, I distinguish four questions concerning evidence: (a) the ontological question: what kind of entity qualifies as evidence? (b) the possession question: what is it for S to possess evidence? (c) the evidential relation question: what is it for one or a set of things to be evidence for another? And (d) the evidential basis question: how does S’s evidence contribute to forming, maintaining, or revising S’s doxastic attitudes? Williamson’s E = K thesis is only concerned with (...) question (b). Next, I argue that three recent arguments against the E = K thesis miss the target either because they combine the E = K thesis with something alien, or because they conflate these questions. The arguments I address are the argument from access to evidence, the argument from evidential bootstrapping, and the argument from true belief based on falsehood. The argument from access to evidence combines the E = K thesis with a quasi-skeptical consideration. The argument from evidential bootstrapping is only directed at Williamson’s theory of probability, which is his answer to question (c). The argument from true belief based on falsehood derives its force from an assumption about the basing relation as an answer to question (d). Although I do not deny that it is possible to argue for or against the E = K thesis by appealing to answers to other questions than (b), it is no easy task. (shrink)

Alcohol addiction can lead to health and social problems. It can also affect people's emotions. Emotion plays a key role in human communications. It is important to recognize the people's emotions at the court and infer the association between the people's emotions and the alcohol addiction. However, it is challenging to recognize people's emotions efficiently in the courtroom. Furthermore, to the best of our knowledge, no existing work is about the association between alcohol addiction and people's emotions at court. (...) In this paper, we propose a deep learning framework for predicting people's emotions based on sound perception, named ResCNN-SER. The proposed model combines several neural network-based components to extract the features of the speech signals and predict the emotions. The evaluation shows that the proposed model performs better than existing methods. By applying ResCNN-SER for emotion recognition based on people's voices at court, we infer the association between alcohol addiction and the defendant's emotion at court. Based on the sound source data from 54 trial records, we found that the defendants with alcohol addiction tend to get angry or fearful more easily at court comparing with defendants without alcohol addiction. (shrink)

Although the connectionist approach has lead to elegant solutions to a number of problems in cognitive science and artificial intelligence, its suitability for dealing with problems in knowledge representation and inference has often been questioned. This paper partly answers this criticism by demonstrating that effective solutions to certain problems in knowledge representation and limited inference can be found by adopting a connectionist approach. The paper presents a connectionist realization of semantic networks, that is, it describes how (...) knowledge about concepts, their properties, and the hierarchical relationship between them may be encoded as an interpreter‐free massively parallel network of simple processing elements that can solve an interesting class of inheritance and recognition problems extremely fast—in time proportional to the depth of the conceptual hierarchy. The connectionist realization is based on an evidential formulation that leads to principled solutions to the problems of exceptions and conflicting multiple inheritance situations during inheritance, and the best‐match or partial‐match computation during recognition. The paper also identifies constraints that must be satisfied by the conceptual structure in order to arrive at an efficient parallel realization. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Philosophy and Rhetoric 34.2 (2001) 93-112 [Access article in PDF] Enthymemes, Common Knowledge, and Plausible Inference Douglas Walton The study of enthymemes has always been regarded as important in logic, critical thinking, and rhetoric, but too often it is the formal or mechanistic aspect of it that has been in the forefront. This investigation will show that there is a kind of plausibilistic script-based reasoning, of (...) a kind that has mainly been studied in artificial intelligence, that should have a much more important role to play in the study of enthymemes. But then curiously, as will also be shown, this plausibilistic type of reasoning was familiar in the ancient world, to the Sophists, as well as to leading philosophers such as Plato and Aristotle. By linking this ancient notion of plausibility to the modern notion used in computer science, this investigation reveals an important basis for the enthymeme that has a type of logical structure in its own right, but also has an informal aspect.An enthymeme, in current usage, is an argument that has one or more premises, or possibly a conclusion, not explicitly stated in the text, but that needs to have these propositions explicitly stated to extract the complete argument from the text. Sometimes enthymemes are described as arguments with "missing premises." That vocabulary is awkward, however, because the nonexplicit statement that needs to be added can be a conclusion, at least in a minority of cases. To make the exposition below smoother, the term nonexplicit assumption will be used to cover either the case of a nonexplicit premise or that of a nonexplicit conclusion. The problem with enthymemes is that if the nonexplicit assumptions in an argument are supposed to be propositions used by the arguer (as opposed to just the propositions needed to make the argument structurally correct, according to some standard), reasonable people can have differences of opinion on what the nonexplicit assumptions are supposed to be. The problem is that filling in the missing parts of enthymemes depends on interpreting the natural language [End Page 93] in which the argument was put forward to try to determine what the speaker meant to say.The solution to the problem comes through the recognition that enthymemes rest not only on formal (structural) criteria, but also on informal criteria. One of the most important of these informal criteria is something often called "common knowledge" (Govier 1992; Freeman 1995). But as shown below in a set of selected case studies of enthymemes, "common knowledge" is not really a kind of knowledge at all. It is really plausibility, or eikos, something well known in the ancient world and often misleadingly translated as "probability." Curiously, Aristotle's original doctrine of the enthymeme was based on this notion of plausibility. This historical fact has often been a source of puzzlement and confusion, and sometimes it has even been taken to indicate a defect or contradiction in Aristotle's treatment of the enthymeme. However, the goal of this investigation is not primarily historical. It is to work out one of the most important required steps toward a solution to the problem of enthymemes. But to do this, it is necessary to come back to the ancient notion of plausible inference. 1. The problem with enthymemes Once you have any formal theory of inference, like syllogistic, for example, you will be confronted by cases of arguments in everyday discourse that meet all the requirements for a structurally correct inference except that some part or parts, usually a premise or a conclusion, is missing. By "missing" is meant that the proposition in question has not been explicitly stated in the text of discourse, even though it may be clear enough that the speaker (writer) was relying on it, or including it, as part of the argument. The classic example is the following inference: All men are mortal; therefore, Socrates is mortal. To evaluate such an argument, surely some account of the "hidden" or "missing" assumption needs to be taken. The problem with enthymemes (Burke 1985; Gough and Tindale 1985; Hitchcock 1985) is that, if given carte... (shrink)

This paper critically evaluates central themes of values-based medicine (VBM). First, I discuss the 'non-descriptivist' conception of value judgments at the heart of VBM. According to it, no inferences can rationally be drawn from factual criteria to value judgments and the inferences that are naturally formed are a matter of human psychology. I argue, however, that it is an essential feature of value judgments that they are themselves subject to normative assessment. This implies an important role for an evaluatively (...) rich notion of psychology. Second, I discuss the role of 'dissensus' in VBM and argue that, although value judgments need not converge, the processes for exploring conflicting values must, themselves, be normatively assessable. Third, I examine how these aspects interact in complex real cases such as the case study of Diane Abbot. (shrink)

In informal terms, abductive reasoning involves inferring the best or most plausible explanation from a given set of facts or data. It is a common occurrence in everyday life and crops up in such diverse places as medical diagnosis, scientific theory formation, accident investigation, language understanding, and jury deliberation. In recent years, it has become a popular and fruitful topic in artificial intelligence research. This volume breaks new ground in the scientific, philosophical, and technological study of abduction. It presents new (...) ideas about inferential and information-processing foundations for knowledge and certainty. The authors argue that knowledge arises from experience by processes of abductive inference, in contrast to the view that it arises non-inferentially, or that deduction and inductive generalization are enough to account for knowledge. Much AI research is hypothetical, so the importance of this book is that it reports key discoveries about abduction that have been made as a result of designing, building, testing, and analyzing actual working knowledge-based systems for medical diagnosis and other abductive tasks. The book tells the story of six generations of increasingly sophisticated generic abduction machines, RED-1, RED-2, PEIRCE, MDX2, TIPS, QUAWDS, and the discovery of reasoning strategies that make it computationally feasible to form well-justified composite explanatory hypotheses, despite the threat of combinatorial explosion. The final chapter argues that perception is logically abductive and presents a layered-abduction computational model of perceptual information processing. This book will be of great interest to researchers in AI, cognitive science, and philosophy of science. (shrink)

Gossip has been the object of a number of different studies in the past 50 years, rehabilitating it not only as something worth being studied, but also as a pivotal informational and social structure of human cognition: Dunbar (Rev Gen Psychol 8(2):100–110, 2004) interestingly linked the emergence of language to nothing less than its ability to afford gossip. Different facets of gossip were analyzed by anthropologists, linguists, psychologists and philosophers, but few attempts were made to frame gossip within an epistemological (...) framework (for instance Ayim in (Good gossip, pp. 85–99, 1994)). Our intention in this paper is to provide a consistent epistemological (applied and social) account of gossip, understood as broadly evaluative talk between two or more people, comfortably acquainted between each other, about an absent third party they are both at least acquainted with. Hence, relying on the most recent multidisciplinary literature about the topic, the first part of this paper will concern the epistemic dynamics of gossip: whereas the sociobiological tradition individuates in gossip the clue for the (theoretically cumbersome) group mind and group-level adaptations Wilson et al. (The evolution of cognition, pp. 347–365, 2002), we will suggest the more parsimonious modeling of gossip as a soft-assembled epistemic synergy, understood as a function-dominant interaction able to project a higher organizational level—in our case, the group as group-of-gossips. We will argue that the aim of this synergy is indeed to update a Knowledge Base of social information between the group (as a projected whole) and its members. The second and third part will instead focus on the epistemological labeling of the inferences characterizing gossip: our contention is that the ever-present moral/evaluative dimension in gossip—be it tacit or explicit, concerning the objects or the partners of gossip—is best analyzed through the epistemological framework of abduction. Consequently, we will suggest that a significant role of gossip is to function as a group-based abductive appraisal of social matter, enacted at various levels. (shrink)

PROBEX (PROBabilities from EXemplars), a model of probabilistic inference and probability judgment based on generic knowledge is presented. Its properties are that: (a) it provides an exemplar model satisfying bounded rationality; (b) it is a “lazy” algorithm that presumes no pre‐computed abstractions; (c) it implements a hybrid‐representation, similarity‐graded probability. We investigate the ecological rationality of PROBEX and find that it compares favorably with Take‐The‐Best and multiple regression (Gigerenzer, Todd, & the ABC Research Group, 1999). PROBEX is fitted (...) to the point estimates, decisions, and probability assessments by human participants. The best fit is obtained for a version that weights frequency heavily and retrieves only two exemplars. It is proposed that PROBEX implements speed and frugality in a psychologically plausible way. (shrink)

In Lehrer’s case of the superstitious lawyer, a lawyer possesses conclusive evidence for his client’s innocence, and he appreciates that the evidence is conclusive, but the evidence is causally inert with respect to his belief in his client’s innocence. This case has divided epistemologists ever since Lehrer originally proposed it in his argument against causal analyses of knowledge. Some have taken the claim that the lawyer bases his belief on the evidence as a data point for our theories to (...) accommodate, while others have denied that the lawyer has knowledge, or that he bases his belief on the evidence. In this paper, we move the dialectic forward by way of arguing that the superstitious lawyer genuinely infers his client’s innocence from the evidence. To show that the lawyer’s inference is genuine, we argue in defense of a version of a doxastic construal of the ‘taking’ condition on inference. We also provide a pared-down superstitious lawyer-style case, which displays the key features of the original case without including its complicated and distracting features. But interestingly, although we argue that the lawyer’s belief is based on his good evidence, and is also plausibly doxastically justified, we do not argue that the lawyer knows that his client is innocent. (shrink)

Participants were unknowingly exposed to complex regularities in a working memory task. The existence of implicit knowledge was subsequently inferred from a preference for stimuli with similar grammatical regularities. Several affective traits have been shown to influence AGL performance positively, many of which are related to a tendency for automatic responding. We therefore tested whether the mindfulness trait predicted a reduction of grammatically congruent preferences, and used emotional primes to explore the influence of affect. Mindfulness was shown to correlate (...) negatively with grammatically congruent responses. Negative primes were shown to result in faster and more negative evaluations. We conclude that grammatically congruent preference ratings rely on habitual responses, and that our findings provide empirical evidence for the non-reactive disposition of the mindfulness trait. (shrink)

Reliabilism is a theory that countenances basic knowledge, that is, knowledge from a reliable source, without requiring that the agent knows the source is reliable. Critics (especially Cohen 2002 ) have argued that such theories generate all-too-easy, intuitively implausible cases of higher-order knowledge based on inference from basic knowledge. For present purposes, the criticism might be recast as claiming that reliabilism implausibly generates cases of understanding from brute, basic knowledge. I argue that the (...) easy knowledge (or easy understanding) criticism rests on an implicit mischaracterization of the notion of a reliable process. Properly understood, reliable processes do not permit the transition from basic knowledge to understanding based on inference. (shrink)

In this paper, the problem of purifying an assumption-based theory KB, i.e., identifying the right extension of KB using knowledge-gathering actions (tests), is addressed. Assumptions are just normal defaults without prerequisite. Each assumption represents all the information conveyed by an agent, and every agent is associated with a (possibly empty) set of tests. Through the execution of tests, the epistemic status of assumptions can change from "plausible" to "certainly true", "certainly false" or "irrelevant", and the KB must be (...) revised so as to incorporate such a change. Because removing all the extensions of an assumption-based theory except one enables both identifying a larger set of plausible pieces of information and renders inference computationally easier, we are specifically interested in finding out sets of tests allowing to purify a KB (whatever their outcomes). We address this problem especially from the point of view of computational complexity. (shrink)

What is involved in making an inference? This chapter argues against what Paul Boghossian calls the Taking Condition: "Inferring necessarily involves the thinker taking his premises to support his conclusion and drawing his conclusion because of that fact" (2014: 5). I won’t argue that the Taking Condition is incoherent: that nothing can coherently play the role that takings are supposed to play in inference. Instead, I’ll argue that it cannot plausibly explain all the inferential knowledge that we (...) ordinarily take ourselves to have. Moreover, I’ll argue that we don’t need it to understand the nature of inference. (shrink)

In this paper I deal with epistemological issues that stem from the hypothesis that reasoning is not only a means of transmitting knowledge from premise-beliefs to conclusion-beliefs, but also a primary source of knowledge in its own right. The idea is that one can gain new knowledge on the basis of suppositional reasoning. After making some preliminary distinctions, I argue that there are no good reasons to think that purported examples of knowledge grounded on pure reasoning (...) are just examples of premise-based inferences in disguise. Next, I establish what kinds of true propositions can to a first approximation be known on the basis of pure reasoning. Finally, I argue that beliefs that are competently formed on the basis of suppositional reasoning satisfy both externalist and internalist criteria of justification. (shrink)

This paper compares three accounts of what can be inferred from a knowledge base that contains conditionals: Lehmann and Magidor’s Rational Entailment; Pearl’s System Z, later extended and refined in collaboration with Goldszmidt; and the present author’s Nonmonotonic conditional logic for belief revision. We show that although the ideas motivating these systems are strikingly different, they are formally equivalent. An explanation of the surprising parallel is offered in terms of the interpretation of conditionals in the context of nonmonotonic reasoning (...) and belief revision. Finally, some common problems with the equivalent systems are outlined, as well as some problems in assessing these problems which indicate that a general definition of dependence between the items in a knowledge base is needed. (shrink)

Some claim that consent requires common knowledge. For a doctor to obtain consent, a doctor must know that her patient has given her permission to perform surgery, and her patient must know the doctor knows that he has given this permission. Some claim that such common knowledge requires communication, and so consent requires communication: the patient must tell the doctor he consents for both to know consent took place, and for both to know the other knows consent took (...) place. I first defend the claim that consent requires common knowledge, responding to recent objections. I then argue that, though consent requires common knowledge, it does not always require communication. It does not require communication when the agent obtaining consent can infer common knowledge based on non-behavioral facts about the world. (shrink)

Chastain and Sawyer, among others, claim that direct cognitive relations can be initiated in evidence cases. Direct cognitive relations will here include Chastain’s knowledge-of and Sawyer’s trace-based acquaintance, as well as related notions such as having-in-mind and singular thought. Against this controversial claim, it is often objected that such cases are better understood as cases of inference rather than cases of direct thought. When one detects something by its footprint, the objection goes, one merely infers that it (...) exists rather than thinking of it directly. The goal of this paper is to analyze what is meant by the inference objection and consider several possible responses to it. Ultimately, I will not offer a knock-down argument against the inference explanation; in fact, I’ll try to explain why I suspect one isn’t possible. Instead, I’ll appeal to the possibility of misdescription and analogous cases involving non-human animals to show that the inference explanation is less plausible than the account to which it provides an alternative. (shrink)

In this paper, the traditional view that argumentum ad ignorantiam is a logical fallacy is challenged, and lessons are drawn on how to model inferences drawn from knowledge in combination with ones drawn from lack of knowledge. Five defeasible rules for evaluating knowledge-based arguments that apply to inferences drawn under conditions of lack of knowledge are formulated. They are the veridicality rule, the consistency of knowledge rule, the closure of knowledge rule, the rule (...) of refutation and the rule for argument from ignorance. The basic thesis of the paper is that knowledge-based arguments, including the argument from ignorance, need to be evaluated by criteria for epistemic closure and other evidential rules that are pragmatic in nature, that need to be formulated and applied differently at different stages of an investigation or discussion. The paper helps us to understand practical criteria that should be used to evaluate all arguments based on knowledge and/or ignorance. (shrink)

We discuss the role of perceptron (or threshold) connectives in the context of Description Logic, and in particular their possible use as a bridge between statistical learning of models from data and logical reasoning over knowledge bases. We prove that such connectives can be added to the language of most forms of Description Logic without increasing the complexity of the corresponding inference problem. We show, with a practical example over the Gene Ontology, how even simple instances of perceptron (...) connectives are expressive enough to represent learned, complex concepts derived from real use cases. This opens up the possibility to import concepts learnt from data into existing ontologies. (shrink)

Philosophers of logic are particularly interested in understanding the aims, epistemology, and methodology of logic. This raises the question of how the philosophy of logic should go about these enquires. According to the practice-based approach, the most reliable method we have to investigate the methodology and epistemology of a research field is by considering in detail the activities of its practitioners. This holds just as true for logic as it does for the recognised empirical and abstract sciences. If we (...) wish to systematically understand the aims and epistemology of logic, we are best placed achieving this by looking at what logicians do, rather than reflecting upon the nature of logic itself. In this entry, we outline the motivations for a practice-based approach towards the philosophy of logic and highlight its advantages over more “top-down” approaches, which attempt to infer conclusions about the nature of logic and its methodology on the basis of traditional assumptions about knowledge, metaphysics, or logic itself. We end by addressing several prominent concerns raised against the practice-based approach. (shrink)

This work has been selected by scholars as being culturally important and is part of the knowledge base of civilization as we know it. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, (...) and made generally available to the public. To ensure a quality reading experience, this work has been proofread and republished using a format that seamlessly blends the original graphical elements with text in an easy-to-read typeface. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. (shrink)

Classical epistemic logic describes implicit knowledge of agents about facts and knowledge of other agents, based on semantic information. The latter is produced by acts of observation or communication, that are described well by dynamic epistemic logics. What these logics do not describe, however, is how significant information is also produced by acts of inference – and key axioms of the system merely postulate “deductive closure”. In this paper, we take the view that all information is (...) produced by acts, and hence we also need a dynamic logic of inference steps showing what effort on the part of the agent makes a conclusion explicit knowledge. Strong omniscience properties of agents should be seen not as static idealizations, but as the result of dynamic processes that agents engage in. This raises two questions: (a) how to define suitable information states of agents and matching notions of explicit knowledge, (b) how to define natural processes over these states that generate new explicit knowledge. To this end, we extend earlier epistemic “awareness models” into a dynamic system that includes acts of public observation, but also adding and dropping formulas from the currently ‘entertained’ set, we give a completeness theorem, and we show how this dynamics updates explicit knowledge. Similar ideas have been proposed before, but they were restricted to update with factual propositions; our new dynamic system applies to arbitrary formulas. We also extend our approach to multi-agent scenarios where awareness changes may happen privately. Finally, we mention further directions and related approaches. (shrink)

We present a method for relevance sensitive non-monotonic inference from belief sequences which incorporates insights pertaining to prioritized inference and relevance sensitive, inconsistency tolerant belief revision. Our model uses a finite, logically open sequence of propositional formulas as a representation for beliefs and defines a notion of inference from maxiconsistent subsets of formulas guided by two orderings: a temporal sequencing and an ordering based on relevance relations between the putative conclusion and formulas in the sequence. The (...) relevance relations are ternary (using context as a parameter) as opposed to standard binary axiomatizations. The inference operation thus defined easily handles iterated revision by maintaining a revision history, blocks the derivation of inconsistent answers from a possibly inconsistent sequence and maintains the distinction between explicit and implicit beliefs. In doing so, it provides a finitely presented formalism and a plausible model of reasoning for automated agents. (shrink)

The aim of this book is to present the fundamental theoretical results concerning inference rules in deductive formal systems. Primary attention is focused on: admissible or permissible inference rules the derivability of the admissible inference rules the structural completeness of logics the bases for admissible and valid inference rules. There is particular emphasis on propositional non-standard logics (primary, superintuitionistic and modal logics) but general logical consequence relations and classical first-order theories are also considered. The book is (...) basically self-contained and special attention has been made to present the material in a convenient manner for the reader. Proofs of results, many of which are not readily available elsewhere, are also included. The book is written at a level appropriate for first-year graduate students in mathematics or computer science. Although some knowledge of elementary logic and universal algebra are necessary, the first chapter includes all the results from universal algebra and logic that the reader needs. For graduate students in mathematics and computer science the book is an excellent textbook. (shrink)

This thesis proposes that an account of first-person reference and first-person thinking requires an account of practical knowledge. At a minimum, first-person reference requires at least a capacity for knowledge of the intentional act of reference. More typically, first-person reasoning requires deliberation and the ability to draw inferences while entertaining different 'I' thoughts. Other accounts of first-person reference--such as the perceptual account and the rule-based account--are criticized as inadequate. An account of practical knowledge is provided by (...) an interpretation of GEM Anscombe's account in her landmark monograph "Intention". (shrink)

This work has been selected by scholars as being culturally important and is part of the knowledge base of civilization as we know it. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to (...) be preserved, reproduced, and made generally available to the public. To ensure a quality reading experience, this work has been proofread and republished using a format that seamlessly blends the original graphical elements with text in an easy-to-read typeface. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. (shrink)

Hintikka argues that abduction is ignorance-preserving in the sense that the hypothesis that abduction delivers and which attempts to explain a set of phenomena is not, epistemologically speaking, on a firmer ground than the phenomena it purports to explain; knowledge is not enhanced until the hypothesis undergoes a further inductive process that will test it against empirical evidence. Hintikka, therefore, introduces a wedge between the abductive process properly speaking and the inductive process of hypothesis testing. Similarly, Minnameier argues that (...) abduction differs from the inference to the best explanation since the former describes the process of generation of theories, while the latter describes the, inductive, process of their evaluation. As Hintikka so Minnameier traces this view back to Peirce’s work on abduction. Recent work on abduction goes as far as to draw a distinction between abducting an hypothesis that is considered worth conjecturing and the decision either to use further this hypothesis to do some inferential work in the given domain of enquiry, or to test it experimentally. The latter step, when it takes place, is an inductive mode of inference that should be distinguished from the abductive inference that led to the hypothesis. In this paper, I argue that in real scientific practise both the distinction between a properly speaking abductive phase and an inductive phase of hypothesis testing and evaluation, and the distinction between testing an hypothesis that has been discovered in a preceding abduction and releasing or activating the same hypothesis for further inferential work in the domain of enquiry in which the ignorance problem arose in the first place are blurred because all these processes form an inextricable whole of theory development and elaboration and this defies and any attempt to analyze this intricate process into discrete well defined steps. Thus, my arguments reinforce Magnani’s view on abduction and its function in scientific practise. (shrink)

‘all A are B’ ; A ⊆ B ‘no A are B’ ; A ⊆ B ‘some A are not B’ ; A ⊆ B ‘some A are B’ ; A ⊆ B (equivalently: A ∩ B = ∅). A knowledge base is a list of triples (Class1, Class2, Boolean) where (A, B, ) expresses that A ⊆ B, and (A, B, ⊥) expresses that A ⊆ B.