We propose a logic to reason about data collected by a number of measurement systems. The semantic of this logic is grounded on the epistemic theory of measurement that gives a central role to measurement devices and calibration. In this perspective, the lack of evidences (in the available data) for the truth or falsehood of a proposition requires the introduction of a third truth-value (the undetermined). Moreover, the data collected by a given source are here represented by means of a possible world, which provide a contextual view on the objects in the domain. We approach (possibly) conflicting data coming from different sources in a social choice theoretic fashion: we investigate viable operators to aggregate data and we represent them in our logic by means of suitable (minimal) modal operators.
This paper is about the norm of truth for assertion, which I henceforth call “The Truth Rule”, and is formulated as follows: “One ought to assert only what is true”. I argue that The Truth Rule as thus formulated is a norm for assertion in a specific sense. I defend the view that assertion is, by its nature, governed by the rule according to which one ought to assert only what is true.
Persuasive messages have recently been shown to be more effective when tailored to the personality and preferences of the recipient. However, much of the literature on adaptive persuasion has evaluated the effectiveness of persuasive attempts by the direct reactions to those attempts instead of changes on the longer term (e.g. lifestyle changes). Results of this study suggest that adaptive persuasion improves attitudes towards persuasive attempts, but does not necessarily cause a change in longer term behavior. This was found through a randomized controlled trial evaluating the implementation an adaptive persuasive system in a health promotion intervention. This article provides a detailed description of this evaluation and encourages the research community to (1) become more skeptical towards the longer term effectiveness of adaptive persuasive techniques and (2) design more explicitly for longer term changes in behavior.
This paper presents a model of contextual awareness implemented for a social communicative robot Leolani. Our model starts from the assumption that robots and humans need to establish a common ground about the world they share. This is not trivial as robots make many errors and start with little knowledge. As such, the context in which communication takes place can both help and complicate the interaction: if the context is interpreted correctly it helps in disambiguating the signals, but if it is interpreted wrongly it may distort interpretation. We defined the surrounding world as a spatial context, the communication as a discourse context and the interaction as a social context, which are all three interconnected and have an impact on each other. We model the result of the interpretations as symbolic knowledge (RDF) in a triple store to reason over the result, detect conflicts, uncertainty and gaps. We explain how our model tries to combine the contexts and the signal interpretation and we mention future directions of research to improve this complex process.
Generics are sentences that express generalizations about a category or about its members. They display a characteristic context-sensitivity: the same generic can express a statistical regularity, a principled connection, or a norm. Sally Haslanger (2014) argues that this phenomenon depends on the implicit content that generics carry in different contexts. I elaborate on Haslanger’s proposal, arguing that the implicit content of generics is complex and constituted by two different propositions. A first proposition, that I here call the robustness proposition, characterizes as robust the link between the category the generic is about and the predicated property. This proposition is relatively invariant and is, as I claimed elsewhere, a generalized conversational implicature. In this paper, I will argue that a second implicature, the ‘explanatory implicature’, arises which crucially depends on what explanation is called for in a certain context. Given its context-dependence, I conclude that this proposition is a particularized conversational implicature. While generics convey by default that the category and the property are strictly related, the specification of this relation hinges on the characteristics of the context in which the generic occurs.
After presenting two forms of Contextualism, I will argue that the phenomenon of polysemy supports the stronger one – so-called Radical Contextualism. My argument will be based on a comparison between indexicality and polysemy.
We propose an approach for modelling, integrating, and querying distributed probabilistic contexts in multi-agent systems. We assume each agent to be equipped with an independently acquired uncertain context. By taking advantage of established database technologies, we represent the uncertain context of each agent as a set of probabilistic facts conveniently stored in a probabilistic database. Members of a multi-agent system act autonomously and interact with each other. The interaction between agents consists of sharing access to their contexts with each other and allowing queries over the combined shared contexts. This amounts to the challenge of combining and querying distributed probabilistic databases. To combine probabilistic contexts, we define a context-matching operator that creates a joint probability distribution with given marginal probabilities. Furthermore, we propose a query answering method over combinations of probabilistic contexts.
This paper approaches traditional puzzles about belief and belief attributions as if they involved instances of paradoxes of identity. I shall argue that the solution to these puzzles comes with a proper understanding of the way we identify individuals in situations where their persistence conditions allow for a “split” (through time or possible worlds) and the way context constraints how we talk about them. My aim in this work is to outline the basics of such a solution and show how well-motivated it is compared to more conventional alternatives.
Compositionality and contextuality give two fundamental principles of linguistic analysis, and yet there is a conflict between them as Burge, Dummett, and others find. Here we aim at elucidating conceptual views underlying their tension in light of both symbolic and statistical paradigms of semantics, arguing, inter alia, that: (i) the conflict is a case of vicious circle analogous to hermeneutic circularity, and may be understood as a tension between symbolic and statistical semantics; (ii) the productivity, systematicity, and learnability of language can be accounted for in accordance with the principle of contextuality as well as compositionality; and (iii) the Chomsky versus Norvig debate on the (symbolic versus statistical) nature of language may be considered a broader manifestation of the tension in the form of the traditional conflict in philosophy between rationalist and empiricist worldviews. We conclude the paper with an outlook for the Kantian synthesis of them, especially the categorical integration of symbolic and statistical AI.
Setswana is an under-resourced Bantu African language that is morphologically rich with the disjunctive writing system. Developing NLP pipeline tools for such a language could be challenging, due to the need to balance the linguistics semantics robustness of the tool with computational parsimony. A Part-of-Speech (POS) tagger is one such NLP tool for assigning lexical categories like noun, verb, pronoun, and so on, to each word in a text corpus. POS tagging is an important task in Natural Language Processing (NLP) applications such as information extraction, Machine Translation, Word prediction, etc. Developing a POS tagger for a morphologically rich language such as Setswana has computational linguistics challenges that could affect the effectiveness of the entire NLP system. This is due to some contextual semantics features of the language, that demand a fine-grained granularity level for the required POS tagset, with the need to balance tool semantic robustness with computational parsimony. In this paper, a context-driven corpus-based model for text segmentation and POS tagging for the language is presented. The tagger is developed using the Apache OpenNLP tool and returns the accuracy of 96.73%.
Image schemas were introduced as mental generalisations learned from the sensorimotor experiences in infancy that in adulthood shape language formation and conceptualisations. So far, little empirical research has been devoted to investigate to which degree image schemas are involved in object conceptualisation more concretely. To address this, this experimental study investigates the relationship between abstract image schemas and their involvement in conceptualisations of common, everyday objects. The experimental set-up asks participants to describe objects using abstract representations of image schemas. The results from the study support the claim that image-schematic thinking is prevalent in the conceptualisation of objects, thus providing empirical evidence for the idea that image schemas can serve as conceptual building blocks for the meaning of objects.
National Geoinformation Center of Bulgaria is a national scientific infrastructure – a consortium with a mission to produce and provide value-added products of Earth observation data gathered by various agencies governed by the state or received through international cooperation. The architecture of the information system of the center is presented – a layered structure that implements concepts of service and microservice, building an organization that reflects the federated nature of the consortium. The paper reviews various implemented design decisions within the perspective of context-aware systems. Four sources of data exploited for the determination of the context of the user are described. The paper concludes with three scenarios prepared as patterns for building context-aware services within the center.
The paper discusses the idea of hybrid names. First, the three theories of hybrid names (according to Künne, Kripke and Textor) are briefly discussed and compared. Second, the paper briefly discusses some problems that the theories face. Third, an alternative hybrid view is outlined. According to that view, utterances are contextually perduring objects. It is argued that in order to determine the contextual distribution of a particular utterance, one has to take into account the admissible distributions of contextual parameters, their potential referents and the speaker’s referential intentions. Finally, the merits of the view are briefly discussed. Two of the most important are: the analysis of cases of multiple occurrences of indexicals and demonstratives, and the solution to the so-called problem of missing demonstrations.
Actual interpretive practice, whether colloquial or formal, is underdetermined with respect to whether qualifications of utterances as racist are based on the speaker’s attitude, communicative intention or the meaning of the utterance and whether in the latter case the racist meaning can be implicit or must be explicit. The focus on the speaker’s expression of evaluation and attitude in current theorizing about slurs involves a similar underdetermination. A common problem is that the speaker’s responsibility is referred to the speaker’s own authority. The interactional model of utterance interpretation which forms the theoretical background of the present paper permits us to elaborate a novel and robust conception of speaker responsibility which situates it within the hearer’s authority. The speaker’s responsibility depends on the hearer’s most reasonable interpretation of the utterance in such a way that the speaker’s actual attitude and intention are irrelevant and also whether the meaning was explicitly or only implicitly conveyed.
A retail environment can be thought as an environment where customers can buy products, goods or services. The user-experience in physical retail environments is important not only for facilitating the selling of goods and services, but also for providing satisfaction and appealing to retain customers over the long term. The user-experience can be enhanced by adapting aspects of the physical environment such as music, colour, fragrance to the tastes of the customers.
Our paper works on a proposal recently put forward by Hunter, Asher and Lascarides (2018) on the use of events in discourse context. We basically accept their view and their proposal of using events as explanation in discourse context. However we think that a stricter connection with demonstrations and causal reasoning in everyday conversation is a necessary step in a coherent view of discourse context. We will not deal with any particular formalism, but only with the general problem of taking into account some elements that may simplify or explain what is taken for granted in some steps of our inferences. A central concept used in these setting is the concept of “explanation” as a way to give coherence to the discourse context. This kind of explanation is also based, besides elements of a general encyclopedic knowledge, on default assumptions derived by the ontology present in the lexicon as Asher (2011) has abundantly shown. However, the steps to recover such coherence would gain clarity with a better specification of causal explanation and with a more precise account of the relation between demonstrative and demonstrations in discourse context. On these two aspects we give some suggestions.
Organizations learn by comparing practices of employees under different aspects and leverage the lessons learned in improved procedures [9]. This paper reports on a pilot research project that shows how the theory of practice-based organizational learning can be transposed to the classroom. Students’ insights about their learning practice are linked to, and reflected in, better learning performance in the subject matter. Peer-controlled self-evaluation is used to measure subject-matter understanding on a scale inspired by Lonergan’s cognitional theory [12]. The pilot research project presented here was undertaken in the 2018/2019 academic year in a French business school in a management class. A multi-class research project in the same school is planned by the authors for the academic year 2019/2020.
Contextuality is a transdisciplinary phenomenon observed across the sciences, including in particular physics, linguistics, artificial intelligence, and cognitive science. In the present paper we shed new light on cognitive contextuality based upon recent developments of quantum cognitive science. We first discuss quantum cognitive science from the perspective of contextuality studies, and formulate the basic tenet of quantum cognitive science as the quantum cognition thesis or the structural quantum mind thesis as opposed to Penrose’s (controversial) quantum brain thesis or material quantum mind thesis. We then discuss Bell-type contextuality results in physics and in cognitive science, and elucidate fundamental differences between cognitive contextuality and physical contextuality. Quantum cognitive science allows us to explicate similarities and dissimilarities between the laws of matter and the laws of mind, contributing to a deeper understanding of the Cartesian dualism.
The General Data Protection Regulation, e.g., provides the “right of access by the data subject” and demands explanations of data usages, i.e. explanations where and for what purpose personal data is being processed. Supporting this kind of privacy control and related personalized explanations of data usage in context-based adaptive collaboration environments are big challenges. Currently, users cannot retrace the usage and the storage of their personal data in context-based adaptive collaboration environments. We address the aforementioned challenges by developing a context-based adaptive collaboration platform, the CONTact platform, that can be linked to or integrated into different kinds of collaboration environments (e.g., meinDorf55+, a novel community support system for elderly). The CONTact platform supports users with privacy control and personalized explanations of data usages. In this paper we present an excerpt of our extended domain model and two sample situations when privacy control and personalized explanations get relevant. We use a sample ontology that is based on our domain model to illustrate the related processes and rules. Using our approach users can control their data usage and are able to get personalized explanations of their data usage in a context-based adaptive collaboration environment. This helps us observing legal regulations, e.g. privacy laws like the GDPR.
This paper is an appendix to the paper "Reasoning with Justifiable Exceptions in Contextual Hierarchies" by Bozzato, Serafini and Eiter, 2018. It provides further details on the language, the complexity results and the datalog translation introduced in the main paper.
