Needs and Intentionality

Biccheri, Luca; Ferrario, Roberta; Porello, Daniele

doi:10.3233/faia200666

Cited by 6 publications

(7 citation statements)

References 19 publications

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“…Differently from x, the variable y ranges over physical objects (POB), which can be other intentional agents 8 or technical artefacts (see [29]), that is to say, every physical object intentionally designed by agents so as to accomplish some goals. In line with [30], agents' goals are conceived of as intentional mental states that are about those entities called 'satisfiers'. According to our framework, satisfiers (sat) are perdurants (PD) that, occurring at a certain time, satisfy agents' goals.…”

Section: Id(x Y Z W)apo(x)∧pob(y)∧pd(z)∧goal(w)∧∃t(sat(z Wt)∧pc(y Zt)...mentioning

confidence: 99%

On the Relation of Instrumental Dependence

Biccheri,

Borgo,

Ferrario

2023

Frontiers in Artificial Intelligence and Applications

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The notion of trust has been traditionally investigated within many disciplines, ranging from sociology to economy, as well as politics, psychology, and philosophy. More recently, it is especially in the fields of AI, ICT, and Engineering (e.g., Critical systems), that the need for a discussion on the concept of trust, problematized in relation to the massive employment of technical artefacts in modern society, is becoming urgent. Yet, being a characteristic trait of human relationships, it is not clear whether the attitude of trust can also be directed towards artefacts. Moreover, with respect to the study of systems’ failures, the engineering sciences provide cognate notions to that of trust, e.g. reliability or dependability, which highlight our dependence on complex systems to fulfil certain tasks in a context of risk, uncertainty and vulnerability. In order to understand how far we can rely on technology, we should be able to understand, first of all, which kinds of dependencies are at stake. To this aim, in this paper, we will briefly review and discuss the main theoretical points related to trust and the technical notions mentioned, looking at both humanities and engineering literature. Then, we shall propose a preliminary ontological analysis aiming at comparing the specificities of the concepts concerned, all sharing a form of instrumental dependence.

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Section: Id(x Y Z W)apo(x)∧pob(y)∧pd(z)∧goal(w)∧∃t(sat(z Wt)∧pc(y Zt)...mentioning

confidence: 99%

On the Relation of Instrumental Dependence

Biccheri,

Borgo,

Ferrario

2023

Frontiers in Artificial Intelligence and Applications

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“…As a foundational ontology, DOLCE 1 provides general categories and relations that can be reused in different application scenarios by specializing them to the specific domains to be modeled.…”

Section: Introductionmentioning

confidence: 99%

“…Some extensions tackle knowledge representation's specific issues, like the modeling of roles [23], artifacts [30,3], and modules [10], others attempts an integration with machine learning and in particular computer vision [8]. Extensions to the modeling of social [6,28,26] and cognitive aspects [9,1] have also been proposed. Today DOLCE is becoming part of an ISO standard and is now available also in CLIF, a syntax of Common Logic [19].…”

Section: Introductionmentioning

confidence: 99%

“…Others showed a possible integration with machine learning and in particular computer vision (Conigliaro et al, 2017). Extensions to the modeling of social (Bottazzi and Ferrario, 2009;Porello et al, 2013Porello et al, , 2014a and cognitive aspects (Ferrario and Oltramari, 2004;Biccheri et al, 2020) have also been proposed. Today DOLCE is becoming part of the ISO 21838 standard, under development, and is available also in CLIF, a syntax of Common Logic ISO 24707 (2018).…”

Section: Introductionmentioning

confidence: 99%

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DOLCE: A descriptive ontology for linguistic and cognitive engineering1

Borgo

Ferrario

Gangemi

et al. 2022

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dolce, the first top-level (foundational) ontology to be axiomatized, has remained stable for twenty years and today is broadly used in a variety of domains. dolce is inspired by cognitive and linguistic considerations and aims to model a commonsense view of reality, like the one human beings exploit in everyday life in areas as diverse as socio-technical systems, manufacturing, financial transactions and cultural heritage. dolce clearly lists the ontological choices it is based upon, relies on philosophical principles, is richly formalized, and is built according to well-established ontological methodologies, e.g. OntoClean. Because of these features, it has inspired most of the existing top-level ontologies and has been used to develop or improve standards and public domain resources (e.g. CIDOC CRM, DBpedia and WordNet). Being a foundational ontology, dolce is not directly concerned with domain knowledge. Its purpose is to provide the general categories and relations needed to give a coherent view of reality, to integrate domain knowledge, and to mediate across domains. In these 20 years dolce has shown that applied ontologies can be stable and that interoperability across reference and domain ontologies is a reality. This paper briefly introduces the ontology and shows how to use it on a few modeling cases.

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“…Furthermore, ontologies are being applied to more abstract and complex domains such as business and law. Indeed, in the FOIS 2020 selected papers, we saw the prevalence of topics relating to these realms (see for instance [4,5,6]). Often in these domains we find that knowledge is described by referring to typical properties of a specific concept, but for specific cases the general theory can be overwritten.…”

Section: Introductionmentioning

confidence: 99%

Debugging Classical Ontologies Using Defeasible Reasoning Tools

Coetzer

Britz

2021

Frontiers in Artificial Intelligence and Applications

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A successful application of ontologies relies on representing as much accurate and relevant domain knowledge as possible, while maintaining logical consistency. As the successful implementation of a real-world ontology is likely to contain many concepts and intricate relationships between the concepts, it is necessary to follow a methodology for debugging and refining the ontology. Many ontology debugging approaches have been developed to help the knowledge engineer pinpoint the cause of logical inconsistencies and rectify them in a strategic way. We show that existing debugging approaches can lead to unintuitive results, which may lead the knowledge engineer to opt for deleting potentially crucial and nuanced knowledge. We provide a methodological and design foundation for weakening faulty axioms in a strategic way using defeasible reasoning tools. Our methodology draws from Rodler’s interactive ontology debugging approach and extends this approach by creating a methodology to systematically find conflict resolution recommendations. Importantly, our goal is not to convert a classical ontology to a defeasible ontology. Rather, we use the definition of exceptionality of a concept, which is central to the semantics of defeasible description logics, and the associated algorithm to determine the extent of a concept’s exceptionality (their ranking); then, starting with the statements containing the most general concepts (the least exceptional concepts) weakened versions of the original statements are constructed; this is done until all inconsistencies have been resolved.

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Needs and Intentionality

Cited by 6 publications

References 19 publications

On the Relation of Instrumental Dependence

On the Relation of Instrumental Dependence

DOLCE: A descriptive ontology for linguistic and cognitive engineering1

Debugging Classical Ontologies Using Defeasible Reasoning Tools

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