There is remarkable convergence between twentieth‐century interpretations of the image of God (imago Dei), what it means for human beings to be created in God's image, and approaches toward creating in our own image in the field of artificial intelligence (AI). Both fields have viewed the intersection between God and humanity or humanity and computers in terms of either (1) a property or set of properties such as intelligence, (2) the functions we engage in or are capable of, or (3) (...) the relationships we establish and maintain. Each of these three approaches reflects a different understanding of what stands at the core of our humanity. Functional and relational approaches were common in the late twentieth century, with a functional understanding the one most accepted by society at large. A relational view, however, gives new insights into human dignity in a computer age as well as new approaches to AI research. (shrink)

This article proposes a cost-benefit analysis of argumentation, with the aim of highlighting the strategic considerations that govern the agent's decision to argue or not. In spite of its paramount importance, the topic of argumentative decision-making has not received substantial attention in argumentation theories so far. We offer an explanation for this lack of consideration and propose a tripartite taxonomy and detailed description of the strategic reasons considered by arguers in their decision-making: benefits, costs, and dangers. We insist that the (...) implications of acknowledging the strategic dimension of arguing are far-reaching, including promising insights on how to develop better argumentation technologies. (shrink)

Susan Hurley's shared circuits model (SCM) rightly begins in action and progresses through a series of layers; but it fails to reach action understanding because it relies on mirroring as a driving force, draws on heavily criticized theories, and neglects the need for shared experience in our grasp of social understanding.

In this paper we expose the theoretical background underlying our current research. This consists in the development of behaviour-based knowledge systems, for closing the gaps between behaviour-based and knowledge-based systems, and also between the understandings of the phenomena they model. We expose the requirements and stages for developing behaviour-based knowledge systems and discuss their limits. We believe that these are necessary conditions for the development of higher order cognitive capacities, in artificial and natural cognitive systems.

In the past decade, the fields of machine learning and artificial intelligence (AI) have seen unprecedented developments that raise human-machine interactions (HMI) to the next level.Smart machines, i.e., machines endowed with artificially intelligent systems, have lost their character as mere instruments. This, at least, seems to be the case if one considers how humans experience their interactions with them. Smart machines are construed to serve complex functions involving increasing degrees of freedom, and they generate solutions not fully anticipated by humans. (...) Consequently, their performances show a touch of action and even autonomy. HMI is therefore often described as a sort of “cooperation” rather than as a mere application of a tool. Some authors even go as far as subsuming cooperation with smart machines under the label ofpartnership, akin to cooperation between human agents sharing a common goal. In this paper, we explore how far the notion of shared agency and partnership can take us in our understanding of human interaction with smart machines. Discussing different topoi related to partnerships in general, we suggest that different kinds of “partnership” depending on the form of interaction between agents need to be kept apart. Building upon these discussions, we propose a tentative taxonomy of different kinds of HMI distinguishing coordination, collaboration, cooperation, and social partnership. (shrink)

Ensuring the trustworthiness of autonomous systems and artificial intelligence is an important interdisciplinary endeavour. In this position paper, we argue that this endeavour will benefit from technical advancements in capturing various forms of responsibility, and we present a comprehensive research agenda to achieve this. In particular, we argue that ensuring the reliability of autonomous system can take advantage of technical approaches for quantifying degrees of responsibility and for coordinating tasks based on that. Moreover, we deem that, in certifying the legality (...) of an AI system, formal and computationally implementable notions of _responsibility_, _blame_, _accountability_, and _liability_ are applicable for addressing potential responsibility gaps (i.e. situations in which a group is responsible, but individuals’ responsibility may be unclear). This is a call to enable AI systems themselves, as well as those involved in the design, monitoring, and governance of AI systems, to represent and reason about who can be seen as responsible in prospect (e.g. for completing a task in future) and who can be seen as responsible retrospectively (e.g. for a failure that has already occurred). To that end, in this work, we show that across all stages of the design, development, and deployment of trustworthy autonomous systems (TAS), responsibility reasoning should play a key role. This position paper is the first step towards establishing a road map and research agenda on how the notion of responsibility can provide novel solution concepts for ensuring the _reliability_ and _legality_ of TAS and, as a result, enables an effective embedding of AI technologies into society. (shrink)

Standards and ontologies for manufacturing understand resources differently. Because of this heterogeneity, misunderstandings arise concerning the basic features that characterize them. The purpose of the paper is to investigate how to ontologically model resources with the goal of facilitating the development of knowledge representation models for manufacturing. By reviewing the literature, we discuss and compare three approaches for the representation of resources depending on whether they are conceived in connection to either processes, plans or goals. By addressing the advantages and (...) shortcomings of each view, we present a unifying perspective to enable the modeling of resources in an integrated manner. In this way, the intended meanings of the used notions are harmonized and, as a result, one can facilitate multiple experts to interact e.g., via data sharing and/or data integration procedures. Differently, by keeping three separated views, there is no guarantee that data coming from different parties will share common meanings even if the same terms are used. By the end of the paper, we present a case study to show the application of our approach and to compare it with an existing ontology for manufacturing. (shrink)

This article discusses three dimensions of creativity: metaphorical thinking; social interaction; and going beyond extrapolation in predictions. An overview of applications of neural networks in these three areas is offered. It is argued that the current reliance on the apparatus of statistical regression limits the scope of possibilities for neural networks in general, and in moving towards artificial creativity in particular. Artificial creativity may require revising some foundational principles on which neural networks are currently built.

In this article we strive to provide a detailed and principled analysis of the role of beliefs in goal processing—that is, the cognitive transition that leads from a mere desire to a proper intention. The resulting model of belief-based goal processing has also relevant consequences for the analysis of intentions, and constitutes the necessary core of a constructive theory of intentions, i.e. a framework that not only analyzes what an intention is, but also explains how it becomes what it is. (...) We discuss similarities and differences between our approach and other standard accounts of intention, in particular Bratman’s planning theory. The aim here is to question and refine the conceptual foundations of many theories of intentional action: as a consequence, although our analysis is not formal in itself, it is ultimately meant to have deep consequences for formal models of intentional agency. (shrink)

In this paper we propose a method for modeling social influence within the STIT approach to action. Our proposal consists in extending the STIT language with special operators that allow us to represent the consequences of an agent’s choices over the rational choices of another agent.

The aim of this paper is to clarify what kind of normativity characterizes a convention. First, we argue that conventions have normative consequences because they always involve a form of trust and reliance. We contend that it is by reference to a moral principle impinging on these aspects (i.e. the principle of Reliability) that interpersonal obligations and rights originate from conventional regularities. Second, we argue that the system of mutual expectations presupposed by conventions is a source of agreements. Agreements stemming (...) from conventions are “tacit” in the sense that they are implicated by what agents do (or forbear from doing) and without that any communication between them is necessary. To justify this conclusion, we assume that: (1) there is a salient interpretation, in some contexts, of everyone’s silence as confirmatory of the others’ expectations (an epistemic assumption), and (2) the participating agents share a value of not being motivated by hostile attitudes (a motivational assumption). By clarifying the relation between conventions and agreements, the peculiar normativity of conventions is analyzed. (shrink)

Thesis: Macro-level social phenomena are implemented through the (social) actions and minds of the individuals. Without an explicit theory of the agents' minds that founds, agents' behavior we cannot understand macro-level social phenomena, and in particular how they work. AntiThesis: Mind is not enough: the theory of individual (social) mind and action is not enough to explain several macro-level social phenomena. First, there are pre-cognitive, objective social structures that constrain the actions of the agents; second, there are emergent, unaware or (...) non-contractual forms of cooperation, organisation, and intelligence. Synthesis: The real challenge is how to reconcile, cognition with emergence, intention and deliberation with unknown or unplanned social functions and social order . Both objective structures and unplanned self-organising complex forms of social order and social function emerge from the interactions of agents and from their individual mental states; both these structures and self-organising systems feedback on agents' behaviors through the agents' individual minds. (shrink)

True to its sensorimotor inspiration, Hurley's shared circuits model (SCM) describes goal-states only within a homeostatic mechanism for action control, neglecting to consider other functions of goals control freaks.”.

Imitation, deliberation, and mindreading are characteristically human sociocognitive skills. Research on imitation and its role in social cognition is flourishing across various disciplines; it is here surveyed under headings of behavior, subpersonal mechanisms, and functions of imitation. A model is then advanced within which many of the developments surveyed can be located and explained. The shared circuits model explains how imitation, deliberation, and mindreading can be enabled by subpersonal mechanisms of control, mirroring and simulation. It is cast at a middle, (...) functional level of description, between the level of neural implementation and the level of conscious perceptions and intentional actions. The shared circuits model connects shared informational dynamics for perception and action with shared informational dynamics for self and other, while also showing how the action/perception, self/other and actual/possible distinctions can be overlaid on these shared informational dynamics. It avoids the common conception of perception and action as separate and peripheral to central cognition. Rather, it contributes to the situated cognition movement by showing how mechanisms for perceiving action can be built on those for active perception. (shrink)