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- Fantasia, Valentina, et al.
(author)
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Making sense with social robots : Extending the landscape of investigation in HRI
- 2022
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In: AIC 2022: Artificial Intelligence and Cognition 2022 : Proceedings of the 8th International Workshop on Artificial Intelligence and Cognition Örebro, Sweden, 15-17 June, 2022 - Proceedings of the 8th International Workshop on Artificial Intelligence and Cognition Örebro, Sweden, 15-17 June, 2022. - 1613-0073. ; 3400, s. 172-179
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Conference paper (peer-reviewed)abstract
- The aim of this position paper is to propose a reflection on how to account for and investigate the many ways in which interaction between robots and humans requires co-ordination, negotiation or reformulation of meanings emerging in the ongoing interaction. Towards this aim, we argue, a perspective shift may be needed: to frame interactions as social engagements whose meaning can only be understood by the standpoint of those participating in it. We first present research on psychological benchmarks and design patterns for sociality in the HRI field. Then we provide arguments for including a new interactional element currently missing in the literature: participatory sense-making processes. As we will argue, such elements can be conceived and operationalised both as a relational benchmark as well as an interactional pattern, therefore proving useful for HRI research.
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- Lieto, Antonio, et al.
(author)
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The role of cognitive architectures in general artificial intelligence
- 2018
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In: Cognitive Systems Research. - : Elsevier. - 2214-4366 .- 1389-0417. ; 48, s. 1-3
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Journal article (peer-reviewed)abstract
- The term "Cognitive Architectures" indicates both abstract models of cognition, in natural and artificial agents, and the software instantiations of such models which are then employed in the field of Artificial Intelligence (AI). The main role of Cognitive Architectures in AI is that one of enabling the realization of artificial systems able to exhibit intelligent behavior in a general setting through a detailed analogy with the constitutive and developmental functioning and mechanisms underlying human cognition. We provide a brief overview of the status quo and the potential role that Cognitive Architectures may serve in the fields of Computational Cognitive Science and Artificial Intelligence (AI) research.
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- Tomic, Stevan, 1981-
(author)
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Human Norms for Robotic Minds
- 2022
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Doctoral thesis (other academic/artistic)abstract
- Interactions within human societies are usually regulated by social norms. When humans fail to cope with norms, their behavior may be perceived as antisocial, amusing, provocative, dumb, or even uncanny, depending on the intention ascribed to the actor by the observer. Robots cannot yet follow social norms, and often their behavior is judged in such a manner. We claim that acceptance of robots into human societies will critically depend on their ability to represent, reason about, and learn social norms. How to provide these abilities is the main problem addressed in the thesis.We formalize the notion of social context as an institution that encapsulates a set of abstract social norms. Then, we connect these abstract norms to physical execution through an explicit notion of grounding, that puts together three levels of representation: (1) a formalization of normative structures in human-readable terms, (2) a mapping of these formal structures to models of execution in the physical world, and (3) a vector space representation of all these elements suitable for machine learning algorithms. Given this background, we identify computational problems central to reasoning and learning with norms, and provide solutions to several of these problems.We start by considering two reasoning problems: verification, i.e., how to verify whether or not a physical execution (or its interpretation) adheres to a set of norms; and planning, i.e., how to generate plans adherent to norms. We address these problems by reducing them to known problems with known solutions. Specifically, the verification problem is reduced to a constraint satisfaction problem (CSP), which, in turn, allows us to address planning as a meta-CSP.We then address two problems related to learning with norms: how to learn policies that generate adherent trajectories; and how to re-use learned policies across physical domains. To do so, we translate the elements of our framework into a vector-space format. We address the former problem by using norm verification mechanisms to guide a reinforcement learning agent. To address the latter problem we break it into two sub-problems: interpreting the current situation in a context, and performing the actual (reinforcement) learning. That puts us in a unique position of combining reasoning and learning by searching in the space of groundings and re-usable policies.We evaluate all our solutions with use-case experiments, both on real robots and on simulated agents. Exploring the concepts of abstraction and of interpretation in physical execution touches upon some general questions about intelligence and computation, hence we complement the technical contributions with a discussion of our work from the perspective of Cognitive Science.
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