Human trust in social robots is a complex attitude based on cognitive and emotional evaluations, as well as a behavior, such as task delegation. Although previous research explored the features of robots that influence the overall trust attitude, it remains unclear whether these features affect behavioral trust. Additionally, there is limited investigation into which features of robots influence cognitive and emotional attitudes, and how these attitudes impact humans’ willingness to delegate new tasks to robots. This study examines the interplay between robot competence, autonomy, and personality trait of agreeableness and their impact on trust attitudes (cognitive and affective trust) and trust behavior (task delegation), within the context of task-oriented Human–Robot Interaction. Our findings indicate that robot competence is a key determinant of trust, influencing cognitive, affective, and behavioral trust. In contrast, robot agreeableness significantly impact only affective trust without affecting cognitive trust or trust behavior. In addition, autonomy was found to moderate the relationship between competence and cognitive trust, as well as between personality and affective trust. Finally, cognitive trust was found to positively influence task delegation, whereas affective trust did not show a significant effect. This article contributes to the literature on Human–Robot Trust by providing novel evidence that enhances the acceptance and effectiveness of social robots in collaborative scenarios.

editorial Free Access Share on Introduction to the Special Section on Trust and AI Editors: Jie Zhang NTU, Singapore NTU, SingaporeView Profile , Jamal Bentahar Concordia University, Canada Concordia University, CanadaView Profile , Rino Falcone ISTC-CNR, Italy ISTC-CNR, ItalyView Profile , Timothy J. Norman University of Southampton, UK University of Southampton, UKView Profile , Murat Şensoy Blue Prism Labs, UK Blue Prism Labs, UKView Profile Authors Info & Claims ACM Transactions on Internet TechnologyVolume 19Issue 4November 2019 Article No.: 44epp 1–3https://doi.org/10.1145/3365675Online:09 November 2019Publication History 0citation344DownloadsMetricsTotal Citations0Total Downloads344Last 12 Months114Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF

Thinking about a scenario with possible risk of flooding and landslides caused by weather conditions, it results really interesting to investigate the way in which citizens take decisions on the basis of different information sources they can access. In this work we start describing a platform we realized in order to study this social phenomenon. Then we present some simulative experiments showing how a population of cognitive agents trusting in a different way their information sources can make decisions more or less suited to the several weather patterns. The complexity of decisions is based on the fact that the agents differently trust the various sources of information, which in turn may be differently trustworthy. In our simulations we analyse some interesting case studies, with particular reference to social agents that need to wait others in order to make decision.

Due to Internet and social media web, the world as we know it is deeply changing integrating two different aspects of the social interaction: the one that develop in the real world and the one that develop in web society. In this paper we focus on the importance of generalized knowledge (agents' categories) in order to understand how much it is crucial in these two worlds. The cognitive advantage of generalized knowledge can be synthesized in this claim: "It allows us to know a lot about something/somebody we do not directly know". At a social level this means that I can know a lot of things on people that I never met; it is social "prejudice" with its good side and fundamental contribution to social exchange. In this study we will analyse and present some differences between the social relationships in the two worlds and how they influence categories' reputation. On this basis, we will experimentally inquire the role played by categories' reputation with respect to the reputation and opinion on single agents: when it is better to rely on the first ones and when are more reliable the second ones. We will consider these simulations for both the two kind of world, investigating how the parameters defining the specific environment (number of agents, their interactions, transfer of reputation, and so on) determine the use of categories' reputation and trying to understand how the role played by categories will be important in the new digital worlds.

In this article, we are interested in the fact that relevance and trustworthiness of information acquired by an agent X from a source F strictly depends and derives from X's trust in F with respect to the kind of information. In particular, we are interested in analyzing the relevance of F's category as indicator for its trustworthiness with respect to the specific informative goals of X. In this article, we analyze an interactive cognitive model for searching information in a world in which each agent can be considered as belonging to a specific agent's category. We also consider variability within the canonical categorical behavior and consequent influence on the trustworthiness of provided information. The introduced interactive cognitive model also allows evaluation of the trustworthiness of a source both on the basis of its category and on past direct experience with it, thus selecting the more adequate source with respect to the informative goals to achieve. We present a computational approach based on fuzzy sets and some selected simulation scenarios together with the discussion of their more interesting results.

No abstract available.

In this article we consider the special abilities needed by agents for assessing trust based on inference and reasoning. We analyze the case in which it is possible to infer trust towards unknown counterparts by reasoning on abstract classes or categories of agents shaped in a concrete application domain. We present a scenario of interacting agents providing a computational model implementing different strategies to assess trust. Assuming a medical domain, categories, including both competencies and dispositions of possible trustees, are exploited to infer trust towards possibly unknown counterparts. The proposed approach for the cognitive assessment of trust relies on agents' abilities to analyze heterogeneous information sources along different dimensions. Trust is inferred based on specific observable properties (manifesta), namely explicitly readable signals indicating internal features (krypta) regulating agents' behavior and effectiveness on specific tasks. Simulative experiments evaluate the performance of trusting agents adopting different strategies to delegate tasks to possibly unknown trustees, while experimental results show the relevance of this kind of cognitive ability in the case of open multiagent systems.

No abstract available.

Typical solutions for agents assessing trust relies on the circulation of information on the individual level, i.e. reputational images, subjective experiences, statistical analysis, etc. This work presents an alternative approach, inspired to the cognitive heuristics enabling humans to reason at a categorial level. The approach is envisaged as a crucial ability for agents in order to: (1) estimate trustworthiness of unknown trustees based on an ascribed membership to categories; (2) learn a series of emergent relations between trustees observable properties and their effective abilities to fulfill tasks in situated conditions. On such a basis, categorization is provided to recognize signs (Manifesta) through which hidden capabilities (Kripta) can be inferred. Learning is provided to refine reasoning attitudes needed to ascribe tasks to categories. A series of architectures combining categorization abilities, individual experiences and context awareness are evaluated and compared in simulated experiments.

The relationship between trust and control is quite relevant both for the very notion of trust and for modelling and implementing trust-control relations with autonomous systems. We claim that control is antagonistic of the strict form of trust: 'trust in y '; but also that it completes and complements it for arriving to a global trust. In other words, putting control and guaranties is trust-building; it produces a sufficient trust, when trust in y 's autonomous willingness and competence would not be enough. We also argue that control requires new forms of trust: trust in the control itself or in the controller, trust in y as for being monitored and controlled, trust in possible authorities, etc. Finally, we show that paradoxically control could not be antagonistic of strict trust in y , but it can even create, increase it by making y more willing or more effective. In conclusion, depending on the circumstances, control makes y more reliable or less reliable; control can either decrease or increase trust. A good theory of trust cannot be complete without a theory of control.

We refer to social autonomy in a collaborative relationship among agents based on delegation and help. We address the problem of adjustable autonomy. We stress in particular the role played in autonomy by: 1) the degree of "openness" of delegation; 2) the allowed initiative in (re)starting negotiation; 3) the degree and kind of control; and 4) the strength of delegation with respect to interaction. We show how the adjustability of delegation and autonomy is actually "bilateral". Adjustment is also "bidirectional" and multidimensional. Finally, we analyze some reasons for modifying the assigned autonomy and show how the adjustment of autonomy depends: on the delegator's side; on a "crisis of trust" and vice versa; the delegee's adjustment of its own autonomy depends on some disagreement about the trust received from the delegator, and, in particular, either a higher or lower confidence in itself or in external circumstances. Some preliminary hints about necessary protocols for adjusting the interaction with agents are provided. This work is aimed at providing a theoretical framework, i.e., the conceptual instruments necessary for analyzing and understanding interaction with autonomous entities.

In this paper we will briefly present the adjustment of autonomy as the problem of adjusting the level and the kind of delegation. The cncept of autonomy here considered is about a delegated task (or role). It is argued that by a careful analysis of the various aspects of delegation (openness, strength, etc.) it is possible to represent and model many dimensions of autonomy and consequently both the reasons and the ways to introduce a dynamic level of control.