From Manifesta to Krypta: The Relevance of Categories for Trusting Others

In this paper 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 Multi Agent Systems

Trust and Transitivity: How trust-transfer works

Transitivity in trust is very often considered as a quite simple property, trivially inferable from the classical transitivity defined in mathematics, logic, or grammar. In fact the complexity of the trust notion suggests evaluating the relationships with the transitivity in a more adequate way. In this paper, starting from a socio-cognitive model of trust, we analyze the different aspects and conceptual frameworks involved in this relation and show how different interpretations of these concepts produce different solutions and definitions of trust transitivity

Facing Openness with Socio Cognitive Trust and Categories.

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

Reasoning with Categories for Trusting Strangers: a Cognitive Architecture

A crucial issue for agents in open systems is the ability to filter out information sources in order to build an image of their counterparts, upon which a subjective evaluation of trust as a promoter of interactions can be assessed. While typical solutions discern relevant information sources by relying on previous experiences or reputational images, this work presents an alternative approach based on the cognitive ability to: (i) analyze heterogeneous information sources along different dimensions; (ii) ascribe qualities to unknown counterparts based on reasoning over abstract classes or categories; and, (iii) learn a series of emergent relationships between particular properties observable on other agents and their effective abilities to fulfill tasks. A computational architecture is presented allowing cognitive agents to dynamically assess trust based on a limited set of observable properties, namely explicitly readable signals (Manifesta) through which it is possible to infer hidden properties and capabilities (Krypta), which finally regulate agents' behavior in concrete work environments. Experimental evaluation discusses the effectiveness of trustor agents adopting different strategies to delegate tasks based on categorization

Reasoning with Categories for Trusting Strangers: a Cognitive Architecture

A crucial issue for agents in open systems is the ability to filter out information sources in order to build an image of their counterparts, upon which a subjective evaluation of trust as a promoter of interactions can be assessed. While typical solutions discern relevant information sources by relying on previous experiences or reputational images, this work presents an alternative approach based on the cognitive ability to: (i) analyze heterogeneous information sources along dierent dimensions; (ii) ascribe qualities to unknown counterparts based on reasoning over abstract classes or categories; and, (iii) learn a series of emergent relationships between particular properties observable on other agents and their effective abilities to fulfill tasks. A computational architecture is presented allowing cognitive agents to dynamically assess trust based on a limited set of observable properties, namely explicitly readable signals (Manifesta) through which it is possible to infer hidden properties and capabilities (Krypta), which finally regulate agents\u27 behavior in concrete work environments. Experimental evaluation discusses the effectiveness of trustor agents adopting different strategies to delegate tasks based on categorization

Facing Openness with Socio Cognitive Trust and Categories

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

Multimodal Trust Formation with Uninformed Cognitive Maps (UnCM)

This work describes a cognitive heuristic allowing agents to assess trust and delegations merging heterogenous information sources. The model is realized through Uninformed Cognitive Maps, based on the combination of: (i) categorization abilities (ii) history of personal experiences (iii) context awareness

Guest editorial

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Trusting the messenger because of the message: feedback dynamics from information quality to source evaluation

Published Online: 28 August 2013International audienceInformation provided by a source should be assessed by an intelligent agent on the basis of several criteria: most notably, its content and the trust one has in its source. In turn, the observed quality of information should feed back on the assessment of its source, and such feedback should intelligently distribute among different features of the source--e.g., competence and sincerity. We propose a formal framework in which trust is treated as a multi-dimensional concept relativized to the sincerity of the source and its competence with respect to specific domains: both these aspects influence the assessment of the information, and also determine a feedback on the trustworthiness degree of its source. We provide a framework to describe the combined effects of competence and sincerity on the perceived quality of information. We focus on the feedback dynamics from information quality to source evaluation, highlighting the role that uncertainty reduction and social comparison play in determining the amount and the distribution of feedback

Taxonomy of Trust-Relevant Failures and Mitigation Strategies

We develop a taxonomy that categorizes HRI failure types and their impact on trust to structure the broad range of knowledge contributions. We further identify research gaps in order to support fellow researchers in the development of trustworthy robots. Studying trust repair in HRI has only recently been given more interest and we propose a taxonomy of potential trust violations and suitable repair strategies to support researchers during the development of interaction scenarios. The taxonomy distinguishes four failure types: Design, System, Expectation, and User failures and outlines potential mitigation strategies. Based on these failures, strategies for autonomous failure detection and repair are presented, employing explanation, verification and validation techniques. Finally, a research agenda for HRI is outlined, discussing identified gaps related to the relation of failures and HR-trust