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

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

AmI Systems as Agent-Based Mirror Worlds: Bridging Humans and Agents through Stigmergy

In this chapter we introduce a vision of agent-oriented AmI systems that is extended to integrate ideas inspired by MirrorWorlds as introduced by Gelernter at the beginning of the eighties. In this view, AmI systems are actually a digital world mirroring but also augmenting the physical world with capabilities, services and functionalities.We then discuss the value of stigmergy as background reference conceptual framework to define and understand interactions occurring between the physical environments and its digital agent-based extension. The digital world augments the physical world so that traces left by humans acting in the physical world are represented in the digital one in order to be perceived by software agents living there and, viceversa, actions taken by software agents in the mirror can have an effect on the connected physical counterpart

Embodied Organizations: a Unifying Perspective in Programming Agents, Organizations and Environments

http://ceur-ws.org/Vol-627/coin_7.pdfInternational audienceMAS research pushes the notion of openness related to systems combining heterogeneous computational entities. Typically, those entities answer to different purposes and functions and their integration is a crucial issue. Starting from a comprehensive approach in developing agents, organizations and environments, this paper devises an integrated approach and describes a unifying programming model. It introduces the notion of embodied organization, which is described first focusing on the main entities as separate concerns; and, second, establishing different interaction styles aimed to seamlessly integrate the various entities in a coherent system. An integration framework, built on top of Jason, CArtAgO and Moise (as programming platforms for agents, environments and organizations resp.) is described as a suitable technology to build embodied organizations in practice

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

From Agents to Artifacts Back and Forth: Purposive and Doxastic use of Artifacts in MAS

Recent approaches in Multi-Agent Systems are focusing on providing models and methodologies for the design of environments and special purpose tools supposed to scale up complexities. Among others, the Agents and Artifacts (A&A) approach introduced the notion af artifact as first class abstraction providing agents with external facilities, services and coordination medium explicitely conceived for easing their activities. In this paper we analyse A&A systems by focusing on the functional roles played by artifacts. In particular, we here investigate the function of artifacts once they are employed in the context of societies of cognitve agents, i.e. agents capable to reason about their epistemic and motivational states. In this context, a twofold kind of interactions is envisaged. On the one side, artifact rapresentational function allows agent to improve epistemic states, i.e., by representing and sharing strategic knowledge in the overall system (doxastic use ). On the other side, artifacts operational function allows agents to improve the repertoire of actions, i.e., by providing additional means which can be purposively triggered by agents to achieve goals (operational use ). Some of the outcomes of this approach are discussed along with test cases showing agents engaged in goal-oriented activities relying on the transmission of relevant knowledge and the operations provided by artifacts