Conflicts within and for Collaboration

Castelfranchi, Cristiano; Falcone, Rino

doi:10.1007/0-306-46985-5_2

Cited by 4 publications

(14 citation statements)

References 11 publications

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“…This can be exemplified via some kind of BDI agent, but in fact it is more general (it does not only apply to a specific kind of architecture). We present our framework in a cognitive perspective because we want to cover the higher levels of autonomy 17 , and also the interaction between a human user and a robot or a software agent, or between humans. However, the basic ontology and claims of the model could be also applied to noncognitive, merely rule-based agents.…”

Section: Discussionmentioning

confidence: 99%

“…We will first examine the problem of adjustable autonomy in a broad sense, i.e., as adjusting the level and the kind of delegation/adoption (in our terminology delegation conflicts [17]). We claim that this is the right theoretical frame for understanding also the adjustment of autonomy in a strict sense, since any autonomy adjustment requires a delegation adjustment but not vice versa (see Section III-B).…”

Section: Adjustment Of Delegation/adoptionmentioning

confidence: 99%

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The human in the loop of a delegated agent: the theory of adjustable social autonomy

Falcone

Castelfranchi

2001

IEEE Trans. Syst., Man, Cybern. A

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In this paper, we refer to social autonomy in a collaborative relationship among agents based on delegation and help. We address the problem of adjustable autonomy, i.e., we discuss when modifying the assigned/received delegation entails a corresponding enlargement or restriction of autonomy and at which level. We stress in particular the role played in autonomy by: 1) the degree of "openness" of delegation (execution autonomy), 2) the allowed initiative in (re)starting negotiation (meta-autonomy), 3) the degree and kind of control (feedback+intervention), and 4) the strength of delegation with respect to interaction. We show how the adjustability of delegation and autonomy is actually "bilateral," because not only the user (delegator, trustor, client) can adjust the autonomy of the agent (delegee, trustee, contractor), but the agent can also have (cooperative) reasons for and the ability to change the received delegation and modify its own autonomy in it. Adjustment is also "bidirectional" (from more autonomy to less autonomy, or vice versa), 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 ; 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 neither directly prescriptive nor simply descriptive. It is aimed at providing a theoretical framework, i.e., the conceptual instruments necessary for analyzing and understanding interaction with autonomous entities.

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Section: Discussionmentioning

confidence: 99%

Section: Adjustment Of Delegation/adoptionmentioning

confidence: 99%

The human in the loop of a delegated agent: the theory of adjustable social autonomy

Falcone

Castelfranchi

2001

IEEE Trans. Syst., Man, Cybern. A

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“…Production context Considering the production perspective, SOHO defines concepts and properties that characterize production procedures in terms of objectives and operations necessary to successfully achieve them. A proper representation of this knowledge is crucial to establish human and robot commitment to production goals [1,18] and a level of agreement about the way the human and the robot together achieve these goals [25,79]. Furthermore, it is necessary to characterize events that may occur in a production environment and that are relevant with respect to the execution of production procedures.…”

Section: Environment Contextmentioning

confidence: 99%

“…∃!DUL:describes.HRCTask ∃!constrains.HumanFunction ∃!constrains.RobotFunction ∃!DUL:isDescribedBy.PrecedenceConstraint (18) An interaction modality of type Supportive requires a human and a robot to perform the same operation on the same object at the same time. The pattern in this case forces the execution of a RobotFunction and a HumanFunction to start and end at the same time.…”

Section: Sequential Interactionmodalitymentioning

confidence: 99%

“…This set of synchronization rules constrains possible task allocations and possible interactions between the worker and the robot. It is at this level that ontological patterns about known collaboration modalities (i.e., Equation (16), Equation (17), Equation (18) and Equation ( 19)) are used to define temporal constraints determining specific humanrobot collaborative behaviors.…”

Section: Knowledge Extraction Proceduresmentioning

confidence: 99%

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Enhancing awareness of industrial robots in collaborative manufacturing

Umbrico

Cesta

Orlandini

2024

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The diffusion of Human-Robot Collaborative cells is prevented by several barriers. Classical control approaches seem not yet fully suitable for facing the variability conveyed by the presence of human operators beside robots. The capabilities of representing heterogeneous knowledge representation and performing abstract reasoning are crucial to enhance the flexibility of control solutions. To this aim, the ontology SOHO (Sharework Ontology for Human-Robot Collaboration) has been specifically designed for representing Human-Robot Collaboration scenarios, following a context-based approach. This work brings several contributions. This paper proposes an extension of SOHO to better characterize behavioral constraints of collaborative tasks. Furthermore, this work shows a knowledge extraction procedure designed to automatize the synthesis of Artificial Intelligence plan-based controllers for realizing flexible coordination of human and robot behaviors in collaborative tasks. The generality of the ontological model and the developed representation capabilities as well as the validity of the synthesized planning domains are evaluated on a number of realistic industrial scenarios where collaborative robots are actually deployed.

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