Quantifying Relative Autonomy in Multiagent Interaction

Braynov, Sviatoslav; Hexmoor, Henry

doi:10.1007/978-1-4419-9198-0_4

Cited by 20 publications

(17 citation statements)

References 9 publications

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“…Such types of systems are often called autonomous agents [35]. The definition of agents' autonomy is too general and makes it possible for these types of systems to have different types and levels of autonomy [9,17,19,42]. In our case we are interested in userautonomy which is to say that a system is autonomous if it can decide which actions to perform without the user's intervention [19].…”

Section: Level Of Autonomymentioning

confidence: 98%

A framework to analyze information systems as knowledge flow facilitators

Rodríguez-Elias

Martínez-García

Vizcaíno

et al. 2008

Information and Software Technology

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Section: Level Of Autonomymentioning

confidence: 98%

A framework to analyze information systems as knowledge flow facilitators

Rodríguez-Elias

Martínez-García

Vizcaíno

et al. 2008

Information and Software Technology

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“…Researchers around the world have carried much research into the evaluation of unmanned systems, each having their own clear application objectives [6][7][8][9][10][11][12][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55]. The level of research interest in China on this topic has slowly risen, with existing research mainly having focused on introducing, analyzing, and tracking foreign research; Chinese research is thus still in its infancy [21][22][23][24][25]27,28,30,32,33,37,38].…”

Section: Survey Of Current Evaluation Methodsmentioning

confidence: 99%

“…Some common technologies and expertise technologies of unmanned autonomous systems have attracted researchers' wide attention, such as sensing technology [54,56], human-robot interaction technology [45], information communication technology [20], path planning technology [32,57,58], multi-platform collaboration technology [15,35,36,50,59,60], movement and motion-control technology [17,61,62], autonomy energy technology [39], mission planning technology [13,16,63], and autonomous flight-control technology [31,34,64,65]. Ref.…”

Section: Key Technologies Of the Cobweb Evaluation Modelmentioning

confidence: 99%

Evaluation methods for the autonomy of unmanned systems

Wang

Liu

2012

Chin. Sci. Bull.

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Future military and civilian unmanned systems will be extensively used on land, under the sea, in air, and in space. Evaluation of their autonomy is critical to realize the autonomous operation ability of unmanned systems. This paper discussed the basic concepts of autonomy and the significance of autonomy evaluation, and then surveys and describes existing evaluating methods. Based on analyses of key technologies and these technologies' technical readiness levels, a cobweb evaluation model as a universal evaluation model has been used to evaluate unmanned systems. If the technology warps (aspects of autonomy) and wefts (levels of those aspects) of unmanned systems can be determined for the cobweb model, the model can handle the mutual coupling, high dimensions, and diversity of warp lines. This model potentially has wide application in evaluating various unmanned systems.

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“…In turn, the ability to generate goals and use them as instigators of action captures our view of autonomy [12], which has been defined in computational systems as the ability to "operate without the direct intervention of others, and have some kind of control over one's actions and internal state" [21]. While some have argued for adjustable autonomy that can be increased or decreased depending on circumstances [1], or for different dimensions of autonomy [3], we see goal generation as absolute and defined in terms of motivation, or the ability to generate goals as result of internal drives.…”

Section: Intentions In Provenancementioning

confidence: 99%

Modelling the provenance of data in autonomous systems

Miles

Munroe

Lück

et al. 2007

Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems

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Determining the provenance of data, i.e. the process that led to that data, is vital in many disciplines. For example, in science, the process that produced a given result must be demonstrably rigorous for the result to be deemed reliable. A provenance system supports applications in recording adequate documentation about process executions to answer queries regarding provenance, and provides functionality to perform those queries. Several provenance systems are being developed, but all focus on systems in which the components are reactive, for example Web Services that act on the basis of a request, job submission system, etc. This limitation means that questions regarding the motives of autonomous actors, or agents, in such systems remain unanswerable in the general case. Such questions include: who was ultimately responsible for a given effect, what was their reason for initiating the process and does the effect of a process match what was intended to occur by those initiating the process? In this paper, we address this limitation by integrating two solutions: a generic, re-usable framework for representing the provenance of data in service-oriented architectures and a model for describing the goal-oriented delegation and engagement of agents in multi-agent systems. Using these solutions, we present algorithms to answer common questions regarding responsibility and success of a process and evaluate the approach with a simulated healthcare example.

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Quantifying Relative Autonomy in Multiagent Interaction

Cited by 20 publications

References 9 publications

A framework to analyze information systems as knowledge flow facilitators

A framework to analyze information systems as knowledge flow facilitators

Evaluation methods for the autonomy of unmanned systems

Modelling the provenance of data in autonomous systems

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