Multiagent Systems Without Agents — Mirror-Holons for the Compilation and Enactment of Communication Structures

Nickles, Matthias; Weiß, Gerhard

doi:10.1007/11594116_14

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…In fact, they appear as black boxes and, therefore, must be observed by the other agents or the system designer in order to determine their characteristics during runtime of the system. Based on such observations, a structuring approach for MAS has been proposed, using explicit modeling of expectations concerning communication flows (Brauer et al, 2002;Nickles and Weiß, 2005). This approach which is inspired by the sociological theory of communication systems (Luhmann, 1995) establishes a notion of communicative agent behavior that is reflected by the modeled expectations.…”

Section: Agent Coordinationmentioning

confidence: 99%

SELF-ORGANIZING SUPPLY NETWORKS - Autonomous Agent Coordination based on Expectations

2011

Proceedings of the 3rd International Conference on Agents and Artificial Intelligence

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Supply networks are faced with the contradictory requirements of achieving high operational efficiency while retaining the ability to adapt to a changing environment. Decentralized approaches representing logistics entities by autonomous artificial agents must therefore be enabled to structure and operate supply networks efficiently according to the domain's inherent dynamics caused, for instance, by changing customer demands and network participants entering or leaving the system. In this paper, a novel approach to self-organization for multiagent systems is presented, avoiding a priori assumptions of agent characteristics by generating expectations from observable behavior.

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Section: Agent Coordinationmentioning

confidence: 99%

SELF-ORGANIZING SUPPLY NETWORKS - Autonomous Agent Coordination based on Expectations

2011

Proceedings of the 3rd International Conference on Agents and Artificial Intelligence

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“…• means for the learning and revision of expectations from selectively-overheard agent communications, and for the enactment and communication of expectations facing other agents (in form of so-called Social Mirrors [20,26] and Expectation Engines).…”

Section: Introductionmentioning

confidence: 99%

“…EOM adopts the concept of agent expectation from [20,23] and is also strongly inuenced by the EXPAND methodology (Expectation-Oriented Analysis and Design) [5] and the concept of Mirror-Holons [26] 23 . EOM also possesses a strong sociological background; more specically, its underlying view of sociality is quite close to Luhmann's Social Systems Theory [22], as it has been adapted to articial agency.…”

Section: Introductionmentioning

confidence: 99%

Expectation-oriented modeling

Nickles

Rovatsos

Weiß

2005

Engineering Applications of Artificial Intelligence

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This work introduces Expectation-oriented Modeling (EOM) as a conceptual and formal framework for the modeling and inuencing of black-or gray-box agents and agent interaction from the viewpoint of modelers like articial agents and application designers. EOM is unique in that autonomous agent behavior is not restricted in advance, but only if it turns out to be necessary at runtime, and does so exploiting a seamless combination of evolving probabilistic and normative behavioral expectations as the key modeling abstraction and as the primary level of analysis and inuence. Expectations are attitudes which allow for the relation of observed and predicted actions and other events to the modeler's intentions and desires on the one hand and her beliefs on the other in an integrated, adaptive manner. In this regard, this work introduces a formal framework for the representation and the semantics of expectations embedded in social contexts. We see the applicability of EOM especially in open domains with a priori unknown and possibly unreliable and insincere actors, where the modeler can not rely on cooperation or pursue her goals through the exertion of strictly normative power, e.g. the development and assertion of exible interaction policies for trading platforms in the Internet, as illustrated in a case study. To our knowledge, EOM is the rst approach to the specication, prediction, analysis and inuencing of social interaction that aims at tackling the level of expectations explicitly and systematically, and allow for representing the beliefs and the intentions of agents in terms of empirical and desired predictions.

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“…While work on systemtheoretic MAS architectures [14] can comment on arbitrary aspects of scalability, be it at the micro-, meso-or macro-level of social systems, multiagent research that seeks to exploit the principles of interactionism [15] has to focus on more specific aspects. Interactionist theories are, generally speaking, concerned with how social structures affect interaction between humans in a society, how individuals process social knowledge and how social sense is continually re-produced and potentially re-constructed through ongoing interaction.…”

Section: Introductionmentioning

confidence: 99%

On the Organisation of Agent Experience: Scaling Up Social Cognition

Rovatsos

Paetow

2005

Socionics

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Abstract. This paper introduces "micro-scalability" as a novel design objective for social reasoning architectures operating in open multiagent systems. Microscalability is based on the idea that social reasoning algorithms should be devised in a way that allows for social complexity reduction, and that this can be achieved by operationalising principles of interactionist sociology. We first present a formal model of InFFrA agents called m 2 InFFrA that utilises two cornerstones of micro-scalability, the principles of social abstraction and transient social optimality. Then, we exemplify the usefulness of these concepts by presenting experimental results with a novel opponent classification heuristic ADHOC that has been developed using the InFFrA social reasoning architecture. These results prove that micro-scalability deserves further investigation as a useful aspect of socionic research.

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Multiagent Systems Without Agents — Mirror-Holons for the Compilation and Enactment of Communication Structures

Cited by 8 publications

References 9 publications

SELF-ORGANIZING SUPPLY NETWORKS - Autonomous Agent Coordination based on Expectations

SELF-ORGANIZING SUPPLY NETWORKS - Autonomous Agent Coordination based on Expectations

Expectation-oriented modeling

On the Organisation of Agent Experience: Scaling Up Social Cognition

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