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Title:Expectation-Oriented Modeling
Authors: Matthias Nickles ; Michael Rovatsos ; Gerhard Weiss
Date:Dec 2005
Publication Title:Engineering Applications of Artificial Intelligence
Publication Type:Journal Article Publication Status:Published
Volume No:18(8) Page Nos:891-918
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 influencing 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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Bibtex format
author = { Matthias Nickles and Michael Rovatsos and Gerhard Weiss },
title = {Expectation-Oriented Modeling},
journal = {Engineering Applications of Artificial Intelligence},
publisher = {Elsevier},
year = 2005,
month = {Dec},
volume = {18(8)},
pages = {891-918},
doi = {doi:10.1016/j.engappai.2005.05.002},
url = {},

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