@Inproceedings{Rezaei2010distributednetwork,
	Author = {Rezaei, G And Pfau, J And Kirley, M},
	Booktitle = {Proceedings - 2010 IEEE/WIC/ACM International Conference On Intelligent Agent Technology, IAT 2010},
	Month = {Dec},
	Pages = {24--31},
	Title = {Distributed Advice-Seeking On An Evolving Social Network},
	Volume = {2},
	Year = {2010},
	Abstract = {Within Service-Oriented Architectures, Components Need To Discover And Interact With Suitable Services, Which In Turn Can Be Mapped To Particular Subtasks. Similarly, In Online Social Communities Users May Wish To Discover Information Related To A Particular Interest That Matches Their Personal Preferences. These Tasks Are Difficult When The Number Of Available Resources To Select From Is Large, And When The Actual Characteristics Of These Resources Do Not Become Available Until Accessed, If They Are Made Explicit At All. Given These Requirements, We Suggest That The Direct Exchange Of "Selection Advice" Between The Users Or Components Of The System Can Be Beneficial. However, Because Individual Requirements Or Preferences May Be Different, The Choice From Whom To Accept Advice Is Crucial. We Capture This Problem In An Abstract Agent-Based Model With A Pool Of Heterogeneous Resources And A Population Of Agents With Varying, But Occasionally Overlapping, Preferences. Based On Local Information Only, Agents Autonomously Form Connections With Other Agents Who Provide Advice Thereby Improving Resource Selection. We Study How This Capability Affects The Match Between Agents' Preferences And The Resources They Access And How The Underlying Connection Network Co-Evolves With Advice Exchange. Our Results Indicate That This Framework Promotes The Formation Of Communities With Agents Of Similar Preferences And Hence Improves The Overall Agent Utility, Which Is Based On Resource Quality. This Outcome Is Significant When The Agents' Personal Knowledge About The Resource Pool Is Small. These Results Highlight How The Cooperation Of Components Or Users In Concrete Systems Can Be Facilitated In Order To Improve System Performance Or User Experience. © 2010 IEEE.},
	Doi = {10.1109/WI-IAT.2010.78},
	Isbn = {9780769541914},
	Day = {13},
	Publicationstatus = {Published},
}

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These Tasks Are Difficult When The Number Of Available Resources To Select From Is Large, And When The Actual Characteristics Of These Resources Do Not Become Available Until Accessed, If They Are Made Explicit At All. Given These Requirements, We Suggest That The Direct Exchange Of \"Selection Advice\" Between The Users Or Components Of The System Can Be Beneficial. However, Because Individual Requirements Or Preferences May Be Different, The Choice From Whom To Accept Advice Is Crucial. We Capture This Problem In An Abstract Agent-Based Model With A Pool Of Heterogeneous Resources And A Population Of Agents With Varying, But Occasionally Overlapping, Preferences. Based On Local Information Only, Agents Autonomously Form Connections With Other Agents Who Provide Advice Thereby Improving Resource Selection. We Study How This Capability Affects The Match Between Agents' Preferences And The Resources They Access And How The Underlying Connection Network Co-Evolves With Advice Exchange. Our Results Indicate That This Framework Promotes The Formation Of Communities With Agents Of Similar Preferences And Hence Improves The Overall Agent Utility, Which Is Based On Resource Quality. This Outcome Is Significant When The Agents' Personal Knowledge About The Resource Pool Is Small. These Results Highlight How The Cooperation Of Components Or Users In Concrete Systems Can Be Facilitated In Order To Improve System Performance Or User Experience. © 2010 IEEE.","doi":"10.1109/WI-IAT.2010.78","isbn":"9780769541914","day":"13","publicationstatus":"Published","bibtex":"@Inproceedings{Rezaei2010distributednetwork,\r\n\tAuthor = {Rezaei, G And Pfau, J And Kirley, M},\r\n\tBooktitle = {Proceedings - 2010 IEEE/WIC/ACM International Conference On Intelligent Agent Technology, IAT 2010},\r\n\tMonth = {Dec},\r\n\tPages = {24--31},\r\n\tTitle = {Distributed Advice-Seeking On An Evolving Social Network},\r\n\tVolume = {2},\r\n\tYear = {2010},\r\n\tAbstract = {Within Service-Oriented Architectures, Components Need To Discover And Interact With Suitable Services, Which In Turn Can Be Mapped To Particular Subtasks. Similarly, In Online Social Communities Users May Wish To Discover Information Related To A Particular Interest That Matches Their Personal Preferences. These Tasks Are Difficult When The Number Of Available Resources To Select From Is Large, And When The Actual Characteristics Of These Resources Do Not Become Available Until Accessed, If They Are Made Explicit At All. Given These Requirements, We Suggest That The Direct Exchange Of \"Selection Advice\" Between The Users Or Components Of The System Can Be Beneficial. However, Because Individual Requirements Or Preferences May Be Different, The Choice From Whom To Accept Advice Is Crucial. We Capture This Problem In An Abstract Agent-Based Model With A Pool Of Heterogeneous Resources And A Population Of Agents With Varying, But Occasionally Overlapping, Preferences. Based On Local Information Only, Agents Autonomously Form Connections With Other Agents Who Provide Advice Thereby Improving Resource Selection. We Study How This Capability Affects The Match Between Agents' Preferences And The Resources They Access And How The Underlying Connection Network Co-Evolves With Advice Exchange. 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