Semantic and Virtual Agents Model in Adaptive Middleware Architecture for Smart Vehicle Space

2005 ◽  
pp. 543-546 ◽  
Author(s):  
Qing Wu ◽  
Zhaohui Wu
Keyword(s):  
Virtual Agents ◽  
Adaptive Middleware ◽  
Smart Vehicle

ScudWare: A Semantic and Adaptive Middleware Platform for Smart Vehicle Space

2007 ◽  
Vol 8 (1) ◽  
pp. 121-132 ◽  
Author(s):  
Zhaohui Wu ◽  
Qing Wu ◽  
Hong Cheng ◽  
Gang Pan ◽  
Minde Zhao ◽  
...  
Keyword(s):  
Adaptive Middleware ◽  
Smart Vehicle

Agency and rationality: Adopting the intentional stance toward evolved virtual agents.

Decision ◽  
2016 ◽  
Vol 3 (1) ◽  
pp. 40-53 ◽  
Author(s):  
Peter C. Pantelis ◽  
Timothy Gerstner ◽  
Kevin Sanik ◽  
Ari Weinstein ◽  
Steven A. Cholewiak ◽  
...  
Keyword(s):  
Virtual Agents ◽  
Intentional Stance

scholarly journals Adaptive middleware in go - a software architecture-based approach

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Nelson Rosa ◽  
David Cavalcanti ◽  
Gláucia Campos ◽  
André Silva
Keyword(s):  
Software Architecture ◽  
Adaptive Middleware

scholarly journals The Matter of Chance: Auditing Web Search Results Related to the 2020 U.S. Presidential Primary Elections Across Six Search Engines

2021 ◽  
pp. 089443932110068
Author(s):  
Aleksandra Urman ◽  
Mykola Makhortykh ◽  
Roberto Ulloa
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Large Scale ◽  
Web Search ◽  
Primary Elections ◽  
Virtual Agents ◽  
Search Results ◽  
Presidential Primary ◽  
Large Scale Analysis ◽  
Algorithmic Information

We examine how six search engines filter and rank information in relation to the queries on the U.S. 2020 presidential primary elections under the default—that is nonpersonalized—conditions. For that, we utilize an algorithmic auditing methodology that uses virtual agents to conduct large-scale analysis of algorithmic information curation in a controlled environment. Specifically, we look at the text search results for “us elections,” “donald trump,” “joe biden,” “bernie sanders” queries on Google, Baidu, Bing, DuckDuckGo, Yahoo, and Yandex, during the 2020 primaries. Our findings indicate substantial differences in the search results between search engines and multiple discrepancies within the results generated for different agents using the same search engine. It highlights that whether users see certain information is decided by chance due to the inherent randomization of search results. We also find that some search engines prioritize different categories of information sources with respect to specific candidates. These observations demonstrate that algorithmic curation of political information can create information inequalities between the search engine users even under nonpersonalized conditions. Such inequalities are particularly troubling considering that search results are highly trusted by the public and can shift the opinions of undecided voters as demonstrated by previous research.

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