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 <channel rdf:about="http://ebiquity.umbc.edu/tag/auction/">
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    <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/14/TAGA"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/190/Multi-agent-simulation-of-financial-markets"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/87/Using-Semantic-web-technology-in-Multi-Agent-systems-a-case-study-in-the-TAGA-Trading-agent-environment"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/139/Strategies-and-heuristics-used-by-the-UMBCTAC-agent-in-the-third-Trading-Agent-Competition"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/140/UMBCTAC-A-Balanced-Bidding-Agent"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/172/TradeWise"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/136/Making-Google-Richer-Optimal-Algorithms-for-the-AdWords-Auction"/>
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 <image rdf:about="http://ebiquity.umbc.edu/img/logo.jpg">
  <title>UMBC ebiquity research group</title>
  <link>http://ebiquity.umbc.edu</link>
  <url>http://ebiquity.umbc.edu/img/logo.jpg</url>
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 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/14/TAGA">
  <title><![CDATA[TAGA]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/14/TAGA</link>
  <description><![CDATA[Travel Agent Game in Agentcities (TAGA) is an agent framework for simulating the global travel market on the Web. It extends and enhances the original TAC system [Wellman 99] to work in an Agentcities environment of FIPA compliant agents.]]></description>
  <dc:date>2002-10-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/190/Multi-agent-simulation-of-financial-markets">
  <title><![CDATA[Multi-agent simulation of financial markets]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/190/Multi-agent-simulation-of-financial-markets</link>
  <description><![CDATA[
 	  
]]></description>
  <dc:date>2004-12-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/87/Using-Semantic-web-technology-in-Multi-Agent-systems-a-case-study-in-the-TAGA-Trading-agent-environment">
  <title><![CDATA[Using Semantic web technology in Multi-Agent systems: a case study in the TAGA Trading agent environment]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/87/Using-Semantic-web-technology-in-Multi-Agent-systems-a-case-study-in-the-TAGA-Trading-agent-environment</link>
  <description><![CDATA[Travel Agent Game in Agentcities (TAGA) is the framework that
extends and enhances the Trading Agent Competition (TAC)
scenario to work in Agentcities, an open multi agent environment
based on FIPA compliant pla tforms. TAGA uses the semantic web
languages and tools (RDF and OWL) to specify and publish the
underlying common ontologies; as a content language within the
FIPA ACL messages; as the basis for agent knowledge bases via
XSB-based reasoning tools; to describe and reason about s...]]></description>
  <dc:date>2003-09-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/139/Strategies-and-heuristics-used-by-the-UMBCTAC-agent-in-the-third-Trading-Agent-Competition">
  <title><![CDATA[Strategies and heuristics used by the UMBCTAC agent in the third Trading Agent Competition]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/139/Strategies-and-heuristics-used-by-the-UMBCTAC-agent-in-the-third-Trading-Agent-Competition</link>
  <description><![CDATA[The UMBCTAC agent was one of the top ranked agents in the third international Trading Agent Com-petition (TAC'02). This paper describes and evalu-ates the key heuristics used by UMBCTAC, includ-ing the early bird heuristic, the balance heuristic, and the separation heuristic. We developed a simple gain-risk model to search safe and profitable allocations for hotel rooms and airline tickets. We also used a novel probabilistic approach to dynamically allocate entertainment tickets and bid in en...]]></description>
  <dc:date>2003-08-13</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/140/UMBCTAC-A-Balanced-Bidding-Agent">
  <title><![CDATA[UMBCTAC: A Balanced Bidding Agent]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/140/UMBCTAC-A-Balanced-Bidding-Agent</link>
  <description><![CDATA[UMBCTAC is one of the top ranking agents in the 3rd International Trading Agent Competition (TAC). A TAC game has multiple auctions running on different but interrelated resources simultaneously, and 8 trading agents will compete with each other for optimal result – making maximum profit.  The spirit of simplicity and balance is used as a guideline to solve the dynamical optimization problem in TAC game. The hotel/airline auctions and the entertainment ticket auctions are handled separately...]]></description>
  <dc:date>2002-12-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/172/TradeWise">
  <title><![CDATA[TradeWise]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/172/TradeWise</link>
  <description><![CDATA[Olga's Internship at GE - Pervasive Decisioning Systems Lab(PDS).

Olga's internship involved designing and developing and electronic
marketplace for owner operated small tractor trailer fleet. The system
provided comprehensive solution to personalized load discovery, valuation
of loads according to the drive's preferences and bidding for the loads.
The system also provided long term solutions that would in the
personalized manner optimize driver's portfolio.]]></description>
  <dc:date>2006-09-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/136/Making-Google-Richer-Optimal-Algorithms-for-the-AdWords-Auction">
  <title><![CDATA[Making Google Richer: Optimal Algorithms for the AdWords Auction]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/136/Making-Google-Richer-Optimal-Algorithms-for-the-AdWords-Auction</link>
  <description><![CDATA[Google's phenomenal success may be ascribed as much to a revolution in advertising as to its superior search results. The key to this online advertising model is an innovative auction run by the search engine companies. Advertisers' bids for search keywords determine which ads are displayed during internet searches. There are a number of algorithmic and game theoretic issues that arise in this context. In particular, the problem of maximizing revenue in such keyword auctions can be formulated...]]></description>
  <dc:date>2006-03-03</dc:date>
 </item>
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