Title :
Agent Learning to Manage Costs for Event Detection
Author :
Aggour, Kareem S. ; Interrante, John ; LaComb, Christina
Author_Institution :
GE Global Res., Niskayuna, NY
Abstract :
Recent scandals around manipulated financial filings have caused investors and analysts to search for alternative ways to study the financial health of companies. The use of news events such as CEO or auditor changes has proven valuable at providing insights into the status of a company´s financial health. However, this information can be extremely difficult and expensive to gather in practice. An intelligent multi-agent system was designed and developed to simulate the collection of news events in an efficient, cost-effective manner. Results show that a multi-agent system is an effective tool for collecting critical business intelligence while minimizing cost
Keywords :
competitive intelligence; financial data processing; learning (artificial intelligence); multi-agent systems; agent learning; business intelligence; company financial health; cost minimization; deliberative learning; event detection; financial filing; intelligent agent; intelligent multiagent system; knowledge representation; resource constraint management; Companies; Costs; Data mining; Event detection; Financial management; Information analysis; Intelligent agent; Intelligent systems; Multiagent systems; Telephony; Deliberative learning; event detection; intelligent agents; knowledge representation; multi-agent systems; resource constraint management;
Conference_Titel :
Intelligent Systems, 2006 3rd International IEEE Conference on
Conference_Location :
London
Print_ISBN :
1-4244-01996-8
Electronic_ISBN :
1-4244-01996-8
DOI :
10.1109/IS.2006.348453
