Paper Info
Title
Q-Strategy: Automated Bidding and Convergence in Computational Markets
Abstract
Agents and market mechanisms are widely elaborated and applied to automate interaction and decision pro- cesses among others in robotics, for decentralized con- trol in sensor networks and by algorithmic traders in fi- nancial markets. Currently there is a high demand of ef- ficient mechanisms for the provisioning, usage and allo- cation of distributed services in the Cloud. Such mech- anisms and processes are not manually manageable and require decisions made in quasi real-time. Thus agent decisions should automatically adapt to changing con- ditions and converge to optimal values. This paper presents a bidding strategy, which is capa- ble of automating the bid generation and utility maxi- mization processes of consumers and providers by the interaction with markets as well as to converge to opti- mal values. The bidding strategy is applied to the con- sumer side against benchmark bidding strategies and its behavior and convergence are evaluated in two market mechanisms, a centralized and a decentralized one.
Year
Venue
Keywords
2009
IAAI
sensor network,real time
Field
DocType
Citations 
Decentralised system,Computer science,Operations research,Provisioning,Real-time bidding,Artificial intelligence,Financial market,Bidding,Wireless sensor network,Ebidding,Machine learning,Cloud computing
Conference
1
PageRank 
References 
Authors
0.35
14
1
Name
Order
Citations
PageRank
Nikolay Borissov119911.25