Why agents for automated negotiations should be adaptive
We show that adaptive agents on the Internet can learn to exploit bidding agents who use a (limited) number of fixed strategies. These learning agents can be generated by adapting a special kind of finite automata with evolutionary algorithms (EAs). Our approach is especially powerful if the adaptive agent participates in frequently occurring micro-transactions, where there is sufficient opportunity for the agent to learn online from past negotiations. More in general, results presented in this paper provide a solid basis for the further development of adaptive agents for Internet applications.
|Optimization (acm G.1.6), Learning (acm I.2.6), Problem Solving, Control Methods, and Search (acm I.2.8)|
|Learning and adaptive systems (msc 68T05), Problem solving (heuristics, search strategies, etc.) (msc 68T20), 2-person games (msc 91A05), Noncooperative games (msc 91A10), Games in extensive form (msc 91A18), Multistage and repeated games (msc 91A20), Evolutionary games (msc 91A22), Rationality, learning (msc 91A26)|
|Software (theme 1), Logistics (theme 3), Energy (theme 4)|
|Software Engineering [SEN]|
|Organisation||Intelligent and autonomous systems|
van Bragt, D.D.B, & La Poutré, J.A. (2002). Why agents for automated negotiations should be adaptive. Software Engineering [SEN]. CWI.