@PhdThesis{gerding04a,
  author =	 {Enrico Harm Gerding},
  title =	 {Autonomous Agents in Bargaining Games: An
                  Evolutionary Investigation of Fundamentals,
                  Strategies, and Business Applicationsg},
  school =	 {Instituut voor Program-matuurkunde en Algoritmieka},
  year =	 2004,
  abstract =	 {In this thesis we consider the setting where agents
                  are adaptive to their environment, and learn
                  effective bargaining policies by trial and error. We
                  apply learning techniques from the field of
                  artificial intelligence, specifically evolutionary
                  algorithms, to model the adaptive nature of
                  bargaining agents in practical settings. In the
                  first part of the thesis, we consider fundamental
                  aspects of bilateral bargaining between a buyer and
                  a seller. We first validate the evolutionary model
                  for bilateral bargaining by comparing the outcomes
                  with game-theoretic results of relatively simple
                  bargaining settings. We then investigate several
                  extensions of game-theoretical bargaining games,
                  which are more complex and closer to real-world
                  settings than traditional models. Such settings are
                  difficult to analyse game-theoretically, but can be
                  approached using computational techniques.},
  keywords = 	 {multiagent negotiation},
  url = 	 {http://jmvidal.cse.sc.edu/library/gerding04a.pdf}
}