@PhdThesis{goradia07phd,
  author =	 {Hrishikesh J. Goradia},
  title =	 {Automated Negotiation Among Autonomous Agents in
                  Negotiation Networks},
  school =	 {University of South Carolina},
  year =	 2007,
  abstract =	 {Distributed software systems are a norm in today’s
                  computing environment. These systems typically
                  comprise of many autonomous components that interact
                  with each other and negotiate to accomplish joint
                  tasks. Today, we can integrate potentially disparate
                  components such that they act coherently by
                  coordinating their actions via message
                  exchanges. Once this integration issue is resolved,
                  the next big challenge in computing is the
                  automation of the negotiation process between the
                  various system components. In this dissertation, we
                  address this automated negotiation problem in
                  environments where there is a conflict of interest
                  among the system components. We present our
                  negotiation model - a negotiation network - where a
                  software system is a network of agents representing
                  individual components in the system. We analyze the
                  software system as a characteristic form game, one
                  of many concepts in this dissertation borrowed from
                  game theory. The agents in our model preserve the
                  selfinterest of the components they represent (their
                  owners), and make decisions that maximize the
                  expected utilities of their owners. These agents
                  accomplish joint tasks by forming coalitions. We
                  show that the problem of computing the optimal
                  solution, where the utilities of all agents are
                  maximized, is hyper-exponential in complexity. We
                  present an approximate algorithm for this hard
                  problem, and evaluate it empirically. The simulation
                  results show that our algorithm has many desirable
                  properties - it is distributed, efficient, stable,
                  scalable, and simple. Our algorithm produces the
                  optimal (social welfare maximizing) solution for
                  96\% of cases, generates maximal global revenue for
                  97\% of cases, converges to 90\% of the best found
                  allocation after only 10 rounds of negotiation, and
                  finds a core-stable solution for revenue
                  distribution among the agents for cases with
                  nonempty core. Finally, to ensure stability for all
                  cases, we present a sliding-window algorithm that
                  computes the nucleolus-stable solution under all
                  situations.},
  url = 	 {http://jmvidal.cse.sc.edu/papers/goradia07c.pdf},
  keywords = 	 {multiagent negotiation}
}