@conference {
	title = {A Distributed Learning Algorithm with Bit-valued Communications for Multi-agent Welfare Optimization},
	author = {Menon, Anup., Baras, John S.},
	year = {2013},
	month = {12/2013},
	abstract = {A multi-agent system comprising N agents, each picking actions from a finite set and receiving a payoff that depends on the action of the whole, is considered. The exact form of the payoffs are unknown and only their values can be measured by the respective agents. A decentralized algorithm was proposed by Marden et al. and in the authors{\textquoteright} earlier work that, in this setting, leads to the agents picking welfare optimizing actions under some restrictive assumptions on the payoff structure. This algorithm is modified in this paper to incorporate exchange of certain bit-valued information between the agents over a directed communication graph. The notion of an interaction graph is then introduced to encode known interaction in the system. Restrictions on the payoff structure are eliminated and conditions that guarantee convergence to welfare minimizing actions w.p. 1 are derived under the assumption that the union of the interaction graph and communication graph is strongly connected.},
	address = {Florence, Italy},
	booktitle = {Proceedings of the 52nd IEEE Conference on Decision and Control,},
	pages = {pp. 2406-2411}
}
@conference {
	title = {Convergence Guarantees for a Decentralized Algorithm Achieving Pareto Optimality},
	author = {Menon, Anup., Baras, John S.},
	year = {2013},
	month = {06/2013},
	abstract = {We consider N agents, each picking actions froma finite set and receiving a payoff according to its individualutility function that may depend on the actions picked byothers. An agent has no knowledge about the functional formof its utility and can only measure its instantaneous value.It is assumed that all agents pick actions and receive payoffssynchronously. For this setting, a fully decentralized iterativealgorithm for achieving Pareto optimality i.e. picking actionsthat maximize the sum of all utilities was proposed by Mardenet. al. in [1] that lacks convergence guarantees. By schedulinga certain noise parameter to go to zero along iterations of thisalgorithm, conditions that guarantee convergence in probabilityare derived in this paper.},
	address = {Washington DC},
	booktitle = {2013 American Control Conference},
	pages = {pp. 1935-1940}
}
