@conference {
	title = {Collaborative Extremum Seeking for Welfare Optimization},
	author = {Menon, Anup., Baras, John S.},
	year = {2014},
	month = {12/2014},
	abstract = {This paper addresses a distributed, model-free optimization problem in the context of multi-agent systems.The set-up comprises of a fixed number of agents, each of which can pick an action and receive/measure a private utility function that can depend on the collective actions taken by all agents. The exact functional form (or model) of the agent utility functions is unknown, and an agent can only measure the numeric value of its utility. The objective of the multi agent system is to optimize the welfare function (i.e. sum of the individual utility functions). A model-free, distributed, on-line learning algorithm is developed that achieves this objective. The proposed solution requires information exchange between the agents over an undirected, connected communication graph,and is based on ideas from extremum seeking control. A result on local convergence of the proposed algorithm to an arbitrarily small neighborhood of a local minimizer of the welfare function is proved. Application of the solution to distributed control of wind turbines for maximizing wind farm-level power capture is explored via numerical simulations. Also included is a novel analysis of a dynamic average consensus algorithm that maybe of independent interest.},
	address = {Los Angeles, California},
	booktitle = {53rd IEEE Conference on Decision and Control},
	pages = {pp. 346-351}
}
@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}
}
@conference {
	title = {Expander Families as Information Patterns for Distributed Control of Vehicle Platoons},
	author = {Menon, Anup., Baras, John S.},
	year = {2012},
	month = {09/2012},
	abstract = {A state space formulation of the distributed control of a 1-D vehicle platoon is considered. The objective is to understand the effects of the underlying information exchange pattern between the vehicles on the control performance of the platoon. The symmetric control case is considered where each vehicle gives equal weight to all the information available to it in determining its control law. It is shown that expander families of graphs when used as information patterns result in stability margins decaying to zero at rate at most O(1/N); an improvement over the previously known O(1/N^2) decay with nearest neighbor type information patterns.},
	address = {Santa Barbara, CA},
	booktitle = {3rd IFAC Workshop on Distributed Estimation and Control in Networked Systems(NecSys 2012), pp. 288-293, Santa Barbara, California},
	pages = {pp. 288-293}
}
@article {
	title = {Distributed Learning Algorithm for Multi agent Welfare Optimization},
	author = {Menon, Anup., Baras, John S.},
	year = {2016},
	month = {01/2016},
	abstract = {A multi-agent system comprising N non-strategic agents is considered. Each agent picks actions~from a finite set and receives a payoff that depends on the actions of the whole. The setting is non-model~based where the exact form of the utility (or payoff) functions are unknown, and only their ~values can~be measured by the respective agents. A distributed algorithm which enables the agents to learn to play~welfare (i.e. the sum of individual utilities) optimal actions is proposed. While the exact form of the~utilities is unknown, a novel framework is developed to capture implicit communication{\textemdash}the measured~change in utility by an agent due to actions by another{\textemdash}and convergence of the algorithm is proved~under appropriate assumptions on implicit and explicit communications. The proposed algorithm has~kinship with a body of work in learning in repeated games and the main analysis tool is perturbed~Markov chains.},
	journal = {IEEE Transactions on Automatic Control}
}
@techreport {
	title = {A Distributed Learning Algorithm with Bit-valued Communications for Multi-agent Welfare Optimization},
	author = {Menon, Anup., Baras, John S.},
	year = {2013},
	month = {07/2013},
	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. [1] and in the authors{\textquoteright} earlier work [2] 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.}
}
