Awards
There are a number of awards associated with the AAMAS conference, some of which are known in advance, and some of which are announced at the conference.
ACM SIGART Autonomous Agents Research Award
The ACM SIGART Autonomous Agents Research Award is an annual award for excellence in research in the area of autonomous agents. The award is intended to recognize researchers in autonomous agents whose current work is an important influence on the field. The award is an official ACM award, funded by an endowment created by ACM SIGART from the proceeds of previous Autonomous Agents conferences. Candidates for the award are nominated through an open nomination process. Previous winners of the SIGART Autonomous Research Award were Jonathan Gratch and Stacy Marsella (2010), Manuela Veloso (2009), Yoav Shoham (2008), Sarit Kraus (2007). Michael Wooldridge (2006), Milind Tambe (2005), Makoto Yokoo (2004), Nick Jennings (2003), Katia Sycara (2002), and Tuomas Sandholm (2001).
The 2011 ACM SIGART Autonomous Agents Research Award recipient is Joe Halpern from Cornell University.
IFAAMAS Victor Lesser Distinguished Dissertation Award
This award was started for dissertations defended in 2006 and is named for Professor Victor Lesser, a long standing member of the AAMAS community who has graduated a large number of outstanding PhD students in the area. To be eligible for the 2010 award, a dissertation had to have been written as part of a PhD defended during the year 2010, and had to be nominated by the supervisor with three supporting references. Selection is based on originality, depth, impact and written quality, supported by quality publications. Previous winners of this award were Andrew Gilpin (2009), Ariel Procaccia (2008), Radu Jurca (2007), and Vincent Conitzer (2006).
The 2010 IFAAMAS Victor Lesser Distinguished Dissertation Award recipient is Dr. Bo An of University of Massachusetts at Amherst (advised by Prof. Victor R. Lesser), with his dissertation “Automated Negotiation for Complex Multi-agent Resource Allocation”.
Due to the extremely close competition this year, two additional candidates were selected for runner-up prizes. In particular,
these are Dr. Chih-Han Yu of Harvard University (advised by Prof. Radhika Nagpal) and Dr. Mingyu Guo of Duke University (advised by Prof. Vincent Conitzer).
IFAAMAS Award for Influential Papers
The International Foundation for Autonomous Agents and Multi-Agent Systems set up an influential paper award in 2006 to recognize publications that have made seminal contributions to the field. Such papers represent the best and most influential work in the area of autonomous agents and multi-agent systems. These papers might, therefore, have proved a key result, led to the development of a new sub-field, demonstrated a significant new application or system, or simply presented a new way of thinking about a topic that has proved influential. The award is open to any paper that was published at least 10 years before the award is made. The paper can have been published in any journal, conference, or workshop. The award is sponsored by the Agent Theories, Architectures and Languages foundation.
Previous awards are as follows:
2010
M. Yokoo, E.H. Durfree, T. Ishida & K. Kuwabara (1998) The Distributed Constraint Satisfaction Problem: Formalization and Algorithms. IEEE Transactions on Knowledge and Data Engineering 10:673-685.
M. Yokoo & K. Hiriyama (1996) Distributed Breakout Algorithm for Solving Distributed Constraint Satisfaction Problems. Second International Conference on Multiagent Systems (ICMAS-96), pp.401-408.
2009
M. N. Huhns. (Ed.) (1987) Distributed Artificial Intelligence. London, Pitman.
A. Bond and L. Gasser. (Eds.) (1988) Readings in Distributed Artificial Intelligence. San Mateo, CA, Morgan Kaufmann.
L. Gasser and M. N. Huhns. (Eds.) (1989) Distributed Artificial Intelligence (Volume II). Pitman and Morgan Kaufmann.
2008
M. E. Bratman, D. J. Israel and M. E. Pollack (1988) Plans and resource-bounded practical reasoning. Computational Intelligence, 4, pages 349-355.
E. H. Durfee and V. Lesser (1991) Partial global planning: A coordination framework for distributed hypothesis formation. In: IEEE Transactions on Systems, Man, and Cybernetics, 21, pages 1167-1183.
2007
J. S. Rosenschein and M. R. Genesereth (1985) Deals Among Rational Agents. In: Proceedings of the 9th International Joint Conference on Artificial Intelligence, Los Angeles , California , August 1985, pages 91-99.
A. Rao and M. Georgeff (1991) Modelling rational agents within a BDI-architecture. In: Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning, Cambridge, Massachussets, pages 473-484.
B. J. Grosz and S. Kraus (1996) Collaborative Plans for Complex Group Actions. Artificial Intelligence, 86, pages 269-358.
2006
P. R. Cohen and H. Levesque (1990) Intention is choice with commitment. Artificial Intelligence , 42(2-3), pages 213-261.
R. Davis and R. Smith (1983) Negotiation as a Metaphor for Distributed Problem Solving. Artificial Intelligence, 20(1), pages 63-109.
Best paper nominees
R40
Boolean games are a natural, compact, and expressive class of logic-based games, in which each player exercises unique control over some set of Boolean variables, and has some logical goal formula that it desires to be achieved. A player's strategy set is the set of all possible valuations that may be made to its variables. A player's goal formula may contain variables controlled by other agents, and in this case, it must reason strategically about how best to assign values to its variables. In the present paper, we consider the possibility of overlaying Boolean games with taxation schemes. A taxation scheme imposes a cost on every possible assignment an agent can make. By designing a taxation scheme appropriately, it is possible to perturb the preferences of the agents within a society, so that agents are rationally incentivised to choose some socially desirable equilibrium that would not otherwise be chosen, or incentivised to rule out some socially undesirable equilibria. After formally presenting the model, we explore some issues surrounding it (e.g., the complexity of finding a taxation scheme that implements some socially desirable outcome), and then discuss possible desirable properties of taxation schemes.
Designing Incentives for Boolean Games
Ulle Endriss, Sarit Kraushas 4 papers, Jérôme Langhas 2 papers, Michael Wooldridgehas 6 papers
B42
In their groundbreaking paper, Bartholdi, Tovey and Trick argued that many well-known voting rules, such as Plurality, Borda, Copeland and Maximin are easy to manipulate. An important assumption made in that paper is that the manipulator's goal is to ensure that his preferred candidate is among the candidates with the maximum score, or, equivalently, that ties are broken in favor of the manipulator's preferred candidate. In this paper, we examine the role of this assumption in the easiness results of Bartholdi et al. We observe that the algorithm presented in Bartholdi et al extends to all rules that break ties according to a fixed ordering over the candidates. We then show that all scoring rules are easy to manipulate if the winner is selected from all tied candidates uniformly at random. This result extends to Maximin under an additional assumption on the manipulator's utility function that is inspired by the original model of Bartholdi et al. In contrast, we show that manipulation becomes hard when arbitrary polynomial-time tie-breaking rules are allowed, both for the rules considered in Bartholdi et al, and for a large class of scoring rules.
Ties Matter: Complexity of Voting Manipulation Revisited
Svetlana Obraztsova, Edith Elkindhas 3 papers, Noam Hazon
Best student paper nominees
R39
Online digital goods auctions are settings where a seller with an unlimited supply of goods (e.g. music or movie downloads) interacts with a stream of potential buyers. In the posted price setting, the seller makes a take-it-or-leave-it offer to each arriving buyer. We study the seller's revenue maximization problem in posted-price auctions of digital goods. We find that algorithms from the multi-armed bandit literature like UCB, which come with good regret bounds, can be slow to converge. We propose and study two alternatives: (1) a scheme based on using Gittins indices with priors that make appropriate use of domain knowledge; (2) a new learning algorithm, LLVD, that assumes a linear demand curve, and maintains a Beta prior over the free parameter using a moment-matching approximation. LLVD is not only (approximately) optimal for linear demand, but also learns fast and performs well when the linearity assumption is violated, for example in the cases of two natural valuation distributions, exponential and log-normal.
Learning the Demand Curve in Posted-Price Digital Goods Auctions
Meenal Chhabrahas 2 papers, Sanmay Dashas 2 papers
R38
Overlapping Coalition Formation (OCF) games are cooperative games where the players can simultaneously participate in several coalitions. Capturing the notion of stability in OCF games is a difficult task: a player may deviate by abandoning some, but not all of the coalitions he is involved in, and the crucial question is whether he then gets to keep his payoff from the unaffected coalitions. In related work the authors introduce three stability concepts for OCF games – the conservative, refined, and optimistic core – that are based on different answers to this question. In this paper, we propose a unified framework for the study of stability in the OCF setting, which encompasses the concepts considered previously as well as a wide variety of alternative stability concepts. Our approach is based on the notion of an arbitrator, which can be thought of as an external party that determines payoff to deviators. We give a complete characterization of outcomes that are stable under arbitration. In particular, our results provide a criterion for the outcome to be in the refined or optimistic core, thus complementing previously results for the conservative core, and answering questions left open previously. We also introduce a notion of the nucleolus for arbitrated OCF games, and argue that it is non-empty. Finally, we extend the definition of the Shapley value to the OCF setting, and provide an axiomatic characterization for it.
Arbitrators in Overlapping Coalition Formation Games
Yair Zick, Edith Elkindhas 3 papers
Best innovative application paper nominees
B39
Central to the vision of the smart grid is the deployment of smart meters that will allow autonomous software agents, representing the consumers, to optimise their use of devices and heating in the smart home while interacting with the grid. However, without some form of coordination, the population of agents may end up with overly-homogeneous optimised consumption patterns that may generate significant peaks in demand in the grid. These peaks, in turn, reduce the efficiency of the overall system, increase carbon emissions, and may even, in the worst case, cause blackouts. Hence, in this paper, we introduce a novel model of a Decentralised Demand Side Management (DDSM) mechanism that allows agents, by adapting the deferment of their loads based on grid prices, to coordinate in a decentralised manner. Specifically, using average UK consumption profiles for 26M homes, we demonstrate that, through an emergent coordination of the agents, the peak demand of domestic consumers in the grid can be reduced by up to 17% and carbon emissions by up to 6%. We also show that our DDSM mechanism is robust to the increasing electrification of heating in UK homes (i.e., it exhibits a similar efficiency).
Agent-Based Control for Decentralised Demand Side Management in the Smart Grid
Sarvapali D. Ramchurn, Perukrishnen Vytelingum, Alex Rogershas 6 papers, Nicholas R. Jenningshas 9 papers
G40
Building on research previously reported at AAMAS conferences, this paper describes an innovative application of a novel gametheoretic approach for a national scale security deployment. Working with the United States Transportation Security Administration (TSA), we have developed a new application called GUARDS to assist in resource allocation tasks for airport protection at over 400 United States airports. In contrast with previous efforts such as ARMOR and IRIS, which focused on one-off tailored applications and one security activity (e.g. canine patrol or checkpoints) per application, GUARDS faces three key issues: (i) reasoning about hundreds of heterogeneous security activities; (ii) reasoning over diverse potential threats; (iii) developing a system designed for hundreds of end-users. Since a national deployment precludes tailoring to specific airports, our key ideas are: (i) creating a new game-theoretic framework that allows for heterogeneous defender activities and compact modeling of a large number of threats; (ii) developing an efficient solution technique based on general purpose Stackelberg game solvers; (iii) taking a partially centralized approach for knowledge acquisition and development of the system. In doing so we develop a software scheduling assistant, GUARDS, designed to reason over two agents – the TSA and a potential adversary – and allocate the TSA's limited resources across hundreds of security activities in order to provide protection within airports. The scheduling assistant has been delivered to the TSA and is currently under evaluation and testing for scheduling practices at an undisclosed airport. If successful, the TSA intends to incorporate the system into their unpredictable scheduling practices nationwide. In this paper we discuss the design choices and challenges encountered during the implementation of GUARDS. GUARDS represents promising potential for transitioning years of academic research into a nationally deployed system.
GUARDS - Game Theoretic Security Allocation on a National Scale
James Pitahas 2 papers, Milind Tambehas 8 papers, Christopher Kiekintveldhas 4 papers, Shane Cullen, Erin Steigerwald
G38
Grid-Integrated Vehicles (GIVs) are plug-in Electric Drive Vehicles (EDVs) with power-management and other controls that allow them to respond to external commands sent by power-grid operators, or their affiliates, when parked and plugged-in to the grid. At a bare minimum, such GIVs should respond to demand-management commands or pricing signals to delay, reduce or switch-off the rate of charging when the demand for electricity is high. In more advanced cases, these GIVs might sell both power and storage capacity back to the grid in any of the several electric power markets – a concept known as Vehicle-to-Grid power or V2G power. Although individual EDVs control too little power to sell in the market at an individual level, a large group of EDVs may form an aggregate or coalition that controls enough power to meaningfully sell, at a profit, in these markets. The profits made by such a coalition can then be used by the coalition members to offset the costs of the electric vehicles and batteries themselves. In this paper we describe an implemented and deployed multi-agent system that is used to integrate EDVs into the electricity grid managed by PJM, the largest transmission service operator in the world. We provide a brief introduction to GIVs and the various power markets and discuss why multi-agent systems are a good match for this application.
Deploying Power Grid-Integrated Electric Vehicles as a Multi-Agent System
Sachin Kamboj, Willett Kempton, Keith S. Decker
