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Travis Service

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers
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5

AAMAS Conference 2012 Conference Paper

Communication Complexity of Approximating Voting Rules

  • Travis Service
  • Julie Adams

This paper considers the communication complexity of approximating common voting rules. Both upper and lower bounds are presented. For $n$ voters and $m$ alternatives. It is shown that for all $\epsilon \in (0, 1)$, the communication complexity of obtaining a $1 - \epsilon$ approximation to Borda is $O(\log(\frac{1}{\epsilon}) nm)$. A lower bound of $\Omega(nm)$ is provided for small values of $\epsilon$. The communication complexity of computing the true Borda winner is $\Omega(nm\log(m))$. Thus, in the case of Borda, one can obtain arbitrarily good approximations with less communication overhead than is required to compute the true Borda winner. For other voting rules, no such $1 \pm \epsilon$ approximation scheme exists. In particular, it is shown that the communication complexity of computing any constant factor approximation, $\rho$, to Bucklin is $\Omega(\frac{nm}{\rho^2})$. Conitzer and Sandholm show that the communication complexity of computing the true Bucklin winner is $O(nm)$. However, we show that for all $\delta \in (0, 1)$, the communication complexity of computing a $m^{\delta}$ approximate winner in Bucklin elections is $O(nm^{1-\delta}\log(m))$. For $\delta \in (\frac{1}{2}, 1)$ a lower bound of $\Omega( nm^{1-2\delta} )$ is also provided. Similar lower bounds are presented on the communication complexity of computing approximate winners in Copeland elections.

AAMAS Conference 2012 Conference Paper

Strategyproof Approximations of Distance Rationalizable Voting Rules

  • Travis Service
  • Julie Adams

This paper considers randomized strategyproof approximations to distance rationalizable voting rules. It is shown that the \emph{Random Dictator} voting rule (return the top choice of a random voter) nontrivially approximates a large class of distances with respect to unanimity. Any randomized voting rule that deviates too greatly from the Random Dictator voting rule is shown to obtain a trivial approximation (i. e. , equivalent to ignoring the voters' votes and selecting an alternative uniformly at random). The outlook for consensus classes, other than unanimity is bleaker. This paper shows that for a large number of distance rationalizations, with respect to the majority and Condorcet consensus classes that no strategyproof randomized rule can asymptotically outperform uniform random selection of an alternative. This paper also shows that veto cannot be approximated nontrivially when approximations are measured with respect to minimizing the number of vetoes an alternative receives.

AIJ Journal 2011 Journal Article

Randomized coalition structure generation

  • Travis Service
  • Julie Adams

Randomization can be employed to achieve constant factor approximations to the coalition structure generation problem in less time than all previous approximation algorithms. In particular, this manuscript presents a new randomized algorithm that can generate a 2 3 approximate solution in O ( n 2. 587 n ) time, improving upon the previous algorithm that required O ( n 2. 83 n ) time to guarantee the same performance. Also, the presented new techniques allow a 1 4 approximate solution to be generated in the optimal time of O ( 2 n ) and improves on the previous best approximation ratio obtainable in O ( 2 n ) time of 1 8. The presented algorithms are based upon a careful analysis of the sizes and numbers of coalitions in the smallest optimal coalition structures. An empirical analysis of the new randomized algorithms compared to their deterministic counterparts is provided. We find that the presented randomized algorithms generate solutions with utility comparable to what is returned by their deterministic counterparts (in some cases producing better results on average). Moreover, a significant speedup was found for most approximation ratios for the randomized algorithms over the deterministic algorithms. In particular, the randomized 1 2 approximate algorithm runs in approximately 22. 4 % of the time required for the deterministic 1 2 approximation algorithm for problems with between 20 and 27 agents.

AAMAS Conference 2010 Conference Paper

Anytime Dynamic Programming for Coalition Structure Generation

  • Travis Service
  • Julie Adams

Determining the optimal coalition structure is a central problem in multi-agent systems. Two popular techniques includedynamic programming and anytime search algorithms. Dynamic programming algorithms guarantee an optimal solution and have the best worst case running time. Anytime algorithms are flexible as they can terminate before the searchhas completed, but have a significantly poorer worst caseruntime. This paper provides an anytime dynamic programming algorithm with the worst case runtime of dynamic programming and the flexibility of anytime search.

AAAI Conference 2010 Conference Paper

Approximate Coalition Structure Generation

  • Travis Service
  • Julie Adams

Coalition formation is a fundamental problem in multi-agent systems. In characteristic function games (CFGs), each coalition C of agents is assigned a value indicating the joint utility those agents will receive if C is formed. CFGs are an important class of cooperative games; however, determining the optimal coalition structure, partitioning of the agents into a set of coalitions that maximizes the social welfare, currently requires O(3n ) time for n agents. In light of the high computational complexity of the coalition structure generation problem, a natural approach is to relax the optimality requirement and attempt to find an approximate solution that is guaranteed to be close to optimal. Unfortunately, it has been shown that guaranteeing a solution within any factor of the optimal requires Ω(2n ) time. Thus, the best that can be hoped for is to find an algorithm that returns solutions that are guaranteed to be as close to the optimal as possible, in as close to O(2n ) time as possible. This paper contributes to the state-of-the-art by presenting an algorithm that achieves better quality guarantees with lower worst case running times than all currently existing algorithms. For example, our algorithm improves the previous best approximation ratio of 1 2 obtainable in O( √ n2. 83n ) time to 2 3 and obtains a 1 2 approximation in O( √ n2. 59n ). Our approach is also the first algorithm to guarantee a constant factor approximation ratio, 1 8, in the optimal time of O(2n ). The previous best ratio obtainable in O(2n ) was 2 n.