15,167 research outputs found
The Quality of Equilibria for Set Packing Games
We introduce set packing games as an abstraction of situations in which
selfish players select subsets of a finite set of indivisible items, and
analyze the quality of several equilibria for this class of games. Assuming
that players are able to approximately play equilibrium strategies, we show
that the total quality of the resulting equilibrium solutions is only
moderately suboptimal. Our results are tight bounds on the price of anarchy for
three equilibrium concepts, namely Nash equilibria, subgame perfect equilibria,
and an equilibrium concept that we refer to as -collusion Nash equilibrium
Tighter Bounds on the Inefficiency Ratio of Stable Equilibria in Load Balancing Games
In this paper we study the inefficiency ratio of stable equilibria in load
balancing games introduced by Asadpour and Saberi [3]. We prove tighter lower
and upper bounds of 7/6 and 4/3, respectively. This improves over the best
known bounds in problem (19/18 and 3/2, respectively). Equivalently, the
results apply to the question of how well the optimum for the -norm can
approximate the -norm (makespan) in identical machines scheduling
A Parameterisation of Algorithms for Distributed Constraint Optimisation via Potential Games
This paper introduces a parameterisation of learning algorithms for distributed constraint optimisation problems (DCOPs). This parameterisation encompasses many algorithms developed in both the computer science and game theory literatures. It is built on our insight that when formulated as noncooperative games, DCOPs form a subset of the class of potential games. This result allows us to prove convergence properties of algorithms developed in the computer science literature using game theoretic methods. Furthermore, our parameterisation can assist system designers by making the pros and cons of, and the synergies between, the various DCOP algorithm components clear
Pilot, Rollout and Monte Carlo Tree Search Methods for Job Shop Scheduling
Greedy heuristics may be attuned by looking ahead for each possible choice,
in an approach called the rollout or Pilot method. These methods may be seen as
meta-heuristics that can enhance (any) heuristic solution, by repetitively
modifying a master solution: similarly to what is done in game tree search,
better choices are identified using lookahead, based on solutions obtained by
repeatedly using a greedy heuristic. This paper first illustrates how the Pilot
method improves upon some simple well known dispatch heuristics for the
job-shop scheduling problem. The Pilot method is then shown to be a special
case of the more recent Monte Carlo Tree Search (MCTS) methods: Unlike the
Pilot method, MCTS methods use random completion of partial solutions to
identify promising branches of the tree. The Pilot method and a simple version
of MCTS, using the -greedy exploration paradigms, are then
compared within the same framework, consisting of 300 scheduling problems of
varying sizes with fixed-budget of rollouts. Results demonstrate that MCTS
reaches better or same results as the Pilot methods in this context.Comment: Learning and Intelligent OptimizatioN (LION'6) 7219 (2012
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