124,677 research outputs found
Algorithms and Conditional Lower Bounds for Planning Problems
We consider planning problems for graphs, Markov decision processes (MDPs),
and games on graphs. While graphs represent the most basic planning model, MDPs
represent interaction with nature and games on graphs represent interaction
with an adversarial environment. We consider two planning problems where there
are k different target sets, and the problems are as follows: (a) the coverage
problem asks whether there is a plan for each individual target set, and (b)
the sequential target reachability problem asks whether the targets can be
reached in sequence. For the coverage problem, we present a linear-time
algorithm for graphs and quadratic conditional lower bound for MDPs and games
on graphs. For the sequential target problem, we present a linear-time
algorithm for graphs, a sub-quadratic algorithm for MDPs, and a quadratic
conditional lower bound for games on graphs. Our results with conditional lower
bounds establish (i) model-separation results showing that for the coverage
problem MDPs and games on graphs are harder than graphs and for the sequential
reachability problem games on graphs are harder than MDPs and graphs; (ii)
objective-separation results showing that for MDPs the coverage problem is
harder than the sequential target problem.Comment: Accepted at ICAPS'1
Evolutionary games on graphs
Game theory is one of the key paradigms behind many scientific disciplines
from biology to behavioral sciences to economics. In its evolutionary form and
especially when the interacting agents are linked in a specific social network
the underlying solution concepts and methods are very similar to those applied
in non-equilibrium statistical physics. This review gives a tutorial-type
overview of the field for physicists. The first three sections introduce the
necessary background in classical and evolutionary game theory from the basic
definitions to the most important results. The fourth section surveys the
topological complications implied by non-mean-field-type social network
structures in general. The last three sections discuss in detail the dynamic
behavior of three prominent classes of models: the Prisoner's Dilemma, the
Rock-Scissors-Paper game, and Competing Associations. The major theme of the
review is in what sense and how the graph structure of interactions can modify
and enrich the picture of long term behavioral patterns emerging in
evolutionary games.Comment: Review, final version, 133 pages, 65 figure
Positional games on random graphs
We introduce and study Maker/Breaker-type positional games on random graphs.
Our main concern is to determine the threshold probability for the
existence of Maker's strategy to claim a member of in the unbiased game
played on the edges of random graph , for various target families
of winning sets. More generally, for each probability above this threshold we
study the smallest bias such that Maker wins the biased game. We
investigate these functions for a number of basic games, like the connectivity
game, the perfect matching game, the clique game and the Hamiltonian cycle
game
On the Complexity of the Mis\`ere Version of Three Games Played on Graphs
We investigate the complexity of finding a winning strategy for the mis\`ere
version of three games played on graphs : two variants of the game
, introduced by Stockmann in 2004 and the game on both directed and undirected graphs. We show that on general
graphs those three games are -Hard or Complete. For one
-Hard variant of , we find an algorithm to compute
an effective winning strategy in time when
is a bipartite graph
Strong games played on random graphs
In a strong game played on the edge set of a graph G there are two players,
Red and Blue, alternating turns in claiming previously unclaimed edges of G
(with Red playing first). The winner is the first one to claim all the edges of
some target structure (such as a clique, a perfect matching, a Hamilton cycle,
etc.). It is well known that Red can always ensure at least a draw in any
strong game, but finding explicit winning strategies is a difficult and a quite
rare task. We consider strong games played on the edge set of a random graph G
~ G(n,p) on n vertices. We prove, for sufficiently large and a fixed
constant 0 < p < 1, that Red can w.h.p win the perfect matching game on a
random graph G ~ G(n,p)
Cut-Matching Games on Directed Graphs
We give O(log^2 n)-approximation algorithm based on the cut-matching
framework of [10, 13, 14] for computing the sparsest cut on directed graphs.
Our algorithm uses only O(log^2 n) single commodity max-flow computations and
thus breaks the multicommodity-flow barrier for computing the sparsest cut on
directed graph
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