1,893 research outputs found
The alldifferent Constraint: A Survey
The constraint of difference is known to the constraint programming community
since Lauriere introduced Alice in 1978. Since then, several solving strategies
have been designed for this constraint. In this paper we give both a practical
overview and an abstract comparison of these different strategies.Comment: 12 pages, 3 figures, paper accepted at the 6th Annual workshop of the
ERCIM Working Group on Constraint
Privacy-Preserving Public Information for Sequential Games
In settings with incomplete information, players can find it difficult to
coordinate to find states with good social welfare. For example, in financial
settings, if a collection of financial firms have limited information about
each other's strategies, some large number of them may choose the same
high-risk investment in hopes of high returns. While this might be acceptable
in some cases, the economy can be hurt badly if many firms make investments in
the same risky market segment and it fails. One reason why many firms might end
up choosing the same segment is that they do not have information about other
firms' investments (imperfect information may lead to `bad' game states).
Directly reporting all players' investments, however, raises confidentiality
concerns for both individuals and institutions.
In this paper, we explore whether information about the game-state can be
publicly announced in a manner that maintains the privacy of the actions of the
players, and still suffices to deter players from reaching bad game-states. We
show that in many games of interest, it is possible for players to avoid these
bad states with the help of privacy-preserving, publicly-announced information.
We model behavior of players in this imperfect information setting in two ways
-- greedy and undominated strategic behaviours, and we prove guarantees on
social welfare that certain kinds of privacy-preserving information can help
attain. Furthermore, we design a counter with improved privacy guarantees under
continual observation
A note on the data-driven capacity of P2P networks
We consider two capacity problems in P2P networks. In the first one, the
nodes have an infinite amount of data to send and the goal is to optimally
allocate their uplink bandwidths such that the demands of every peer in terms
of receiving data rate are met. We solve this problem through a mapping from a
node-weighted graph featuring two labels per node to a max flow problem on an
edge-weighted bipartite graph. In the second problem under consideration, the
resource allocation is driven by the availability of the data resource that the
peers are interested in sharing. That is a node cannot allocate its uplink
resources unless it has data to transmit first. The problem of uplink bandwidth
allocation is then equivalent to constructing a set of directed trees in the
overlay such that the number of nodes receiving the data is maximized while the
uplink capacities of the peers are not exceeded. We show that the problem is
NP-complete, and provide a linear programming decomposition decoupling it into
a master problem and multiple slave subproblems that can be resolved in
polynomial time. We also design a heuristic algorithm in order to compute a
suboptimal solution in a reasonable time. This algorithm requires only a local
knowledge from nodes, so it should support distributed implementations.
We analyze both problems through a series of simulation experiments featuring
different network sizes and network densities. On large networks, we compare
our heuristic and its variants with a genetic algorithm and show that our
heuristic computes the better resource allocation. On smaller networks, we
contrast these performances to that of the exact algorithm and show that
resource allocation fulfilling a large part of the peer can be found, even for
hard configuration where no resources are in excess.Comment: 10 pages, technical report assisting a submissio
Optimal Recombination in Genetic Algorithms
This paper surveys results on complexity of the optimal recombination problem
(ORP), which consists in finding the best possible offspring as a result of a
recombination operator in a genetic algorithm, given two parent solutions. We
consider efficient reductions of the ORPs, allowing to establish polynomial
solvability or NP-hardness of the ORPs, as well as direct proofs of hardness
results
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