3,367 research outputs found
A Search Game on a Hypergraph with Booby Traps
A set of n boxes, located on the vertices of a hypergraph G, contain known
but different rewards. A Searcher opens all the boxes in some hyperedge of G
with the objective of collecting the maximum possible total reward. Some of the
boxes, however, are booby trapped. If the Searcher opens a booby trapped box,
the search ends and she loses all her collected rewards. We assume the number k
of booby traps is known, and we model the problem as a zero-sum game between
the maximizing Searcher and a minimizing Hider, where the Hider chooses k boxes
to booby trap and the Searcher opens all the boxes in some hyperedge. The
payoff is the total reward collected by the Searcher. This model could reflect
a military operation in which a drone gathers intelligence from guarded
locations, and a booby trapped box being opened corresponds to the drone being
destroyed or incapacitated. It could also model a machine scheduling problem,
in which rewards are obtained from successfully processing jobs but the machine
may crash. We solve the game when G is a 1-uniform hypergraph (the hyperedges
are all singletons), so the Searcher can open just 1 box. When G is the
complete hypergraph (containing all possible hyperedges), we solve the game in
a few cases: (1) same reward in each box, (2) k=1, and (3) n=4 and k=2. The
solutions to these few cases indicate that a general simple, closed form
solution to the game appears unlikely
Searching for Multiple Objects in Multiple Locations
Many practical search problems concern the search for multiple hidden objects
or agents, such as earthquake survivors. In such problems, knowing only the
list of possible locations, the Searcher needs to find all the hidden objects
by visiting these locations one by one. To study this problem, we formulate new
game-theoretic models of discrete search between a Hider and a Searcher. The
Hider hides balls in boxes, and the Searcher opens the boxes one by one
with the aim of finding all the balls. Every time the Searcher opens a box she
must pay its search cost, and she either finds one of the balls it contains or
learns that it is empty. If the Hider is an adversary, an appropriate payoff
function may be the expected total search cost paid to find all the balls,
while if the Hider is Nature, a more appropriate payoff function may be the
difference between the total amount paid and the amount the Searcher would have
to pay if she knew the locations of the balls a priori (the regret). We give a
full solution to the regret version of this game, and a partial solution to the
search cost version. We also consider variations on these games for which the
Hider can hide at most one ball in each box. The search cost version of this
game has already been solved in previous work, and we give a partial solution
in the regret version
Optimal Patrol of a Perimeter
A defender dispatches patrollers to circumambulate a perimeter to guard
against potential attacks. The defender decides on the time points to dispatch
patrollers and each patroller's direction and speed, as long as the long-run
rate patrollers are dispatched is capped at some constant. An attack at any
point on the perimeter requires the same amount of time, during which it will
be detected by each passing patroller independently with the same probability.
The defender wants to maximize the probability of detecting an attack before it
completes, while the attacker wants to minimize it. We study two scenarios,
depending on whether the patrollers are undercover or wear a uniform.
Conventional wisdom would suggest that the attacker gains advantage if he can
see the patrollers going by so as to time his attack, but we show that the
defender can achieve the same optimal detection probability by carefully
spreading out the patrollers probabilistically against a learning attacker.Comment: 17 pages, 1 figur
Parcel plus marketing plan : targeting student move-out's
http://deepblue.lib.umich.edu/bitstream/2027.42/96919/1/BBA_LinW_1998Final.pd
A microfluidic 2×2 optical switch
A 2×2 microfluidic-based optical switch is proposed and demonstrated. The switch is made of an optically clear silicon elastomer, Polydimethylsiloxane (PDMS), using soft lithography. It has insertion loss smaller than 1 dB and extinction ratio on the order of 20 dB. The device is switching between transmission (bypass) and reflection (exchange) modes within less than 20 m
Developing effective service policies for multiclass queues with abandonment:asymptotic optimality and approximate policy improvement
We study a single server queuing model with multiple classes and impatient customers. The goal is to determine a service policy to maximize the long-run reward rate earned from serving customers net of holding costs and penalties respectively due to customers waiting for and leaving before receiving service. We first show that it is without loss of generality to study a pure-reward model. Since standard methods can usually only compute the optimal policy for problems with up to three customer classes, our focus is to develop a suite of heuristic approaches, with a preference for operationally simple policies with good reward characteristics. One such heuristic is the Rμθ rule—a priority policy that ranks all customer classes based on the product of reward R, service rate μ, and abandonment rate θ. We show that the Rμθ rule is asymptotically optimal as customer abandonment rates approach zero and often performs well in cases where the simpler Rμ rule performs poorly. The paper also develops an approximate policy improvement method that uses simulation and interpolation to estimate the bias function for use in a dynamic programming recursion. For systems with two or three customer classes, our numerical study indicates that the best of our simple priority policies is near optimal in most cases; when it is not, the approximate policy improvement method invariably tightens up the gap substantially. For systems with five customer classes, our heuristics typically achieve within 4% of an upper bound for the optimal value, which is computed via a linear program that relies on a relaxation of the original system. The computational requirement of the approximate policy improvement method grows rapidly when the number of customer classes or the traffic intensity increases
GenerationMania: Learning to Semantically Choreograph
Beatmania is a rhythm action game where players must reproduce some of the
sounds of a song by pressing specific controller buttons at the correct time.
In this paper we investigate the use of deep neural networks to automatically
create game stages - called charts - for arbitrary pieces of music. Our
technique uses a multi-layer feed-forward network trained on sound sequence
summary statistics to predict which sounds in the music are to be played by the
player and which will play automatically. We use another neural network along
with rules to determine which controls should be mapped to which sounds. We
evaluated our system on the ability to reconstruct charts in a held-out test
set, achieving an -score that significantly beats LSTM baselines.Comment: To appear in AIIDE 201
The Attrition Dynamics of Multilateral War
We extend classical force-on-force combat models to study the attrition dynamics of three-way and multilateral war. We introduce a new multilateral combat model (the multiduel) which generalizes the Lanchester models, and solve it under an objective function which values one's own surviving force minus that of one's enemies. The outcome is stark: either one side is strong enough to destroy all the others combined, or all sides are locked in a stalemate which results in collective mutual annihilation. The situation in Syria fits this paradigm
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