51,041 research outputs found
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
Ludii -- The Ludemic General Game System
While current General Game Playing (GGP) systems facilitate useful research
in Artificial Intelligence (AI) for game-playing, they are often somewhat
specialised and computationally inefficient. In this paper, we describe the
"ludemic" general game system Ludii, which has the potential to provide an
efficient tool for AI researchers as well as game designers, historians,
educators and practitioners in related fields. Ludii defines games as
structures of ludemes -- high-level, easily understandable game concepts --
which allows for concise and human-understandable game descriptions. We
formally describe Ludii and outline its main benefits: generality,
extensibility, understandability and efficiency. Experimentally, Ludii
outperforms one of the most efficient Game Description Language (GDL)
reasoners, based on a propositional network, in all games available in the
Tiltyard GGP repository. Moreover, Ludii is also competitive in terms of
performance with the more recently proposed Regular Boardgames (RBG) system,
and has various advantages in qualitative aspects such as generality.Comment: Accepted at ECAI 202
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