2,962 research outputs found
TAIP: an anytime algorithm for allocating student teams to internship programs
In scenarios that require teamwork, we usually have at hand a variety of
specific tasks, for which we need to form a team in order to carry out each
one. Here we target the problem of matching teams with tasks within the context
of education, and specifically in the context of forming teams of students and
allocating them to internship programs. First we provide a formalization of the
Team Allocation for Internship Programs Problem, and show the computational
hardness of solving it optimally. Thereafter, we propose TAIP, a heuristic
algorithm that generates an initial team allocation which later on attempts to
improve in an iterative process. Moreover, we conduct a systematic evaluation
to show that TAIP reaches optimality, and outperforms CPLEX in terms of time.Comment: 10 pages, 7 figure
Online Gamma-Ray Burst catalog for neutrino telescopes
The origin of cosmic rays is still one of the unresolved questions in modern
physics. Violent and high energetic explosions of {\gamma}-ray emission known
as Gamma Ray Bursts (GRBs) are perhaps one of the main candidates of sources of
hadron acceleration and therefore of neutrino emission. Neutrino telescopes
search for signatures of cosmic neutrinos such as an excess of neutrinos in
time and space coincidence with a GRB. These searches use catalogues of GRBs by
satellite experiments. The online catalog presented here is a useful tool that
provides a reference catalog of GRBs for neutrino telescopes in particular and
GRBs analyzers in general.Comment: 4 pages, 2 figures, 32nd ICRC Beijing 201
Synergistic Team Composition
Effective teams are crucial for organisations, especially in environments
that require teams to be constantly created and dismantled, such as software
development, scientific experiments, crowd-sourcing, or the classroom. Key
factors influencing team performance are competences and personality of team
members. Hence, we present a computational model to compose proficient and
congenial teams based on individuals' personalities and their competences to
perform tasks of different nature. With this purpose, we extend Wilde's
post-Jungian method for team composition, which solely employs individuals'
personalities. The aim of this study is to create a model to partition agents
into teams that are balanced in competences, personality and gender. Finally,
we present some preliminary empirical results that we obtained when analysing
student performance. Results show the benefits of a more informed team
composition that exploits individuals' competences besides information about
their personalities
Weaving a fabric of socially aware agents
The expansion of web-enabled social interaction has shed light on social aspects of intelligence that have not been typically studied within the AI paradigm so far. In this context, our aim is to understand what constitutes intelligent social behaviour and to build computational systems that support it. We argue that social intelligence involves socially aware, autonomous individuals that agree on how to accomplish a common endeavour, and then enact such agreements. In particular, we provide a framework with the essential elements for such agreements to be achieved and executed by individuals that meet in an open environment. Such framework sets the foundations to build a computational infrastructure that enables socially aware autonomy.This work has been supported by the projects EVE(TIN2009-14702-C02-01) and AT (CSD2007-0022)Peer Reviewe
Indirect dark matter search in the Galactic Centre with IceCube
It is assumed that dark matter can annihilate or decay into Standard Model
particles which then can produce a neutrino flux detectable at IceCube. Such a
signal can be emitted from the Galactic Center thanks to the high density of
dark matter abundance being gravitationally captured. This analysis aims at
searching for neutrino signals from dark matter annihilation and decay in the
Galactic Center using 9 years of IceCube-DeepCore data with an optimized
selection for low energy. In this contribution, we present the sensitivities on
the thermally averaged dark matter self-annihilation cross-section for dark
matter masses ranging from 5 GeV up to 8 TeV.Comment: Presented at the 38th International Cosmic Ray Conference (ICRC2023).
See arXiv:2307.13047 for all IceCube contribution
Algorithms for Graph-Constrained Coalition Formation in the Real World
Coalition formation typically involves the coming together of multiple,
heterogeneous, agents to achieve both their individual and collective goals. In
this paper, we focus on a special case of coalition formation known as
Graph-Constrained Coalition Formation (GCCF) whereby a network connecting the
agents constrains the formation of coalitions. We focus on this type of problem
given that in many real-world applications, agents may be connected by a
communication network or only trust certain peers in their social network. We
propose a novel representation of this problem based on the concept of edge
contraction, which allows us to model the search space induced by the GCCF
problem as a rooted tree. Then, we propose an anytime solution algorithm
(CFSS), which is particularly efficient when applied to a general class of
characteristic functions called functions. Moreover, we show how CFSS can
be efficiently parallelised to solve GCCF using a non-redundant partition of
the search space. We benchmark CFSS on both synthetic and realistic scenarios,
using a real-world dataset consisting of the energy consumption of a large
number of households in the UK. Our results show that, in the best case, the
serial version of CFSS is 4 orders of magnitude faster than the state of the
art, while the parallel version is 9.44 times faster than the serial version on
a 12-core machine. Moreover, CFSS is the first approach to provide anytime
approximate solutions with quality guarantees for very large systems of agents
(i.e., with more than 2700 agents).Comment: Accepted for publication, cite as "in press
Automating decision making to help establish norm-based regulations
Norms have been extensively proposed as coordination mechanisms for both
agent and human societies. Nevertheless, choosing the norms to regulate a
society is by no means straightforward. The reasons are twofold. First, the
norms to choose from may not be independent (i.e, they can be related to each
other). Second, different preference criteria may be applied when choosing the
norms to enact. This paper advances the state of the art by modeling a series
of decision-making problems that regulation authorities confront when choosing
the policies to establish. In order to do so, we first identify three different
norm relationships -namely, generalisation, exclusivity, and substitutability-
and we then consider norm representation power, cost, and associated moral
values as alternative preference criteria. Thereafter, we show that the
decision-making problems faced by policy makers can be encoded as linear
programs, and hence solved with the aid of state-of-the-art solvers
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