5,454 research outputs found
Proceedings of the 11th European Agent Systems Summer School Student Session
This volume contains the papers presented at the Student Session of the 11th European Agent Systems Summer School (EASSS) held on 2nd of September 2009 at Educatorio della Providenza, Turin, Italy. The Student Session, organised by students, is designed to encourage student interaction and feedback from the tutors. By providing the students with a conference-like setup, both in the presentation and in the review process, students have the opportunity to prepare their own submission, go through the selection process and present their work to each other and their interests to their fellow students as well as internationally leading experts in the agent field, both from the theoretical and the practical sector. Table of Contents: Andrew Koster, Jordi Sabater Mir and Marco Schorlemmer, Towards an inductive algorithm for learning trust alignment . . . 5; Angel Rolando Medellin, Katie Atkinson and Peter McBurney, A Preliminary Proposal for Model Checking Command Dialogues. . . 12; Declan Mungovan, Enda Howley and Jim Duggan, Norm Convergence in Populations of Dynamically Interacting Agents . . . 19; Akın Günay, Argumentation on Bayesian Networks for Distributed Decision Making . . 25; Michael Burkhardt, Marco Luetzenberger and Nils Masuch, Towards Toolipse 2: Tool Support for the JIAC V Agent Framework . . . 30; Joseph El Gemayel, The Tenacity of Social Actors . . . 33; Cristian Gratie, The Impact of Routing on Traffic Congestion . . . 36; Andrei-Horia Mogos and Monica Cristina Voinescu, A Rule-Based Psychologist Agent for Improving the Performances of a Sportsman . . . 39; --Autonomer Agent,Agent,Künstliche Intelligenz
Agent-Based Computational Economics
Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Starting from initial conditions, specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other and learn from these interactions. ACE is therefore a bottom-up culture-dish approach to the study of economic systems. This study discusses the key characteristics and goals of the ACE methodology. Eight currently active research areas are highlighted for concrete illustration. Potential advantages and disadvantages of the ACE methodology are considered, along with open questions and possible directions for future research.Agent-based computational economics; Autonomous agents; Interaction networks; Learning; Evolution; Mechanism design; Computational economics; Object-oriented programming.
Spatial Fluid Limits for Stochastic Mobile Networks
We consider Markov models of large-scale networks where nodes are
characterized by their local behavior and by a mobility model over a
two-dimensional lattice. By assuming random walk, we prove convergence to a
system of partial differential equations (PDEs) whose size depends neither on
the lattice size nor on the population of nodes. This provides a macroscopic
view of the model which approximates discrete stochastic movements with
continuous deterministic diffusions. We illustrate the practical applicability
of this result by modeling a network of mobile nodes with on/off behavior
performing file transfers with connectivity to 802.11 access points. By means
of an empirical validation against discrete-event simulation we show high
quality of the PDE approximation even for low populations and coarse lattices.
In addition, we confirm the computational advantage in using the PDE limit over
a traditional ordinary differential equation limit where the lattice is modeled
discretely, yielding speed-ups of up to two orders of magnitude
A Bayesian nonparametric approach to modeling market share dynamics
We propose a flexible stochastic framework for modeling the market share
dynamics over time in a multiple markets setting, where firms interact within
and between markets. Firms undergo stochastic idiosyncratic shocks, which
contract their shares, and compete to consolidate their position by acquiring
new ones in both the market where they operate and in new markets. The model
parameters can meaningfully account for phenomena such as barriers to entry and
exit, fixed and sunk costs, costs of expanding to new sectors with different
technologies and competitive advantage among firms. The construction is
obtained in a Bayesian framework by means of a collection of nonparametric
hierarchical mixtures, which induce the dependence between markets and provide
a generalization of the Blackwell-MacQueen P\'{o}lya urn scheme, which in turn
is used to generate a partially exchangeable dynamical particle system. A
Markov Chain Monte Carlo algorithm is provided for simulating trajectories of
the system, by means of which we perform a simulation study for transitions to
different economic regimes. Moreover, it is shown that the infinite-dimensional
properties of the system, when appropriately transformed and rescaled, are
those of a collection of interacting Fleming-Viot diffusions.Comment: Published in at http://dx.doi.org/10.3150/11-BEJ392 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
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