Sciweavers

ATAL
2015
Springer
8 years 7 months ago
How Hard is Bribery in Party Based Elections?
d Abstract) Yongjie Yang Universität des Saarlandes
Yongjie Yang, Yash Raj Shrestha, Jiong Guo
ATAL
2015
Springer
8 years 7 months ago
A Unifying Methodology for Confronting Uncertainties in Security Games: Advances and Algorithms: (Doctoral Consortium)
Given the real-world applications of Stackelberg security games (SSGs), addressing uncertainties in these games is a major challenge. Two competitive approaches have been pursued ...
Thanh Hong Nguyen, Milind Tambe
ATAL
2015
Springer
8 years 7 months ago
Paving the way for Large-Scale Combinatorial Auctions
Francisco Cruz-Mencia, Jesús Cerquides, Ant...
ATAL
2015
Springer
8 years 7 months ago
Metrics for Evaluating Modularity and Extensibility in HMAS Systems
Nowadays, software systems are more and more frequently designed in order to realize complex dynamical behavior for solving complicated problems. Holonic Multi Agent Systems (HMAS...
Massimo Cossentino, Carmelo Lodato, Salvatore Lope...
ATAL
2015
Springer
8 years 7 months ago
Considering Agent and Task Openness in Ad Hoc Team Formation
Bin Chen, Xi Chen, Anish Timsina, Leen-Kiat Soh
ATAL
2015
Springer
8 years 7 months ago
Stackelberg Games for Robust Vaccine Design
Drug and vaccination therapies are important tools in the battle against infectious diseases such as HIV and influenza. However, many viruses, including HIV, can rapidly escape t...
Swetasudha Panda, Yevgeniy Vorobeychik
ATAL
2015
Springer
8 years 7 months ago
Controlling Elections by Replacing Candidates or Votes
Andrea Loreggia, Nina Narodytska, Francesca Rossi,...
ATAL
2015
Springer
8 years 7 months ago
To Ask, Sense, or Share: Ad Hoc Information Gathering
Agents operating in complex (e.g., dynamic, uncertain, partially observable) environments must gather information from various sources to inform their incomplete knowledge. Two po...
Adam Eck, Leen-Kiat Soh
ATAL
2015
Springer
8 years 7 months ago
Monte Carlo Hierarchical Model Learning
Reinforcement learning (RL) is a well-established paradigm for enabling autonomous agents to learn from experience. To enable RL to scale to any but the smallest domains, it sary ...
Jacob Menashe, Peter Stone
ATAL
2015
Springer
8 years 7 months ago
Predicting Bundles of Spatial Locations from Learning Revealed Preference Data
We propose the problem of predicting a bundle of goods, where the goods considered is a set of spatial locations that an agent wishes to visit. This typically arises in the touris...
Truc Viet Le, Siyuan Liu, Hoong Chuin Lau, Ramayya...