Showing 921 - 930 of 2040 Items
Statement by Cheryl Lola and Mary Jenkins collected by Rachel George on April 24, 2015
Date: 2015-04-24
Creator: Cheryl Lola
Mary Jenkins
Cheryl Lola and Mary Jenkins
Access: Open access
On influence, stable behavior, and the most influential individuals in networks: A game-theoretic approach
Date: 2014-01-01
Creator: Mohammad T. Irfan
Luis E. Ortiz
Access: Open access
- We introduce a new approach to the study of influence in strategic settings where the action of an individual depends on that of others in a network-structured way. We propose network influence games as a game-theoretic model of the behavior of a large but finite networked population. In particular, we study an instance we call linear-influence games that allows both positive and negative influence factors, permitting reversals in behavioral choices. We embrace pure-strategy Nash equilibrium, an important solution concept in non-cooperative game theory, to formally define the stable outcomes of a network influence game and to predict potential outcomes without explicitly considering intricate dynamics. We address an important problem in network influence, the identification of the most influential individuals, and approach it algorithmically using pure-strategy Nash-equilibria computation. Computationally, we provide (a) complexity characterizations of various problems on linear-influence games; (b) efficient algorithms for several special cases and heuristics for hard cases; and (c) approximation algorithms, with provable guarantees, for the problem of identifying the most influential individuals. Experimentally, we evaluate our approach using both synthetic network influence games and real-world settings of general interest, each corresponding to a separate branch of the U.S. Government. Mathematically, we connect linear-influence games to important models in game theory: potential and polymatrix games. © 2014 Published by Elsevier B.V.
APPSSAT: Approximate probabilistic planning using stochastic satisfiability
Date: 2005-01-01
Creator: Stephen M. Majercik
Access: Open access
- We describe APPSSAT, an approximate probabilistic contingent planner based on ZANDER, a probabilistic contingent planner that operates by converting the planning problem to a stochastic satisfiability (SSAT) problem and solving that problem instead [1]. The values of some of the variables in an SSAT instance are probabilistically determined; APPSSAT considers the most likely instantiations of these variables (the most probable situations facing the agent) and attempts to construct an approximation of the optimal plan that succeeds under those circumstances, improving that plan as time permits. Given more time, less likely instantiations/situations are considered and the plan is revised as necessary. In some cases, a plan constructed to address a relatively low percentage of possible situations will succeed for situations not explicitly considered as well, and may return an optimal or near-optimal plan. This means that APPSSAT can sometimes find optimal plans faster than ZANDER. And the anytime quality of APPSSAT means that suboptimal plans could be efficiently derived in larger time-critical domains in which ZANDER might not have sufficient time to calculate the optimal plan. We describe some preliminary experimental results and suggest further work needed to bring APPSSAT closer to attacking real-world problems. © Springer-Verlag Berlin Heidelberg 2005.