Shih-Fen Cheng

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In a symmetric game, every player is identical with respect to the game rules. We show that a symmetric 2strategy game must have a pure-strategy Nash equilibrium. We also discuss Nash’s original paper and its generalized notion of symmetry in games. As a special case of Nash’s theorem, any finite symmetric game has a symmetric Nash equilibrium. Furthermore,(More)
Since the year 2000, the annual trading agent competition has provided a forum for designers to evaluate programmed trading techniques in a challenging market scenario in competition with other design groups. After three years of apparent progress, we attempt to evaluate the trading competence of competition participants, in the 2002 tournament and over(More)
To deal with exponential growth in the size of a game with the number of agents, we propose an approximation based on a hierarchy of reduced games. The reduced game achieves savings by restricting the number of agents playing any strategy to fixed multiples. We validate the idea through experiments on randomly generated local-effect games. An extended(More)
A Strategy Generation Engine is a system that reads a description of a game or market mechanism and outputs strategies for participants. Ideally, this means a game solver—an algorithm to compute Nash equilibria. This is a well-studied problem and very general solutions exist, but they can only be applied to small, finite games. This thesis presents methods(More)
Multi-agent planning is a well-studied problem with various applications including disaster rescue, urban transportation and logistics, both for autonomous agents and for decision support to humans. Due to computational constraints, existing research typically focuses on one of two scenarios: unstructured domains with many agents where we are content with(More)
In this work, we investigate the problem of largescale mobile crowdsourcing, where workers are financially motivated to perform location-based tasks physically. Unlike current industry practice that relies on workers to manually pick tasks to perform, we automatically make task recommendation based on workers’ historical trajectories and desired time(More)
We investigate the problem of large-scale mobile crowdtasking, where a large pool of citizen crowd-workers are used to perform a variety of location-specific urban logistics tasks. Current approaches to such mobile crowd-tasking are very decentralized: a crowd-tasking platform usually provides each worker a set of available tasks close to the worker’s(More)
Mobile crowd-tasking markets are growing at an unprecedented rate with increasing number of smartphone users. Such platforms differ from their online counterparts in that they demand physical mobility and can benefit from smartphone processors and sensors for verification purposes. Despite the importance of such mobile crowd-tasking markets, little is known(More)
The problem of finding efficient coordinated signal timing plans for a large number of traffic signals is a challenging problem because of the exponential growth in the number of joint timing plans that need to be explored as the network size grows. In this paper, we employ the game-theoretic paradigm of fictitious play to iteratively converge to a locally(More)