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The Continuous Double Auction (CDA) is the dominant market institution for real-world trading of equities, commodities, derivatives, etc. We describe a series of laboratory experiments that, for the first time, allow human subjects to interact with software bidding agents in a CDA. Our bidding agents use strategies based on extensions of the(More)
Server farms today consume more than 1.5% of the total electricity in the U.S. at a cost of nearly $4.5 billion. Given the rising cost of energy, many industries are now seeking solutions for how to best make use of their available power. An important question which arises in this context is how to distribute available power among servers in a server farm(More)
How does evolution produce sophisticated emergent computation in systems composed of simple components limited to local interactions? To model such a process, we used a genetic algorithm (GA) to evolve cellular automata to perform a computational task requiring globally-coordinated information processing. On most runs a class of relatively unsophisticated(More)
The goal of autonomic computing is to create computing systems capable of managing themselves to a far greater extent than they do today. This paper presents Unity, a decentralized architecture for autonomic computing based on multiple interacting agents called autonomic elements. We illustrate how the Unity architecture realizes a number of desired(More)
How does an evolutionary process interact with a decentralized, distributed system in order to produce globally coordinated behavior? Using a genetic algorithm (GA) to evolve cellular automata (CAs), we show that the evolution of spontaneous synchronization , one type of emergent coordination, takes advantage of the underlying medium's potential to form(More)
Continuous space word embeddings learned from large, unstructured corpora have been shown to be effective at capturing semantic regularities in language. In this paper we replace LDA's param-eterization of " topics " as categorical distributions over opaque word types with multivariate Gaussian distributions on the embedding space. This encourages the model(More)
Utility functions provide a natural and advantageous framework for achieving self-optimization in distributed au-tonomic computing systems. We present a distributed architecture , implemented in a realistic prototype data center, that demonstrates how utility functions can enable a collection of autonomic elements to continually optimize the use of(More)
Reinforcement Learning (RL) provides a promising new approach to systems performance management that differs radically from standard queuing-theoretic approaches making use of explicit system performance models. In principle, RL can automatically learn high-quality management policies without an explicit performance model or traffic model, and with little(More)