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ASPLOS
2010
ACM
15 years 12 months ago
Modeling GPU-CPU workloads and systems
Heterogeneous systems, systems with multiple processors tailored for specialized tasks, are challenging programming environments. While it may be possible for domain experts to op...
Andrew Kerr, Gregory F. Diamos, Sudhakar Yalamanch...
247
Voted
AGENTS
2001
Springer
15 years 11 months ago
Hierarchical multi-agent reinforcement learning
In this paper, we investigate the use of hierarchical reinforcement learning (HRL) to speed up the acquisition of cooperative multi-agent tasks. We introduce a hierarchical multi-a...
Rajbala Makar, Sridhar Mahadevan, Mohammad Ghavamz...
170
Voted
PODC
1999
ACM
15 years 11 months ago
Optimal, Distributed Decision-Making: The Case of no Communication
We present a combinatorial framework for the study of a natural class of distributed optimization problems that involve decisionmaking by a collection of n distributed agents in th...
Marios Mavronicolas, Paul G. Spirakis
ICCBR
2010
Springer
15 years 10 months ago
Imitating Inscrutable Enemies: Learning from Stochastic Policy Observation, Retrieval and Reuse
In this paper we study the topic of CBR systems learning from observations in which those observations can be represented as stochastic policies. We describe a general framework wh...
Kellen Gillespie, Justin Karneeb, Stephen Lee-Urba...
CF
2006
ACM
15 years 10 months ago
Performance characteristics of an adaptive mesh refinement calculation on scalar and vector platforms
Adaptive mesh refinement (AMR) is a powerful technique that reduces the resources necessary to solve otherwise intractable problems in computational science. The AMR strategy solv...
Michael L. Welcome, Charles A. Rendleman, Leonid O...