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AMAI
2004
Springer
15 years 9 months ago
A Framework for Sequential Planning in Multi-Agent Settings
This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state spac...
Piotr J. Gmytrasiewicz, Prashant Doshi
PLDI
2010
ACM
15 years 9 months ago
Green: a framework for supporting energy-conscious programming using controlled approximation
Energy-efficient computing is important in several systems ranging from embedded devices to large scale data centers. Several application domains offer the opportunity to tradeof...
Woongki Baek, Trishul M. Chilimbi
SIGUCCS
2000
ACM
15 years 7 months ago
From VTs to iMacs: Moving Public Computing Access into the 21st Century
In a Collegiate setting, technology must be functional, effective, and highly visible. In the fall of 1999 the CIS department at Bowdoin College saw the need for a greater number ...
Robert Denton, Patrick Jensen
JMLR
2006
132views more  JMLR 2006»
15 years 4 months ago
Learning to Detect and Classify Malicious Executables in the Wild
We describe the use of machine learning and data mining to detect and classify malicious executables as they appear in the wild. We gathered 1,971 benign and 1,651 malicious execu...
Jeremy Z. Kolter, Marcus A. Maloof
NIPS
1998
15 years 5 months ago
Risk Sensitive Reinforcement Learning
In this paper, we consider Markov Decision Processes (MDPs) with error states. Error states are those states entering which is undesirable or dangerous. We define the risk with re...
Ralph Neuneier, Oliver Mihatsch