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139
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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
144
Voted
WSC
2000
15 years 5 months ago
Using simulation and critical points to define states in continuous search spaces
Many artificial intelligence techniques rely on the notion ate" as an abstraction of the actual state of the nd an "operator" as an abstraction of the actions that ...
Marc S. Atkin, Paul R. Cohen
156
Voted
SOSP
1997
ACM
15 years 5 months ago
Towards Transparent and Efficient Software Distributed Shared Memory
Despite a large research effort, software distributed shared memory systems have not been widely used to run parallel applications across clusters of computers. The higher perform...
Daniel J. Scales, Kourosh Gharachorloo
111
Voted
AAAI
1994
15 years 5 months ago
Learning to Reason
We introduce a new framework for the study of reasoning. The Learning (in order) to Reason approach developed here views learning as an integral part of the inference process, and ...
Roni Khardon, Dan Roth
FORTE
1994
15 years 5 months ago
An improvement in formal verification
Critical safety and liveness properties of a concurrent system can often be proven with the help of a reachability analysis of a finite state model. This type of analysis is usual...
Gerard J. Holzmann, Doron Peled
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