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NIPS
1993
15 years 2 months ago
Robust Reinforcement Learning in Motion Planning
While exploring to nd better solutions, an agent performing online reinforcement learning (RL) can perform worse than is acceptable. In some cases, exploration might have unsafe, ...
Satinder P. Singh, Andrew G. Barto, Roderic A. Gru...
100
Voted
SBIA
1998
Springer
15 years 5 months ago
Building Object-Agents from a Software Meta-Architecture
Multi-agent systems can be viewed as object-oriented systems in which their entities show an autonomous behavior. If objects could acquire such skill in a flexible way, agents coul...
Analía Amandi, Ana Price
119
Voted
ATAL
2004
Springer
15 years 6 months ago
Resource Allocation in the Grid Using Reinforcement Learning
One of the main challenges in Grid computing is efficient allocation of resources (CPU-hours, network bandwidth, etc.) to the tasks submitted by users. Due to the lack of centrali...
Aram Galstyan, Karl Czajkowski, Kristina Lerman
112
Voted
CSL
1998
Springer
15 years 12 days ago
Evaluating spoken dialogue agents with PARADISE: Two case studies
This paper presents PARADISE PARAdigm for DIalogue System Evaluation, a general framework for evaluating and comparing the performance of spoken dialogue agents. The framework d...
Marilyn A. Walker, Diane J. Litman, Candace A. Kam...
105
Voted
GECCO
2008
Springer
128views Optimization» more  GECCO 2008»
15 years 1 months ago
Multi-agent task allocation: learning when to say no
This paper presents a communication-less multi-agent task allocation procedure that allows agents to use past experience to make non-greedy decisions about task assignments. Exper...
Adam Campbell, Annie S. Wu, Randall Shumaker