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» Learning Action Selection Network of Intelligent Agent
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103
Voted
ICML
2003
IEEE
16 years 2 months ago
Q-Decomposition for Reinforcement Learning Agents
The paper explores a very simple agent design method called Q-decomposition, wherein a complex agent is built from simpler subagents. Each subagent has its own reward function and...
Stuart J. Russell, Andrew Zimdars
112
Voted
AAAI
2000
15 years 3 months ago
Collective Intelligence and Braess' Paradox
We consider the use of multi-agent systems to control network routing. Conventional approaches to this task are based on Ideal Shortest Path routing Algorithm (ISPA), under which ...
Kagan Tumer, David Wolpert
AIPS
1996
15 years 3 months ago
Least-Commitment Action Selection
The principle of least commitment was embraced early in planning research. Hierarchical task networks (HTNs)reason about high-level tasks without committing to specific low-level ...
Marc Friedman, Daniel S. Weld
ATAL
2008
Springer
15 years 3 months ago
Modeling parallel and reactive empathy in virtual agents: an inductive approach
Humans continuously assess one another's situational context, modify their own affective state, and then respond based on these outcomes through empathetic expression. Virtua...
Scott W. McQuiggan, Jennifer L. Robison, Robert Ph...
ATAL
2005
Springer
15 years 7 months ago
Agent-organized networks for dynamic team formation
Many multi-agent systems consist of a complex network of autonomous yet interdependent agents. Examples of such networked multi-agent systems include supply chains and sensor netw...
Matthew E. Gaston, Marie desJardins