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» Instance-Based Action Models for Fast Action Planning
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AAAI
1997
15 years 5 months ago
Incremental Methods for Computing Bounds in Partially Observable Markov Decision Processes
Partially observable Markov decision processes (POMDPs) allow one to model complex dynamic decision or control problems that include both action outcome uncertainty and imperfect ...
Milos Hauskrecht
ICCV
2011
IEEE
14 years 4 months ago
Gradient-based learning of higher-order image features
Recent work on unsupervised feature learning has shown that learning on polynomial expansions of input patches, such as on pair-wise products of pixel intensities, can improve the...
Roland Memisevic
ICML
2010
IEEE
15 years 2 months ago
Constructing States for Reinforcement Learning
POMDPs are the models of choice for reinforcement learning (RL) tasks where the environment cannot be observed directly. In many applications we need to learn the POMDP structure ...
M. M. Hassan Mahmud
ATAL
2009
Springer
15 years 11 months ago
Improving adjustable autonomy strategies for time-critical domains
As agents begin to perform complex tasks alongside humans as collaborative teammates, it becomes crucial that the resulting humanmultiagent teams adapt to time-critical domains. I...
Nathan Schurr, Janusz Marecki, Milind Tambe
STORYTELLING
2007
Springer
15 years 10 months ago
Wide Ruled: A Friendly Interface to Author-Goal Based Story Generation
We present Wide Ruled, an authoring tool for the creation of generative stories. It is based on the Universe author-goal-based model of story generation, and extends this model by ...
James Skorupski, Lakshmi Jayapalan, Sheena Marquez...