110 search results - page 15 / 22 » Learning Action Strategies for Planning Domains |
ATAL
2010
Springer
13 years 7 months ago
2010
Springer
A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate model of ...
DAGSTUHL
2007
13 years 8 months ago
2007
The ways in which an agent’s actions affect the world can often be modeled compactly using a set of relational probabilistic planning rules. This paper addresses the problem of ...
ICML
2005
IEEE
14 years 7 months ago
2005
IEEE
We study an approach for performing concurrent activities in Markov decision processes (MDPs) based on the coarticulation framework. We assume that the agent has multiple degrees ...
AAAI
2010
13 years 8 months ago
2010
Recent advancements in model-based reinforcement learning have shown that the dynamics of many structured domains (e.g. DBNs) can be learned with tractable sample complexity, desp...
AIPS
2008
13 years 9 months ago
2008
In the planning-as-SAT paradigm there have been numerous recent developments towards improving the speed and scalability of planning at the cost of finding a step-optimal parallel...