AAAI
2008
13 years 9 months ago
2008
We describe a novel integration of Planning with Probabilistic State Estimation and Execution resulting in a unified representational and computational framework based on declarat...
AAAI
2008
13 years 9 months ago
2008
We present the first planner capable of reasoning with both the full semantics of PDDL2.1 (level 3) temporal planning and with numeric resources. Our planner, CRIKEY3, employs heu...
AAAI
2008
13 years 9 months ago
2008
We present a new algorithm for reasoning in the description logic SHIQ, which is the most prominent fragment of the Web Ontology Language OWL. The algorithm is based on ordered bi...
AAAI
2008
13 years 9 months ago
2008
Probabilistic modeling has been a dominant approach in Machine Learning research. As the field evolves, the problems of interest become increasingly challenging and complex. Makin...
AAAI
2008
13 years 9 months ago
2008
This work addresses the problem of efficiently learning action schemas using a bounded number of samples (interactions with the environment). We consider schemas in two languages-...
AAAI
2008
13 years 9 months ago
2008
Agents often have to construct plans that obey resource limits for continuous resources whose consumption can only be characterized by probability distributions. While Markov Deci...
AAAI
2008
13 years 9 months ago
2008
In this paper, we set up a framework to study approximation of manipulation, control, and bribery in elections. We show existence of approximation algorithms (even fully polynomia...
AAAI
2008
13 years 9 months ago
2008
A seed-based framework for textual information extraction allows for weakly supervised acquisition of open-domain class attributes over conceptual hierarchies, from a combination ...
AAAI
2008
13 years 9 months ago
2008
Query translation for Cross-Lingual Information Retrieval (CLIR) has gained increasing attention in the research area. Previous work mainly used machine translation systems, bilin...
AAAI
2008
13 years 9 months ago
2008
Hierarchical state decompositions address the curse-ofdimensionality in Q-learning methods for reinforcement learning (RL) but can suffer from suboptimality. In addressing this, w...