AAAI
2010
13 years 6 months ago
2010
Policy search is a successful approach to reinforcement learning. However, policy improvements often result in the loss of information. Hence, it has been marred by premature conv...
AAAI
2010
13 years 6 months ago
2010
We introduce a new method to find semantic inconsistencies (i.e., concepts with erroneous synonymity) in the Unified Medical Language System (UMLS). The idea is to identify the in...
AAAI
2010
13 years 6 months ago
2010
We study the distributed allocation of tasks to cooperating robots in real time, where each task has to be assigned to exactly one robot so that the sum of the latencies of all ta...
AAAI
2010
13 years 6 months ago
2010
The depth first proof number search (df-pn) is an effective and popular algorithm for solving and-or tree problems by using proof and disproof numbers. This paper presents a simpl...
AAAI
2010
13 years 6 months ago
2010
As users of social networking websites expand their network of friends, they are often flooded with newsfeed posts and status updates, most of which they consider to be understand...
AAAI
2010
13 years 6 months ago
2010
Existing controller-based approaches for centralized and decentralized POMDPs are based on automata with output known as Moore machines. In this paper, we show that several advant...
AAAI
2010
13 years 6 months ago
2010
The AI community has achieved great success in designing high-performance algorithms for hard combinatorial problems, given both considerable domain knowledge and considerable eff...
AAAI
2010
13 years 6 months ago
2010
The results of the latest International Probabilistic Planning Competition (IPPC-2008) indicate that the presence of dead ends, states with no trajectory to the goal, makes MDPs h...
AAAI
2010
13 years 6 months ago
2010
A major challenge in the field of AI is combining symbolic and statistical techniques. My dissertation work aims to bridge this gap in the domain of real-time strategy games.
AAAI
2010
13 years 6 months ago
2010
Multi-label learning deals with data associated with multiple labels simultaneously. Previous work on multi-label learning assumes that for each instance, the "full" lab...