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Collaborating Authors

 University of Wisconsin Oshkosh


Combining Two Fast-Learning Real-Time Search Algorithms Yields Even Faster Learning

AAAI Conferences

Real-time search methods, such as LRTA*, have been used to solve awide variety of planning problems because they can make decisions fastand still converge to a minimum-cost plan if they solve the sameplanning task repeatedly. In this paper, we perform an empiricalevaluation of two existing variants of LRTA* that were developed tospeed up its convergence, namely HLRTA* and FALCONS. Our experimentalresults demonstrate that these two real-time search methods havecomplementary strengths and can be combined. We call the new real-timesearch method eFALCONS and show that it converges with fewer actionsto a minimum-cost plan than LRTA*, HLRTA*, and FALCONS.


AAAI 2008 Workshop Reports

AI Magazine

AAAI 2008 Workshop Reports


AAAI 2008 Workshop Reports

AI Magazine

AAAI was pleased to present the AAAI-08 Workshop Program, held Sunday and Monday, July 13–14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers; AI Education Workshop Colloquium; Coordination, Organizations, Institutions, and Norms in Agent Systems, Enhanced Messaging; Human Implications of Human-Robot Interaction; Intelligent Techniques for Web Personalization and Recommender Systems; Metareasoning: Thinking about Thinking; Multidisciplinary Workshop on Advances in Preference Handling; Search in Artificial Intelligence and Robotics; Spatial and Temporal Reasoning; Trading Agent Design and Analysis; Transfer Learning for Complex Tasks; What Went Wrong and Why: Lessons from AI Research and Applications; and Wikipedia and Artificial Intelligence: An Evolving Synergy.