The International Conference on
Automated Planning and Scheduling
Providence, Rhode Island, USA, September 22 - 26, 2007
W1. AI Planning and Learning
PrefaceGreat strides have been made in automated AI Planning in recent years, including very efficient planning techniques that use controlled search with domain-specific and/or domain-independent heuristics, constraint-satisfaction techniques for reasoning with time and resources, and model-checking based planning algorithms. The effectiveness of these techniques has been demonstrated in the past International Planning Competitions, and to some extent, in real-world applications such as navigation tasks, space operations, railway control, and rescue-evacuation tasks.
One challenge for most planning systems is that they require a domain expert to provide some sort of planning knowledge to the system. In many realistic planning problems, however, such planning knowledge may not be completely available; this is partly because it is very hard to compile such knowledge due to the complexities in the domains, e.g., evacuation and rescue operations, and it is partly because there is no expert to provide it, e.g., space operations. In these complex domains, a planning system that can learn such knowledge to develop ways on how to operate in the world holds great promise to be successful.
There has always been an interest in research on developing new techniques in the intersection of AI Planning and Learning. Recently, the two communities have started to move towards each other in several venues. The biggest evidence for this trend is the recent projects initiated by the major funding agencies throughout the world; e.g., the DARPA Grand Challenge, the Integrated Learning and Transfer Learning programs at DARPA, and "Dynamic Planning, Optimization, and Learning" (DPOL) project at NICTA, Australia. All of these programs can benefit greatly from research that integrates planning and learning, and to some extent have generated research in this direction.
The aim of this workshop is to bring researchers together from the AI planning and machine learning communities. The tentative topics that will be covered in the workshop include, but are not limited to:
Ome © Marjorie Mikasen 2005