2008 International Planning Competition

Learning Track

Overview

The International Planning Competition (IPC) is a biennial event organized in the context of the International Conference on Automated Planning and Scheduling (ICAPS). This year we are pleased to introduce a new learning track of the competition to complement the existing deterministic and probabilistic tracks.

Previous planning competitions have all been structured around a number of planning domains such as Logistics, Freecell, and Airport, with each planner being asked to solve a sequence of problems from each domain. Previous (non-learning) planners in the competitions do not attempt to leverage this structure, for example, by trying to learn from previous problems in a domain to solve future problems in the domain more effectively. The motivation for the learning track is to encourage work on planning systems that do have such learning capabilities.


Workshop Co-chairs
Important Information

Structure of Learning Track

The general structure of the learning track will be as follows. Competitors will submit two programs to the organizers before the competition begins: a learner and a planner. The competition will then be run in two stages. First, there will be a learning stage, where the learning programs will be provided with the domain definition and example problems for each domain that will appear in the competition. For each domain, the learning program will be given a certain amount of learning time, after which it must output a domain-specific control-knowledge file. Second, there will be an evaluation stage, where, for each domain, the planner will be provided with the appropriate domain-specific control-knowledge file and asked to solve a sequence of test problems from that domain.

The organizers are not placing any constraints on what style of learning approach might be used. For example, a system might utilize statistical/inductive learning or purely deductive learning techniques. In addition, the learning track provides a good venue for entering approaches that might not traditionally be viewed as learning, such as pure domain-analysis. For example, domain analysis could be conducted during the learning period and the resulting knowledge used during the evaluation period. Ultimately, we hope to see a wide variety of approaches, that will help answer the following questions. How can a planner best use a learning, or domain analysis, period in order to improve future performance?



Preliminary Schedule (subject to change)

Related Events

School of Electrical Engineering and Computer Science, 1148 Kelley Engineering Center
Oregon State University, Corvallis, OR 97331-5501
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