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Lessons Learned from Benchmarking in the Automated Planning Community
| Content Provider | Semantic Scholar |
|---|---|
| Author | Helmert, Malte |
| Copyright Year | 2010 |
| Abstract | Automated planning is a classical area of artificial intelligence research that is concerned with the problem of planning a course of action for an agent (or set of agents) acting in a complex environment. In the frequently studied case of classical planning, e. g. in the propositional STRIPS formalism [1], the problem is that of finding a sequence of actions that achieves the goal of a single agent in a deterministic, static, fully observable world, given only a logical description of the initial state, goal, and preconditions and effects of actions. Automated planning is an active research area, represented at general AI conferences like IJCAI, AAAI and ECAI as well as in the annual ICAPS (International Conference on Automated Planning and Scheduling) conference series. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.informatik.uni-freiburg.de/~ki/papers/helmert-ecai2010ws.pdf |
| Alternate Webpage(s) | http://gki.informatik.uni-freiburg.de/papers/helmert-ecai2010ws.pdf |
| Alternate Webpage(s) | http://ai.cs.unibas.ch/papers/helmert-ecai2010ws.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
