Abstract
Goal recognition is the problem of inferring the goal of an agent, based on its observed actions. An inspiring approach-plan recognition by planning (PRP)-uses off-the-shelf planners to dynamically generate plans for given goals, eliminating the need for the traditional plan library. However, existing PRP formulation is inherently inefficient in online recognition, and cannot be used with motion planners for continuous spaces. In this paper, we utilize a different PRP formulation which allows for online goal recognition, and for application in continuous spaces. We present an online recognition algorithm, where two heuristic decision points may be used to improve run-time significantly over existing work. We specify heuristics for continuous domains, prove guarantees on their use, and empirically evaluate the algorithm over n hundreds of experiments in both a 3D navigational environment and a cooperative robotic team task.
Original language | English |
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Title of host publication | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 |
Editors | Carles Sierra |
Publisher | International Joint Conferences on Artificial Intelligence |
Pages | 4447-4454 |
Number of pages | 8 |
ISBN (Electronic) | 9780999241103 |
DOIs | |
State | Published - 2017 |
Event | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia Duration: 19 Aug 2017 → 25 Aug 2017 |
Publication series
Name | IJCAI International Joint Conference on Artificial Intelligence |
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Volume | 0 |
ISSN (Print) | 1045-0823 |
Conference
Conference | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 |
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Country/Territory | Australia |
City | Melbourne |
Period | 19/08/17 → 25/08/17 |
Bibliographical note
Funding Information:This work is supported by (NSFC.60162001).
Funding
This work is supported by (NSFC.60162001).