Plan-based control of robotic agents : improving the capabilities of autonomous robots
Robotic agents, such as autonomous office couriers or robot tourguides, must be both reliable and efficient. Thus, they have to flexibly interleave their tasks, exploit opportunities, quickly plan their course of action, and, if necessary, revise their intended activities. This book makes three majo...
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| Главный автор: | |
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| Формат: | Livre numérique |
| Язык: | Anglais |
| Опубликовано: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Серии: | Lecture notes in computer science. Lecture notes in artificial intelligence
2554 |
| Предметы: | |
| Online-ссылка: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Примечание: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Plan-based control of robotic agents, improving the capabilities of autonomous robots, Michael Beetz, Berlin, Springer, 2002, 1 vol. (XI-191 p.), Lecture notes in computer science, 3-540-00335-5 • Plan-Based Control of Robotic Agents, Texte imprimé, 9783662195024 |
| Итог: | Robotic agents, such as autonomous office couriers or robot tourguides, must be both reliable and efficient. Thus, they have to flexibly interleave their tasks, exploit opportunities, quickly plan their course of action, and, if necessary, revise their intended activities. This book makes three major contributions to improving the capabilities of robotic agents: - first, a plan representation method is introduced which allows for specifying flexible and reliable behavior - second, probabilistic hybrid action models are presented as a realistic causal model for predicting the behavior generated by modern concurrent percept-driven robot plans - third, the system XFRMLEARN capable of learning structured symbolic navigation plans is described in detail. |
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| Примечание: | Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| ISBN: | 9783540363811 (PDF) |
| ISSN: | 1611-3349 2945-9141 |
| Доступ: | Accès en ligne pour les établissements français bénéficiaires des licences nationales Accès soumis à abonnement pour tout autre établissement Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |