Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Diego Aineto"'
Autor:
Diego Aineto, Javier Iranzo-Sánchez, Lenin G. Lemus-Zúñiga, Eva Onaindia, Javier F. Urchueguía
Publikováno v:
Energies, Vol 12, Iss 11, p 2082 (2019)
The mainstream of EU policies is heading towards the conversion of the nowadays electricity consumer into the future electricity prosumer (producer and consumer) in markets in which the production of electricity will be more local, renewable and econ
Externí odkaz:
https://doaj.org/article/0d625b45dfbd459abb62d85ea88cc90b
Publikováno v:
Journal of Artificial Intelligence Research. 74:1091-1123
A declarative action model is a compact representation of the state transitions of dynamic systems that generalizes over world objects. The specification of declarative action models is often a complex hand-crafted task. In this paper we formulate de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::58d13e3cf3a256fb1f77cc6dceb8b3bd
https://doi.org/10.1613/jair.1.13073
https://doi.org/10.1613/jair.1.13073
Publikováno v:
Knowledge Engineering Tools and Techniques for AI Planning ISBN: 9783030385606
Knowledge Engineering Tools and Techniques for AI Planning
Knowledge Engineering Tools and Techniques for AI Planning
The paper presents a classical planning compilation for learning STRIPS action models from partial observations of plan executions. The compilation is flexible to different amounts and types of input knowledge, from learning samples that comprise par
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7e03ccb47b391840e12993def85bc0b3
https://doi.org/10.1007/978-3-030-38561-3_1
https://doi.org/10.1007/978-3-030-38561-3_1
Publikováno v:
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname
instname
[EN] This paper presents FAMA, a novel approach for learning STRIPS action models from observations of plan executions that compiles the learning task into a classical planning task. Unlike all existing learning systems, FAMA is able to learn when th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2200fed728682b4984b2a4efa3a56c2d
https://doi.org/10.1016/j.artint.2019.05.003
https://doi.org/10.1016/j.artint.2019.05.003
Autor:
Eva Onaindia, Javier F. Urchueguía, Diego Aineto, Javier Iranzo-Sánchez, Lenin G. Lemus-Zúñiga
Publikováno v:
Energies, Vol 12, Iss 11, p 2082 (2019)
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname
Energies; Volume 12; Issue 11; Pages: 2082
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname
Energies; Volume 12; Issue 11; Pages: 2082
[EN] The mainstream of EU policies is heading towards the conversion of the nowadays electricity consumer into the future electricity prosumer (producer and consumer) in markets in which the production of electricity will be more local, renewable and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::685ade1c2796b49729b3bccb1c2f6fbb
https://www.mdpi.com/1996-1073/12/11/2082
https://www.mdpi.com/1996-1073/12/11/2082
Publikováno v:
Scopus-Elsevier
This paper presents a novel approach for learning STRIPS action models from examples that compiles this inductive learning task into a classical planning task. Interestingly, the compilation approach is flexible to different amounts of available inpu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::789d4c3e8aad06224a6a38ec1981656d
Publikováno v:
Scopus-Elsevier
Given a partially observed plan execution, and a set of possible planning models (models that share the same state variables but different action schemata), model recognition is the task of identifying the model that explains the observation. The pap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8f41cf07a8e639cc8e2953c8e103f3f9
http://www.scopus.com/inward/record.url?eid=2-s2.0-85085595368&partnerID=MN8TOARS
http://www.scopus.com/inward/record.url?eid=2-s2.0-85085595368&partnerID=MN8TOARS
Publikováno v:
Proceedings of the Eighteenth International Conference on Principles of Knowledge Representation and Reasoning
KR
KR
This paper introduces the Temporal Inference Problem (TIP), a general formulation for a family of inference problems that reason about the past, present or future state of some observed agent. A TIP builds on the models of an actor and of an observer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6eaa83d270b4cb2981bbc60e65efc5be