Cargando…
The logic of adaptive behavior: knowledge representation and algorithms for adaptive sequential decision making under uncertainty in first-order and relational domains
Markov decision processes have become the de facto standard in modeling and solving sequential decision making problems under uncertainty. This book studies lifting Markov decision processes, reinforcement learning and dynamic programming to the first-order (or, relational) setting.
Autor principal: | Van Otterlo, M |
---|---|
Lenguaje: | eng |
Publicado: |
IOS Press
2009
|
Materias: | |
Acceso en línea: | http://cds.cern.ch/record/1608202 |
Ejemplares similares
-
Knowledge representation: logical, philosophical, and computational foundations
por: Sowa, John F
Publicado: (2000) -
Dynamic parameter adaptation for meta-heuristic optimization algorithms through type-2 fuzzy logic
por: Olivas, Frumen, et al.
Publicado: (2018) -
Self-learning and adaptive algorithms for business applications: a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions
por: Hu, Zhengbing, et al.
Publicado: (2019) -
Sequential learning and decision-making in wireless resource management
por: Zheng, Rong, et al.
Publicado: (2017) -
Theorem proving with first-order predicate logic, 3
por: Humpert, Benedikt
Publicado: (1987)