Decision making under uncertainty : theory and applicationSeries: Lincoln Laboratory seriesPublisher: Massachusetts, U.S. The MIT Press 2015Description: 323 p. 24 cmISBN: 978-0-262-02925-4Subject(s): SISTEMAS INTELIGENTES DE CONTROL | MAQUINARIA AUTOMÁTICA | TOMA DE DECISIONESDDC classification: 003.56 K63d
|Item type||Current location||Call number||Status||Date due||Barcode|
|Libro Impreso||Biblioteca Lead University||003.56 K63d (Browse shelf)||Available||1460|
|Libro Impreso||Biblioteca Lead University||003.56 K63d (Browse shelf)||Available|
Cita APA: Kochenderfer, M.J. (2015). Decision making under uncertainty. Theory and application. Massachusetts, U.S.: Massachusetts Institute of Technology-MIT.
Contents: Decision making. Probabilistic models: Inference, parameter learning, structure learning. Decision problems: utility theory, Decision Networks, Games. Sequential problems: Formulation. Dynamic programming. Structured and linear representations. Approximate dynamic programming. Online methods. Direct policy search. Model uncertainty: Exploration and exploitation. Maximum likelihood-Bayesian model-based methods. Model-free methods. Generalization. State uncertainty. Formulation. Belief updating. Exact solution methods. Offline methods. Online methods. Cooperative decision making: formulation. Properties. Notable subclasses. Exact solution methods. Approximate solution methods. Application: Probabilistic surveillance video search. Attribute-based person search. Probabilistic appearance model. Learning and inference techniques. Performance. Interactive search tool. Dynamic models for speech applications: Modeling speech signals. Speech recognition. Topic identification. Language recognition. Speaker identification. Machine translation. Optimized airborne collision avoidance: Airborne collision avoidance systems. Collision avoidance problem formulation. State estimation. Real-Time execution. Evaluation. Multiagent planning for persistent surveillance: Mission description. Centralized problem formulation. Decentralized approximate formulations. Model learning. Integrating automation with humans: Human capabilities and coping. Considering the human in design. A Systems view of implementation.