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Author: Robert Henry Klein Publisher: ISBN: Category : Languages : en Pages : 119
Book Description
Autonomous agents are increasingly being called upon to perform challenging tasks in complex settings with little information about underlying environment dynamics. To successfully complete such tasks the agent must learn from its interactions with the environment. Many existing techniques make assumptions about problem structure to remain tractable, such as limiting the class of possible models or specifying a fixed model expressive power. Complicating matters, there are many scenarios where the environment exhibits multiple underlying sets of dynamics; in these cases, most existing approaches assume the number of underlying models is known a priori, or ignore the possibility of multiple models altogether. Bayesian nonparametric (BNP) methods provide the flexibility to solve both of these problems, but have high inference complexity that has limited their adoption. This thesis provides several methods to tractably plan under uncertainty using BNPs. The first is Simultaneous Clustering on Representation Expansion (SCORE) for learning Markov Decision Processes (MDPs) that exhibit an underlying multiple-model structure. SCORE addresses the co-dependence between observation clustering and model expansion. The second contribution provides a realtime, non-myopic, risk-aware planning solution for use in camera surveillance scenarios where the number of underlying target behaviors and their parameterization are unknown. A BNP model is used to capture target behaviors, and a solution that reduces uncertainty only as needed to perform a mission is presented for allocating cameras. The final contribution is a reinforcement learning (RL) framework RLPy, a software package to promote collaboration and speed innovation in the RL community. RLPy provides a library of learning agents, function approximators, and problem domains for performing RL experiments. RLPy also provides a suite of tools that help automate tasks throughout the experiment pipeline, from initial prototyping through hyperparameter optimization, parallelization of large-scale experiments, and final publication-ready plotting.
Author: Robert Henry Klein Publisher: ISBN: Category : Languages : en Pages : 119
Book Description
Autonomous agents are increasingly being called upon to perform challenging tasks in complex settings with little information about underlying environment dynamics. To successfully complete such tasks the agent must learn from its interactions with the environment. Many existing techniques make assumptions about problem structure to remain tractable, such as limiting the class of possible models or specifying a fixed model expressive power. Complicating matters, there are many scenarios where the environment exhibits multiple underlying sets of dynamics; in these cases, most existing approaches assume the number of underlying models is known a priori, or ignore the possibility of multiple models altogether. Bayesian nonparametric (BNP) methods provide the flexibility to solve both of these problems, but have high inference complexity that has limited their adoption. This thesis provides several methods to tractably plan under uncertainty using BNPs. The first is Simultaneous Clustering on Representation Expansion (SCORE) for learning Markov Decision Processes (MDPs) that exhibit an underlying multiple-model structure. SCORE addresses the co-dependence between observation clustering and model expansion. The second contribution provides a realtime, non-myopic, risk-aware planning solution for use in camera surveillance scenarios where the number of underlying target behaviors and their parameterization are unknown. A BNP model is used to capture target behaviors, and a solution that reduces uncertainty only as needed to perform a mission is presented for allocating cameras. The final contribution is a reinforcement learning (RL) framework RLPy, a software package to promote collaboration and speed innovation in the RL community. RLPy provides a library of learning agents, function approximators, and problem domains for performing RL experiments. RLPy also provides a suite of tools that help automate tasks throughout the experiment pipeline, from initial prototyping through hyperparameter optimization, parallelization of large-scale experiments, and final publication-ready plotting.
Author: Trevor David Jan Campbell Publisher: ISBN: Category : Languages : en Pages : 105
Book Description
Autonomous multiagent systems are beginning to see use in complex, changing environments that cannot be completely specified a priori. In order to be adaptive to these environments and avoid the fragility associated with making too many a priori assumptions, autonomous systems must incorporate some form of learning. However, learning techniques themselves often require structural assumptions to be made about the environment in which a system acts. Bayesian nonparametrics, on the other hand, possess structural flexibility beyond the capabilities of past parametric techniques commonly used in planning systems. This extra flexibility comes at the cost of increased computational cost, which has prevented the widespread use of Bayesian nonparametrics in realtime autonomous planning systems. This thesis provides a suite of algorithms for tractable, realtime, multiagent planning under uncertainty using Bayesian nonparametrics. The first contribution is a multiagent task allocation framework for tasks specified as Markov decision processes. This framework extends past work in multiagent allocation under uncertainty by allowing exact distribution propagation instead of sampling, and provides an analytic solution time/quality tradeoff for system designers. The second contribution is the Dynamic Means algorithm, a novel clustering method based upon Bayesian nonparametrics for realtime, lifelong learning on batch-sequential data containing temporally evolving clusters. The relationship with previous clustering models yields a modelling scheme that is as fast as typical classical clustering approaches while possessing the flexibility and representational power of Bayesian nonparametrics. The final contribution is Simultaneous Clustering on Representation Expansion (SCORE), which is a tractable model-based reinforcement learning algorithm for multimodel planning problems, and serves as a link between the aforementioned task allocation framework and the Dynamic Means algorithm.
Author: Nils Lid Hjort Publisher: Cambridge University Press ISBN: 1139484605 Category : Mathematics Languages : en Pages : 309
Book Description
Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.
Author: Xuefeng Zhou Publisher: Springer Nature ISBN: 9811562636 Category : Automatic control Languages : en Pages : 149
Book Description
This open access book focuses on robot introspection, which has a direct impact on physical human-robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can effectively be modeled as a parametric hidden Markov model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the hierarchical Dirichlet process (HDP) on the standard HMM parameters, known as the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods. This book is a valuable reference resource for researchers and designers in the field of robot learning and multimodal perception, as well as for senior undergraduate and graduate university students.
Author: Mykel J. Kochenderfer Publisher: MIT Press ISBN: 0262331713 Category : Computers Languages : en Pages : 350
Book Description
An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
Author: Justin C. Sahs Publisher: ISBN: Category : Artificial intelligence Languages : en Pages :
Book Description
Many aspects of modern science, business and engineering have become data-centric, relying on tools from Artificial Intelligence and Machine Learning. Practitioners and researchers in these fields need tools that can incorporate observed data into rich models of uncertainty to make discoveries and predictions. One area of study that provides such models is the field of Bayesian Nonparametrics. This dissertation is focused on furthering the development of this field. After reviewing the relevant background and surveying the field, we consider two areas of structured data: - We first consider relational data that takes the form of a 2-dimensional array--such as social network data. We introduce a novel nonparametric model that takes advantage of a representation theorem about arrays whose column and row order is unimportant. We then develop an inference algorithm for this model and evaluate it experimentally. - Second, we consider the classification of streaming data whose distribution evolves over time. We introduce a novel nonparametric model that finds and exploits a dynamic hierarchical structure underlying the data. We present an algorithm for inference in this model and show experimental results. We then extend our streaming model to handle the emergence of novel and recurrent classes, and evaluate the extended model experimentally.
Author: Publisher: Elsevier ISBN: 0444639705 Category : Technology & Engineering Languages : en Pages : 3064
Book Description
27th European Symposium on Computer Aided Process Engineering, Volume 40 contains the papers presented at the 27th European Society of Computer-Aided Process Engineering (ESCAPE) event held in Barcelona, October 1-5, 2017. It is a valuable resource for chemical engineers, chemical process engineers, researchers in industry and academia, students, and consultants for chemical industries. - Presents findings and discussions from the 27th European Society of Computer-Aided Process Engineering (ESCAPE) event
Author: Peter Müller Publisher: Springer ISBN: 3319189689 Category : Mathematics Languages : en Pages : 203
Book Description
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages.
Author: Hassen Fourati Publisher: CRC Press ISBN: 1482263750 Category : Technology & Engineering Languages : en Pages : 639
Book Description
Multisensor Data Fusion: From Algorithms and Architectural Design to Applications covers the contemporary theory and practice of multisensor data fusion, from fundamental concepts to cutting-edge techniques drawn from a broad array of disciplines. Featuring contributions from the world’s leading data fusion researchers and academicians, this authoritative book: Presents state-of-the-art advances in the design of multisensor data fusion algorithms, addressing issues related to the nature, location, and computational ability of the sensors Describes new materials and achievements in optimal fusion and multisensor filters Discusses the advantages and challenges associated with multisensor data fusion, from extended spatial and temporal coverage to imperfection and diversity in sensor technologies Explores the topology, communication structure, computational resources, fusion level, goals, and optimization of multisensor data fusion system architectures Showcases applications of multisensor data fusion in fields such as medicine, transportation's traffic, defense, and navigation Multisensor Data Fusion: From Algorithms and Architectural Design to Applications is a robust collection of modern multisensor data fusion methodologies. The book instills a deeper understanding of the basics of multisensor data fusion as well as a practical knowledge of the problems that can be faced during its execution.
Author: Robert Henry Klein
Author: Robert Henry Klein
Author: Trevor David Jan Campbell
Author: Nils Lid Hjort
Author: Xuefeng Zhou
Author: Mykel J. Kochenderfer
Author: Justin C. Sahs
Author: 
Author: Peter Müller
Author: Hassen Fourati
Author: Charles Gadd