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Author: Nir Lipovetzky Publisher: Lulu.com ISBN: 1312466219 Category : Computers Languages : en Pages : 180
Book Description
Classical planning is the problem of finding a sequence of actions for achieving a goal from an initial state assuming that actions have deterministic effects. The most effective approach for finding such plans is based on heuristic search guided by heuristics extracted automatically from the problem representation. In this thesis, we introduce alternative approaches for performing inference over the structure of planning problems that do not appeal to heuristic functions, nor to reductions to other formalisms such as SAT or CSP. We show that many of the standard benchmark domains can be solved with almost no search or a polynomially bounded amount of search, once the structure of planning problems is taken into account. In certain cases we can characterize this structure in terms of a novel width parameter for classical planning.
Author: Nir Lipovetzky Publisher: Lulu.com ISBN: 1312466219 Category : Computers Languages : en Pages : 180
Book Description
Classical planning is the problem of finding a sequence of actions for achieving a goal from an initial state assuming that actions have deterministic effects. The most effective approach for finding such plans is based on heuristic search guided by heuristics extracted automatically from the problem representation. In this thesis, we introduce alternative approaches for performing inference over the structure of planning problems that do not appeal to heuristic functions, nor to reductions to other formalisms such as SAT or CSP. We show that many of the standard benchmark domains can be solved with almost no search or a polynomially bounded amount of search, once the structure of planning problems is taken into account. In certain cases we can characterize this structure in terms of a novel width parameter for classical planning.
Author: Michael P. Georgeff Publisher: Elsevier ISBN: 0323141722 Category : Computers Languages : en Pages : 432
Book Description
Reasoning About Actions and Plans discusses approaches to a number of the more challenging problems in reasoning about the future and forming plans of action to achieve their goals. Reasoning about actions and plans can be seen as fundamental to the development of intelligent machines that are capable of dealing effectively with real-world problems. This book comprises 17 chapters, with the first delving into the semantics of STRIPS. The following chapters then discuss a theory of plans; formulating multiagent, dynamic-world problems in the classical planning framework; and a representation of parallel activity based on events, structure, and causality. Other chapters cover branching regular expressions and multi-agent plans; a representation of action and belief for automatic planning systems; possible worlds planning; and intractability and time-dependent planning. The remaining chapters discuss goal structure, holding periods and "clouds"; a model of plan inference that distinguishes between the beliefs of actors and observers; persistence, intention, and commitment; the context-sensitivity of belief and desire; the doxastic theory of intention; an architecture for intelligent reactive systems; and abstract reasoning as emergent from concrete activity. This book will be of interest to practitioners in the fields of cognition and artificial intelligence.
Author: Hector Radanovic Publisher: Springer Nature ISBN: 3031015649 Category : Computers Languages : en Pages : 132
Book Description
Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials. The target audience of the book are students and researchers interested in autonomous behavior and planning from an AI, engineering, or cognitive science perspective. Table of Contents: Preface / Planning and Autonomous Behavior / Classical Planning: Full Information and Deterministic Actions / Classical Planning: Variations and Extensions / Beyond Classical Planning: Transformations / Planning with Sensing: Logical Models / MDP Planning: Stochastic Actions and Full Feedback / POMDP Planning: Stochastic Actions and Partial Feedback / Discussion / Bibliography / Author's Biography
Author: David J. C. MacKay Publisher: Cambridge University Press ISBN: 9780521642989 Category : Computers Languages : en Pages : 694
Book Description
Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.
Author: Steven Minton Publisher: Morgan Kaufmann ISBN: 1483221172 Category : Social Science Languages : en Pages : 555
Book Description
Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning. Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credit assignment and describe tractable classes of problems for which optimal plans can be derived. This book discusses as well how reactive, integrated systems give rise to new requirements and opportunities for machine learning. The final chapter deals with a method for learning problem decompositions, which is based on an idealized model of efficiency for problem-reduction search. This book is a valuable resource for production managers, planners, scientists, and research workers.
Author: Ron van der Meyden Publisher: Springer Science & Business Media ISBN: 3642206735 Category : Computers Languages : en Pages : 139
Book Description
This book presents revised versions of selected papers from the 6th Workshop on Model Checking and Artificial Intelligence, MoChArt 2010, held in Atlanta, GA, USA in July 2010, as well as papers contributed subsequent to the workshop. The 7 papers presented were carefully reviewed and selected for inclusion in this book. In addition, the book also contains an extended abstract of the invited talk held at the workshop. The topics covered by these papers are general search algorithms, application of AI techniques to automated program verification, multiagent systems and epistemic logic, abstraction, epistemic model checking, and theory of model checking.
Author: Patrik Haslum Publisher: Morgan & Claypool Publishers ISBN: 1627057374 Category : Computers Languages : en Pages : 189
Book Description
Planning is the branch of Artificial Intelligence (AI) that seeks to automate reasoning about plans, most importantly the reasoning that goes into formulating a plan to achieve a given goal in a given situation. AI planning is model-based: a planning system takes as input a description (or model) of the initial situation, the actions available to change it, and the goal condition to output a plan composed of those actions that will accomplish the goal when executed from the initial situation. The Planning Domain Definition Language (PDDL) is a formal knowledge representation language designed to express planning models. Developed by the planning research community as a means of facilitating systems comparison, it has become a de-facto standard input language of many planning systems, although it is not the only modelling language for planning. Several variants of PDDL have emerged that capture planning problems of different natures and complexities, with a focus on deterministic problems. The purpose of this book is two-fold. First, we present a unified and current account of PDDL, covering the subsets of PDDL that express discrete, numeric, temporal, and hybrid planning. Second, we want to introduce readers to the art of modelling planning problems in this language, through educational examples that demonstrate how PDDL is used to model realistic planning problems. The book is intended for advanced students and researchers in AI who want to dive into the mechanics of AI planning, as well as those who want to be able to use AI planning systems without an in-depth explanation of the algorithms and implementation techniques they use.
Author: Eduardo Fermé Publisher: Springer ISBN: 3319115588 Category : Computers Languages : en Pages : 719
Book Description
This book constitutes the proceedings of the 14th European Conference on Logics in Artificial Intelligence, JELIA 2014, held in Funchal, Madeira, Portugal, in September 2014. The 35 full papers and 14 short papers included in this volume were carefully reviewed and selected from 121 submissions. They are organized in topical sections named: description logics; automated reasoning; logics for uncertain reasoning; non-classical logics; answer-set programming; belief revision; dealing with inconsistency in ASP and DL; reason about actions and causality; system descriptions; short system descriptions; and short papers. The book also contains 4 full paper invited talks.
Author: João Gama Publisher: Springer Science & Business Media ISBN: 3642047467 Category : Computers Languages : en Pages : 487
Book Description
This book constitutes the refereed proceedings of the twelfth International Conference, on Discovery Science, DS 2009, held in Porto, Portugal, in October 2009. The 35 revised full papers presented were carefully selected from 92 papers. The scope of the conference includes the development and analysis of methods for automatic scientific knowledge discovery, machine learning, intelligent data analysis, theory of learning, as well as their applications.
Author: Bruno Siciliano Publisher: Springer Science & Business Media ISBN: 354023957X Category : Technology & Engineering Languages : en Pages : 1626
Book Description
With the science of robotics undergoing a major transformation just now, Springer’s new, authoritative handbook on the subject couldn’t have come at a better time. Having broken free from its origins in industry, robotics has been rapidly expanding into the challenging terrain of unstructured environments. Unlike other handbooks that focus on industrial applications, the Springer Handbook of Robotics incorporates these new developments. Just like all Springer Handbooks, it is utterly comprehensive, edited by internationally renowned experts, and replete with contributions from leading researchers from around the world. The handbook is an ideal resource for robotics experts but also for people new to this expanding field.
Author: Nir Lipovetzky
Author: Nir Lipovetzky
Author: Michael P. Georgeff
Author: Hector Radanovic
Author: David J. C. MacKay
Author: Steven Minton
Author: Ron van der Meyden
Author: Patrik Haslum
Author: Eduardo Fermé
Author: João Gama
Author: Bruno Siciliano