Download or read book Learning Control Knowledge for Planning written by Yi-Cheng Huang and published by . This book was released on 2003 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Learning Search Control Knowledge to Improve Plan Quality written by M. Alicia Pérez and published by . This book was released on 1995 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Generating good, production-quality plans is an essential element in transforming planners from research tools into real- world applications, but one that has been frequently overlooked in research on machine learning for planning. Most work has aimed at improving the efficiency of planning ('speed-up learning') or at acquiring or refining the planner's action model. This thesis focuses on learning search-control knowledge to improve the quality of the plans produced by the planner. Knowledge about plan quality in a domain comes in two forms: (a) a post- facto quality metric that computes the quality (e.g. execution cost) of a plan, and (b) planning-time decision-control knowledge used to guide the planner towards high-quality plans. The first kind is not operational until after a plan is produced, but is exactly the kind typically available, in contrast to the far more complex operational decision-time knowledge. Learning operational quality control knowledge can be seen as translating the domain knowledge and quality metrics into runtime decision guidance. The full automation of this mapping based on planning experience is the ultimate objective of this thesis. Given a domain theory, a domain-specific metric of plan quality, and problems which provide planning experience, the Quality architecture developed in this thesis automatically acquires operational control knowledge that effectively improves the quality of the plans generated. Quality can (optionally) learn from human experts who suggest improvements to the plans at the operator (plan step) level. We have designed two distinct domain- independent learning mechanisms to efficiently acquire quality control knowledge. They differ in the language used to represent the learned knowledge, namely control rules and control knowledge trees, and in the kinds of quality metrics for which they are best suited. Quality is fully implemented on top of the Prodigy4.0 nonlinear planner. Its empirical evaluation has shown that the learned knowledge produces near-optimal plans (reducing before-learning plan execution costs 8% to 96%). Although the learning mechanisms and learned knowledge representations have been developed for Prodigy4.0, the framework is general and addresses a problem that must be confronted by any planner that treats planning as a constructive decision-making process."
Download or read book Machine Learning Methods for Planning written by Steven Minton and published by Morgan Kaufmann. This book was released on 2014-05-12 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Knowledge and Regularity in Planning written by and published by . This book was released on 1992 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Learning Search Control Knowledge written by Steven Minton and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ability to learn from experience is a fundamental requirement for intelligence. One of the most basic characteristics of human intelligence is that people can learn from problem solving, so that they become more adept at solving problems in a given domain as they gain experience. This book investigates how computers may be programmed so that they too can learn from experience. Specifically, the aim is to take a very general, but inefficient, problem solving system and train it on a set of problems from a given domain, so that it can transform itself into a specialized, efficient problem solver for that domain. on a knowledge-intensive Recently there has been considerable progress made learning approach, explanation-based learning (EBL), that brings us closer to this possibility. As demonstrated in this book, EBL can be used to analyze a problem solving episode in order to acquire control knowledge. Control knowledge guides the problem solver's search by indicating the best alternatives to pursue at each choice point. An EBL system can produce domain specific control knowledge by explaining why the choices made during a problem solving episode were, or were not, appropriate.
Download or read book Intelligent Techniques for Planning written by Ioannis Vlahavas and published by IGI Global. This book was released on 2005-01-01 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Intelligent Techniques for Planning presents a number of modern approaches to the area of automated planning. These approaches combine methods from classical planning such as the construction of graphs and the use of domain-independent heuristics with techniques from other areas of artificial intelligence. This book discuses, in detail, a number of state-of-the-art planning systems that utilize constraint satisfaction techniques in order to deal with time and resources, machine learning in order to utilize experience drawn from past runs, methods from knowledge systems for more expressive representation of knowledge and ideas from other areas such as Intelligent Agents. Apart from the thorough analysis and implementation details, each chapter of the book also provides extensive background information about its subject and presents and comments on similar approaches done in the past.
Download or read book ARPA Rome Laboratory Knowledge based Planning and Scheduling Initiative Workshop Proceedings Tuscon Arizona February 21 24 1994 written by and published by Morgan Kaufmann. This book was released on 1994 with total page 558 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Learning for Adaptive and Reactive Robot Control written by Aude Billard and published by MIT Press. This book was released on 2022-02-08 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methods by which robots can learn control laws that enable real-time reactivity using dynamical systems; with applications and exercises. This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans. It introduces a set of control laws that enable reactivity using dynamical systems, a widely used method for solving motion-planning problems in robotics. These control approaches can replan in milliseconds to adapt to new environmental constraints and offer safe and compliant control of forces in contact. The techniques offer theoretical advantages, including convergence to a goal, non-penetration of obstacles, and passivity. The coverage of learning begins with low-level control parameters and progresses to higher-level competencies composed of combinations of skills. Learning for Adaptive and Reactive Robot Control is designed for graduate-level courses in robotics, with chapters that proceed from fundamentals to more advanced content. Techniques covered include learning from demonstration, optimization, and reinforcement learning, and using dynamical systems in learning control laws, trajectory planning, and methods for compliant and force control . Features for teaching in each chapter: applications, which range from arm manipulators to whole-body control of humanoid robots; pencil-and-paper and programming exercises; lecture videos, slides, and MATLAB code examples available on the author’s website . an eTextbook platform website offering protected material[EPS2] for instructors including solutions.
Download or read book Applications Of Learning And Planning Methods written by Nikolas G Bourbakis and published by World Scientific. This book was released on 1991-03-29 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning and planning are two important topics of artificial intelligence. Learning deals with the algorithmic processes that make a computing machine able to “learn” and improve its performance during the process of complex tasks. Planning on the other hand, deals with decision and construction processes that make a machine capable of constructing an intelligent plan for the solution of a particular complex problem.This book combines both learning and planning methodologies and their applications in different domains. It is divided into two parts. The first part contains seven chapters on the ongoing research work in symbolic and connectionist learning. The second part includes seven chapters which provide the current research efforts in planning methodologies and their application to robotics.
Download or read book IJCAI 97 written by International Joint Conferences on Artificial Intelligence and published by Morgan Kaufmann. This book was released on 1997 with total page 1720 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book LEARNING SEARCH CONTROL KNOWLEDGE FOR PLANNING WITH CONJUNCTIVE GOALS written by KWANG RYEL RYU and published by . This book was released on 1992 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: learning correct rules. The overhead involved in learning is very low because this methodology needs only a small amount of data to learn from, namely, the goal stacks from the leaf nodes of a failure search tree, rather than the whole search tree. Empirical tests show that the rules derived by our system PAL, after sufficient learning, performs as well as, and in some cases better than, those derived by other systems such as PRODIGY/EBL and STATIC.
Download or read book New Directions in AI Planning written by Malik Ghallab and published by . This book was released on 1996 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book ECAI 2014 written by T. Schaub and published by IOS Press. This book was released on 2014-08 with total page 1264 pages. Available in PDF, EPUB and Kindle. Book excerpt: The role of artificial intelligence (AI) applications in fields as diverse as medicine, economics, linguistics, logical analysis and industry continues to grow in scope and importance. AI has become integral to the effective functioning of much of the technical infrastructure we all now take for granted as part of our daily lives. This book presents the papers from the 21st biennial European Conference on Artificial Intelligence, ECAI 2014, held in Prague, Czech Republic, in August 2014. The ECAI conference remains Europe's principal opportunity for researchers and practitioners of Artificial Intelligence to gather and to discuss the latest trends and challenges in all subfields of AI, as well as to demonstrate innovative applications and uses of advanced AI technology. Included here are the 158 long papers and 94 short papers selected for presentation at the conference. Many of the papers cover the fields of knowledge representation, reasoning and logic as well as agent-based and multi-agent systems, machine learning, and data mining. The proceedings of PAIS 2014 and the PAIS System Demonstrations are also included in this volume, which will be of interest to all those wishing to keep abreast of the latest developments in the field of AI.
Download or read book Planning and Learning by Analogical Reasoning written by Manuela M. Veloso and published by Springer Science & Business Media. This book was released on 1994-12-07 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research monograph describes the integration of analogical and case-based reasoning into general problem solving and planning as a method of speedup learning. The method, based on derivational analogy, has been fully implemented in PRODIGY/ANALOGY and proven in practice to be amenable to scaling up, both in terms of domain and problem complexity. In this work, the strategy-level learning process is cast for the first time as the automation of the complete cycle of construction, storing, retrieving, and flexibly reusing problem solving experience. The algorithms involved are presented in detail and numerous examples are given. Thus the book addresses researchers as well as practitioners.
Download or read book Learning Hierarchical Decomposition Rules for Planning written by Chandrasekhara K. Reddy and published by . This book was released on 1998 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) planning techniques have been central to automating a gamut of tasks from the mundane route planning and beer production to the ethereal image processing of space-ship images. Of all the planning techniques, hierarchical-decomposition planning has been the technique most employed in industrial-strength planners. Hierarchical-decomposition planning is performed by recursively decomposing a planning task into its subtasks, until the decomposition results in primitive tasks which can be directly achieved by executing the primitive actions. Hierarchical-decomposition planning is knowledge intensive; it exploits knowledge of the structure and the constraints of a planning domain, to decompose a task into subtasks. Because dependence on human experts for this knowledge leads to knowledge-acquisition bottleneck, machine learning of this domain-specific knowledge becomes important. There exist two opportunities for learning in the context of hierarchical-decomposition planning. One is to learn how a planning task decomposes into subtasks. The other is to learn control knowledge to choose among various decompositions for a task, depending upon situations. In this dissertation, the focus is on the former; more specifically, we focus on learning rules for task or goal decompositions. Goal-decomposition rules (d-rules) decompose goals into a sequence of subgoals under certain conditions. These are a special case of hierarchical task networks (HTNs). The methodology we used for learning d-rules is to map d-rules to Horn clauses, and, thus, transform the problem of learning d-rules to learning Horn clauses. We developed provably correct algorithms for learning Horn clauses. Our algorithms are based on a "generalize-and-test" method, where inductive least-general generalization of positive examples is followed by pruning of irrelevant literals by asking queries or performing self-testing. We implemented systems that are founded in the theoretical algorithms, and tested the applicability of the systems in two planning domains, robot navigation domain and an air-traffic control domain. One of these systems, ExEL, learned from solved problems and expert-answered queries. The other, LeXer, learned from unsolved but ordered problems, or exercises, and self- testing. The applicability of the theoretical algorithms developed for learning Horn clauses, however, transcends the learning of d-rules and even the learning of the more general HTNs.
Download or read book Innovative Approaches to Planning Scheduling and Control written by Katia P. Sycara and published by Morgan Kaufmann. This book was released on 1990 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Inductive Synthesis of Functional Programs written by Ute Schmid and published by Springer Science & Business Media. This book was released on 2003-08-21 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Because of its promise to support human programmers in developing correct and efficient program code and in reasoning about programs, automatic program synthesis has attracted the attention of researchers and professionals since the 1970s. This book focusses on inductive program synthesis, and especially on the induction of recursive functions; it is organized into three parts on planning, inductive program synthesis, and analogical problem solving and learning. Besides methodological issues in inductive program synthesis, emphasis is placed on its applications to control rule learning for planning. Furthermore, relations to problem solving and learning in cognitive psychology are discussed.
