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Automated Planning and Acting
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TLDR
This book presents a comprehensive paradigm of planning and acting using the most recent and advanced automated-planning techniques, and explains the computational deliberation capabilities that allow an actor to reason about its actions, choose them, organize them purposefully, and act deliberately to achieve an objective.Abstract:
Autonomous AI systems need complex computational techniques for planning and performing actions. Planning and acting require significant deliberation because an intelligent system must coordinate and integrate these activities in order to act effectively in the real world. This book presents a comprehensive paradigm of planning and acting using the most recent and advanced automated-planning techniques. It explains the computational deliberation capabilities that allow an actor, whether physical or virtual, to reason about its actions, choose them, organize them purposefully, and act deliberately to achieve an objective. Useful for students, practitioners, and researchers, this book covers state-of-the-art planning techniques, acting techniques, and their integration which will allow readers to design intelligent systems that are able to act effectively in the real world.read more
Citations
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Automated Planning: Theory and Practice.
TL;DR: A translation apparatus is provided which comprises an inputting section for inputting a source document in a natural language and a layout analyzing section for analyzing layout information.
Journal ArticleDOI
Integrated Task and Motion Planning
Caelan Reed Garrett,Rohan Chitnis,Rachel Holladay,Beomjoon Kim,Tom Silver,Leslie Pack Kaelbling,Tomás Lozano-Pérez +6 more
TL;DR: This paper defines a class of TAMP problems and survey algorithms for solving them, characterizing the solution methods in terms of their strategies for solving the continuous-space subproblems and their techniques for integrating the discrete and continuous components of the search.
Journal ArticleDOI
Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda
TL;DR: It is argued that AI can support the derivation of culturally appropriate organizational processes and individual practices to reduce the natural resource and energy intensity of human activities and facilitate and fosters environmental governance.
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Behavior Trees in Robotics and AI: An Introduction
TL;DR: Behavior Trees provide a way to structure the behavior of an artificial agent such as a robot or a non-player character in a computer game.
Posted Content
Rearrangement: A Challenge for Embodied AI.
Dhruv Batra,Angel X. Chang,Sonia Chernova,Andrew J. Davison,Jia Deng,Vladlen Koltun,Sergey Levine,Jitendra Malik,Igor Mordatch,Roozbeh Mottaghi,Manolis Savva,Hao Su +11 more
TL;DR: A framework for research and evaluation in Embodied AI is described, based on a canonical task: Rearrangement, that can focus the development of new techniques and serve as a source of trained models that can be transferred to other settings.
References
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Introduction to Automata Theory, Languages, and Computation
TL;DR: This book is a rigorous exposition of formal languages and models of computation, with an introduction to computational complexity, appropriate for upper-level computer science undergraduates who are comfortable with mathematical arguments.
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Markov Decision Processes: Discrete Stochastic Dynamic Programming
TL;DR: Puterman as discussed by the authors provides a uniquely up-to-date, unified, and rigorous treatment of the theoretical, computational, and applied research on Markov decision process models, focusing primarily on infinite horizon discrete time models and models with discrete time spaces while also examining models with arbitrary state spaces, finite horizon models, and continuous time discrete state models.
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Dynamic Programming and Optimal Control
TL;DR: The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization.
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A Formal Basis for the Heuristic Determination of Minimum Cost Paths
TL;DR: How heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching is described and an optimality property of a class of search strategies is demonstrated.