tableware cutlery ai planning 2024

AI PLANNING Miffy Insulated Mug

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Brand from Japan AI PLANNINGColor As Figure Shown Materials NA Size Size14021590mm Care NA

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AI PLANNING Crayon Shin Chan

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AI PLANNING Sumikko Gurashi Wide

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AI PLANNING Crayon Shin Chan

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AI PLANNING Crayon Shin Chan

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AI PLANNING Rilakkuma Jute Handbag

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Brand from Japan AI PLANNING Color As Figure Shown Materials Jute Cotton PP Size Size240 200 150mm Care NA

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AI PLANNING Crayon Shin Chan

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Brand from Japan AI PLANNINGColor As Figure Shown Materials Polyester Size Size330230120mm Care NA

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AI PLANNING Sumikko Gurashi Die

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Brand from Japan AI PLANNING Color As Figure Shown Materials PU Size Size155 130 40mm Care NA

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AI PLANNING Crayon Shin Chan

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Brand from Japan AI PLANNINGColor As Figure Shown Materials Polyester Size Size16512060mm Care NA

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AI PLANNING Sumikko Gurashi Die

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Brand from Japan AI PLANNING Color As Figure Shown Materials PU Size Size155 130 40mm Care NA

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AI PLANNING Miffy Travel Wallet

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Seauto Roker Plus AI Driven

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The Roker Plus is a powerful pool vacuum robot that outdoes the competition The Roker Plus is the newest robot on the market that charges quicker and lasts 40 longer then the closes competitor giving you more cleaning power Our flow rate is over 35 higher then our competitors giving you better suction and a faster cleaning pace The Roker Plus is designed for above ground and inground pools up to 753 sq ft and works best on pools with flat bottoms

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AI PLANNING Crayon Shin Chan

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AI PLANNING Sumikko Gurashi Die

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AI PLANNING Crayon Shin Chan

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Brand from Japan AI PLANNINGColor As Figure Shown Materials Polyester Size Size20010065mm Care NA

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Vitalsource Technologies Inc Knowledge

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A digital copy of Knowledge Engineering Tools And Techniques For Ai Planning by Unknown Download is immediately available upon purchase

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EAN: 9783030385613

Still Room for Humans Career

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Optimization Algorithms AI Techniques for

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Planning with Markov Decision Processes

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Markov Decision Processes MDPs are widely popular in Artificial Intelligence for modeling sequential decisionmaking scenarios with probabilistic dynamics They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes MDPs are actively researched in two related subareas of AI probabilistic planning and reinforcement learning Probabilistic planning assumes known models for the agents goals and domain dynamics and focuses on determining how the agent should behave to achieve its objectives On the other hand reinforcement learning additionally learns these models based on the feedback the agent gets from the environment This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems with an emphasis on the algorithmic perspective It covers the whole spectrum of the field from the basics to stateoftheart optimal and approximation algorithms We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them We then discuss modern optimal algorithms based on heuristic search and the use of structured representations A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature These include determinizationbased approaches sampling techniques heuristic functions dimensionality reduction and hierarchical representations Finally we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems Table of Contents Introduction MDPs Fundamental Algorithms Heuristic Search Algorithms Symbolic Algorithms Approximation Algorithms Advanced Notes

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Planning with Markov Decision Processes

Find tips, reviews, and features on Planning with Markov Decision Processes sold for 64.72 $ , available in the Books & Magazines category; this product belongs to and is sold by Abebooks.com.

Markov Decision Processes MDPs are widely popular in Artificial Intelligence for modeling sequential decisionmaking scenarios with probabilistic dynamics They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes MDPs are actively researched in two related subareas of AI probabilistic planning and reinforcement learning Probabilistic planning assumes known models for the agents goals and domain dynamics and focuses on determining how the agent should behave to achieve its objectives On the other hand reinforcement learning additionally learns these models based on the feedback the agent gets from the environment This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems with an emphasis on the algorithmic perspective It covers the whole spectrum of the field from the basics to stateoftheart optimal and approximation algorithms We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them We then discuss modern optimal algorithms based on heuristic search and the use of structured representations A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature These include determinizationbased approaches sampling techniques heuristic functions dimensionality reduction and hierarchical representations Finally we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems Table of Contents Introduction MDPs Fundamental Algorithms Heuristic Search Algorithms Symbolic Algorithms Approximation Algorithms Advanced Notes

Condition: used
Disponibility: in_stock
Shipping: -1




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