A review of learning planning action models
نویسندگان
چکیده
منابع مشابه
Autonomous Learning of Action Models for Planning
This paper introduces two new frameworks for learning action models for planning. In the mistake-bounded planning framework, the learner has access to a planner for the given model representation, a simulator, and a planning problem generator, and aims to learn a model with at most a polynomial number of faulty plans. In the planned exploration framework, the learner does not have access to a p...
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We consider the problem of learning action models for planning in two frameworks and present general sufficient conditions for efficient learning. In the mistake-bounded planning framework, the learner has access to a sound and complete planner for the given action model language, a simulator, and a planning problem generator. In the planned exploration framework, the learner has access to a pl...
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In multi-agent planning environments, action models for each agent must be given as input. However, creating such action models by hand is difficult and time-consuming, because it requires formally representing the complex relationships among different objects in the environment. The problem is compounded in multi-agent environments where agents can take more types of actions. In this paper, we...
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This paper reports on experiments where techniques of supervised machine learning are applied to the problem of planning. The input to the learning algorithm is composed of a description of a planning domain, planning problems in this domain, and solutions for them. The output is an eecient algorithm | a strategy | for solving problems in that domain. We test the strategy on an independent set ...
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ژورنال
عنوان ژورنال: The Knowledge Engineering Review
سال: 2018
ISSN: 0269-8889,1469-8005
DOI: 10.1017/s0269888918000188