Learning Symbolic Models of Stochastic Domains
نویسندگان
چکیده
منابع مشابه
Learning Symbolic Models of Stochastic Domains
In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a probabilistic, relational planning rule representation that compactly models noisy, nondeterministic action effects, and show how such rules can be effectively learned. Through experiments in simple planning domains and a 3D simulated blocks world with realistic physics, we demon...
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Long-living autonomous agents must be able to learn to perform competently in novel environments. One important aspect of competence is the ability to plan, which entails the ability to learn models of the agent’s own actions and their effects on the environment. In this paper we describe an approach to learn action models of environments with continuous-valued spatial states and realistic phys...
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Stochastic process algebras have been introduced in order to enable compositional performance analysis. The size of the state space is a limiting factor, especially if the system consists of many cooperating components. To fight state space explosion, compositional aggregation based on congruence relations can be applied. This paper addresses the computational complexity of minimisation algorit...
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Probabilistic planning offers a powerful framework for general problem solving. Historically, probabilistic planning algorithms have contributed to a variety of critical application areas and technologies, including conservation biology [42], self-driving cars [48, 37], and space exploration [14, 6, 20]. However, optimal planning is known to be P-Complete with respect to the size of the state-a...
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ژورنال
عنوان ژورنال: Journal of Artificial Intelligence Research
سال: 2007
ISSN: 1076-9757
DOI: 10.1613/jair.2113