A Concise Introduction to Models and Methods for Automated PlanningDiscussion
A Concise Introduction to Models and Methods for Automated Planning: Discussion
Geffner, Hector; Bonet, Blai
2013-01-01 00:00:00
[The selection of the action to do next is one of the central problems faced by autonomous agents. As discussed in Chapter 1, the problem is normally addressed in three different ways: in the hardwired approach, the control is set by nature or by a programmer, in the learning-based approach, the control is learned by trial-and-error, in the model-based approach, the control is derived from a model of the actions, sensors, and goals. Planning is the model-based approach to autonomous behavior, and in this book we have considered the main planning models and methods. In this last chapter, we list some challenges in current planning research, and discuss briefly how the work in scalable computational models of planning can contribute to the understanding of one of the most unique human features, namely, the ability to plan, often in the context of other agents that have goals and make plans too.]
http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.pnghttp://www.deepdyve.com/lp/springer-journals/a-concise-introduction-to-models-and-methods-for-automated-planning-M8yTGN0F30
A Concise Introduction to Models and Methods for Automated PlanningDiscussion
[The selection of the action to do next is one of the central problems faced by autonomous agents. As discussed in Chapter 1, the problem is normally addressed in three different ways: in the hardwired approach, the control is set by nature or by a programmer, in the learning-based approach, the control is learned by trial-and-error, in the model-based approach, the control is derived from a model of the actions, sensors, and goals. Planning is the model-based approach to autonomous behavior, and in this book we have considered the main planning models and methods. In this last chapter, we list some challenges in current planning research, and discuss briefly how the work in scalable computational models of planning can contribute to the understanding of one of the most unique human features, namely, the ability to plan, often in the context of other agents that have goals and make plans too.]
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