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We describe HTN-MAKER, an algorithm for learning hierarchical planning knowledge in the form of decomposition methods for Hierarchical Task Networks (HTNs). HTNMAKER takes as input the initial states from a set of classical planning problems in a planning domain and solutions to those problems, as well as a set of semantically-annotated tasks to be(More)
This paper presents CBRetaliate, an agent that combines Case-Based Reasoning (CBR) and Reinforcement Learning (RL) algorithms. Unlike most previous work where RL is used to improve accuracy in the action selection process, CBRetaliate uses CBR to allow RL to respond more quickly to changing conditions. CBRetaliate combines two key features: it uses a time(More)
To apply hierarchical task network (HTN) planning to real-world planning problems, one needs to encode the HTN schemata and action models beforehand. However, acquiring such domain knowledge is difficult and time-consuming because the HTN domain definition involves a significant knowledge-engineering effort. A system that can learn the HTN planning domain(More)
This paper presents RepairSHOP a system capable of performing plan adaptation and plan repair. RepairSHOP is built on top of the HTN planner SHOP. RepairSHOP has three properties. The first property is its design modularity, which makes it is straightforward to apply the same process discussed in this paper to build plan adaptation capabilities in other HTN(More)
The content of erythrocytes in the intrinsic lymphatics of the kidneys of young adult male Swiss-Albino rats was studied by optical and transmission electron microscopy, in material fixed by vascular perfusion. Erythrocytes were consistently found in lymphatics associated with interlobular, arcuate, interlobar and hilar arteries. They are presumed to have(More)
This paper describes how to learn Hierarchical Task Networks (HTNs) in nondeterministic planning domains, where actions may have multiple possible outcomes. We discuss several desired properties that guarantee that the resulting HTNs will correctly handle the nondeterminism in the domain. We developed a new learning algorithm, called HTN-MAKERND , that(More)
We consider how to learn Hierarchical Task Networks (HTNs) for planning problems in which both the quality of solution plans generated by the HTNs and the speed at which those plans are found is important. We describe an integration of HTN Learning with Reinforcement Learning to both learn methods by analyzing semantic annotations on tasks and to produce(More)
This paper presents a new approach for spatial event prediction that combines a value function approximation algorithm and case-based reasoning predictors. Each of these predictors makes unique contributions to the overall spatial event prediction. The function value approximation prediction is particularly suitable to reasoning with geographical features(More)
AI planning has become more and more important in many real-world domains such as military applications and intelligent scheduling. However, planning systems require complete specifications of domain models, which can be difficult to encode, even for domain experts. Thus, research on effective and efficient methods to construct domain models or(More)