During the planning process, a planner may often have many diierent options for what kind of plan reenement to perform next (for example, what task or goal to work on next, what operator or method to use to achieve the task or goal, or how to resolve a connict or enforce some constraint in the plan). The planner's eeciency depends greatly on how well it chooses among these options. In this pape...

Model adaptation is a process of modifying a model trained with a large amount of training data from the source domain to adapt a speci c similar target domain by using a small amount of adaptation data regarding the target domain. Bayesian hierarchical modeling is well known as a general tool for model adaptation and multi-task learning, and widely used in various areas such as marketing, ecol...

Theoretical results on HTN planning are mostly related to the plan existence problem. In this paper, we study the structure of the generated plans in terms of the language they produce. We show that such languages are always context-sensitive. Furthermore we identify certain subclasses of HTN planning problems which generate either regular or context-free languages. Most importantly we have dis...

This paper describes WORD2HTN, an algorithm for learning hierarchical tasks and goals from plan traces in planning domains. WORD2HTN combines semantic text analysis techniques and subgoal learning in order to generate Hierarchical Task Networks (HTNs). Unlike existing HTN learning algorithms, WORD2HTN learns distributed vector representations that represent the similarities and semantics of the...

One drawback of Hierarchical Task Network (HTN) planning is the difficulty of providing complete domain knowledge, i.e., a complete and correct set of HTN methods for every task. To provide a principled way to overcome this difficulty, we define a simple formalism that extends classical planning to include problem decomposition using methods, and a planning algorithm based on this formalism. In...

This paper describes HICAP, a general-purpose, interactive case-based plan authoring architecture that can be applied to decision support tasks to yield a hierarchical course of action. It integrates a hierarchical task editor with a conversational case-based planner. HICAP maintains both a task hierarchy representing guidelines that constrain the nal plan and the hierarchical social organizati...

This paper proposes a goal recognition and planning algorithm, HTN-GRP-PO, to enable intelligent assistant agents to recognize older adults’ goals and reason about desired further steps. It will be used in a larger system aimed to help older adults with cognitive impairments to accomplish activities of daily living independently. The algorithm addresses issues including partial observability du...

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 knowledge automatically would save time...

We describe in this paper a new method for extracting knowledge on Hierarchical Task-Network (HTN) planning problems for speeding up the search. This knowledge is gathered by propagating properties through an AND/OR tree that represents disjunctively all possible decompositions of an HTN planning problem. We show how to use this knowledge during the search process of our GraphHTN planner, to sp...

In this paper, we introduce an admissible heuristic for hybrid planning with preferences. Hybrid planning is the fusion of hierarchical task network (HTN) planning with partial order causal link (POCL) planning. We consider preferences to be soft goals — facts one would like to see satisfied in a goal state, but which do not have to hold necessarily. Our heuristic estimates the best quality of ...