The latest world-championship competition for computer bridge programs was the Baron Barclay World Bridge Computer Challenge, hosted in July 1997 by the American Contract Bridge League. As reported in The New York Times and The Washington Post, the competition’s winner was a new version of Great Game Products’ Bridge Baron program. This version, Bridge Baron 8, has since gone on the market; and...

In this paper, we address the problem of generating preferred plans by combining the procedural control knowledge specified by Hierarchical Task Networks (HTNs) with rich qualitative user preferences. The outcome of our work is a language for specifying user preferences, tailored to HTN planning, together with a provably optimal preference-based planner, HTNPLAN, that is implemented as an exten...

Approaches to computer game playing based on (typically α−β) search of the tree of possible move sequences combined with an evaluation function have been successful for many games, notably Chess. For games with large search spaces and complex positions, such as Go, these approaches are less successful and we are led to seek alternative approaches. One such alternative is to model the goals of t...

Hierarchical Task Network (HTN) planning is a popular approach that cuts down on the classical planning search space by relying on a given hierarchical library of domain control knowledge. This provides an intuitive methodology for specifying high-level instructions on how robots and agents should perform tasks, while also giving the planner enough flexibility to choose the lower-level steps an...

Autonomous mobile robots are promising tools for operations in environments that are difficult to access for humans. When these environments are dynamic and non-deterministic, like in collapsed buildings, the robots must coordinate their actions and the use of resources using planning. This paper presents Planner9, a hierarchical task network (htn) planner that runs on groups of miniature mobil...

Detecting interactions and resolving conflicts is one of the key issues for generative planning systems. Hierarchical Task Network (HTN) planning systems use critics for this purpose. Critics have provided extra efficiency and flexibility to HTN planning systems, but their procedural –and sometimes domain-specific – nature has not been amenable to analytical studies. As a result, little work is...

Although HTN planning is in general undecidable, there are many syntactically identifiable sub-classes of HTN problems that can be decided. For these sub-classes, the decision procedures provide upper complexity bounds. Lower bounds were often not investigated in more detail, however. We generalize a propositional HTN formalization to one that is based upon a function-free first-order logic and...

Hierarchical Task-Network (HTN) based planning techniques have been applied to the problem of composing Web Services, especially when described using the OWL-S service ontologies. Many of the existing Web Services are either exclusively information providing or crucially depend on information-providing services. Thus, many interesting service compositions involve collecting information either d...

This paper reports on the development of a planning system for an important industrial planning problem. This planner, which is one of the modules in an integrated design-and-planning system called EDAPS, provides an integrated approach to process planning in both the electronic and mechanical domains, specifically in the manufaeture of microwave transmit-receive O/R) modules. Our planner is ba...

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 estimates of the expected values of the ...