This paper is focused on how a general-purpose hierarchical planning representation, based on the HTN paradigm, can be used to support the representation of oncology treatment protocols. The planning algorithm used is a temporally extended HTN planning process capable of interpreting such representation and generating oncology treatment plans that have been proven to support clinical decisions ...

Dept of Population Health, Division of Health & Behavior and Center for Healthful Behavior Change, New York University Langone Medical Center, New York, NY; Clinical Directors Network (CDN), New York, NY; Dept of Epidemiology and Population Health, Albert Einstein College of Medicine of Yeshiva University, Bronx, NY; Center for Clinical and Translational Science, The Rockefeller University, New...

In classical planning it is easy to verify if a given sequence of actions is a solution to a planning problem. It has to be checked whether the actions are applicable in the given order and if a goal state is reached after executing them. In this paper we show that verifying whether a plan is a solution to an HTN planning problem is much harder. More specifically, we prove that this problem is ...

We present here an overview of several planning techniques in robotics. We will not be concerned with the synthesis of abstract mission and task plans, using well known classical and other domain-independent planning techniques. We will mainly focus on to how refine such abstract plans into robust sensory-motor actions and on some planning techniques that can be useful for that. The paper intro...

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...

Problem speciications for classical planners based on a STRIPS-like representation typically consist of an initial situation and a partially deened goal state. Hierarchical planning approaches, e.g., Hierarchical Task Network (HTN) Planning, have not only richer representations for actions but also for the representation of planning problems. The latter are deened by giving an initial state and...

This paper presents a framework for cost-optimal HTN planning. The framework includes an optimal algorithm combining a branch-and-bound with a heuristic search, which can also be used as a near-optimal algorithm given a time limit. It also includes different heuristics based on weighted cost estimations and different decomposition strategies. The different elements from this framework are empir...

Hierarchical task networks provide an efficient way to encode user prescriptions about what constitute good plans using component methods. However, manual construction of these methods is complex and time consuming. In this paper, we propose a novel approach to learning probabilistic hierarchical task networks that capture the user preference by examining user-produced plans given no prior info...

This paper describes HICAP (Hierarchical Interactive Case-based Architecture for Planning), a general purpose planning architecture that we have developed and applied to assist military commanders and their staa with planning NEOs (Noncom-batant Evacuation Operations). HICAP integrates a hierarchical task editor, HTE, with a conversational case-based planning tool, NaCoDAE/HTN. In this applicat...

Hierarchical Task Network (HTN) planning is a formalism that can express constraints which cannot easily be expressed by classical (non-hierarchical) planning approaches. It enables reasoning about procedural structures and domainspecific search control knowledge. Yet the cornucopia of modern heuristic search techniques remains largely unincorporated in current HTN planners, in part because it ...