scholarly journals SHOP2: An HTN Planning System

2003 ◽  
Vol 20 ◽  
pp. 379-404 ◽  
Author(s):  
D. S. Nau ◽  
T. C. Au ◽  
O. Ilghami ◽  
U. Kuter ◽  
J. W. Murdock ◽  
...  
Keyword(s):  
Planning System ◽  
Htn Planning ◽  
Metric Planning ◽  
International Planning

The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.

scholarly journals A Special Report on 2019 International Planning Competition and a Comprehensive Analysis of Its Results

2020 ◽  
Vol 10 ◽  
Author(s):  
Jiayun Chen ◽  
Jianrong Dai ◽  
Ahmad Nobah ◽  
Sen Bai ◽  
Nan Bi ◽  
...  
Keyword(s):  
Work Experience ◽  
Medical Physics ◽  
Large Population ◽  
Locally Advanced ◽  
Planning System ◽  
Treatment Planning System ◽  
Plan Quality ◽  
Wide Range ◽  
The Mean ◽  
International Planning

PurposeThe aim of this work is to introduce the 2019 International Planning Competition and to analyze its results.Methods and materialsA locally advanced non-small cell lung cancer (LA-NSCLC) case using the simultaneous integrated boost approach was selected. The plan quality was evaluated by using a ranking system in accordance with practice guidelines. Planners used their clinical Treatment Planning System (TPS) to generate the best possible plan along with a survey, designed to obtain medical physics aspects information. We investigated the quality of the large population of plans designed by worldwide planners using different planning and delivery systems. The correlations of plan quality with relevant planner characteristics (work experience, department scale, and competition experience) and with technological parameters (TPS and modality) were examined.ResultsThe number of the qualified plans was 287 with a wide range of scores (38.61–97.99). The scores showed statistically significant differences by the following factors: 1) department scale: the mean score (89.76 ± 8.36) for planners from the departments treating >2,000 patients annually was the highest of all; 2) competition experience: the mean score for the 107 planners with previous competition experience was 88.92 ± 9.59, statistically significantly from first-time participants (p = .001); 3) techniques: the mean scores for planners using VMAT (89.18 ± 6.43) and TOMO (90.62 ± 7.60) were higher than those using IMRT (82.28 ± 12.47), with statistical differences (p <.001). The plan scores were negligibly correlated with the planner’s years of work experience or the type of TPS used. Regression analysis demonstrated that plan score was associated with dosimetric objectives that were difficult to achieve, which is generally consistent with a clinical practice evaluation. However, 51.2% of the planners abandoned the difficult component of total lung receiving a dose of 5 Gy in their plan design to achieve the optimal plan.ConclusionThe 2019 international planning competition was carried out successfully, and its results were analyzed. Plan quality was not correlated with work experiences or the TPS used, but it was correlated with department scale, modality, and competition experience. These findings differed from those reported in previous studies.

scholarly journals TALplanner in IPC-2002: Extensions and Control Rules

2003 ◽  
Vol 20 ◽  
pp. 343-377 ◽  
Author(s):  
J. Kvarnström ◽  
M. Magnusson
Keyword(s):  
Temporal Logic ◽  
Domain Knowledge ◽  
Search Space ◽  
Planning System ◽  
Short Introduction ◽  
The Third ◽  
Control Rules ◽  
Benchmark Tests ◽  
International Planning ◽  
And Control

TALplanner is a forward-chaining planner that relies on domain knowledge in the shape of temporal logic formulas in order to prune irrelevant parts of the search space. TALplanner recently participated in the third International Planning Competition, which had a clear emphasis on increasing the complexity of the problem domains being used as benchmark tests and the expressivity required to represent these domains in a planning system. Like many other planners, TALplanner had support for some but not all aspects of this increase in expressivity, and a number of changes to the planner were required. After a short introduction to TALplanner, this article describes some of the changes that were made before and during the competition. We also describe the process of introducing suitable domain knowledge for several of the competition domains.

scholarly journals Grounding of HTN Planning Domain

2017 ◽  
Vol 26 (05) ◽  
pp. 1760021 ◽  
Author(s):  
Abdeldjalil Ramoul ◽  
Damien Pellier ◽  
Humbert Fiorino ◽  
Sylvie Pesty
Keyword(s):  
Expert Knowledge ◽  
State Of The Art ◽  
Autonomous Systems ◽  
Planning Problem ◽  
World State ◽  
Hierarchical Task Network ◽  
Htn Planning ◽  
Speed Up ◽  
International Planning ◽  
Planning Problems

Many Artificial Intelligence techniques have been developed for intelligent and autonomous systems to act and make rational decisions based on perceptions of the world state. Among these techniques, HTN (Hierarchical Task Network) planning is one of the most used in practice. HTN planning is based on expressive languages allowing to specify complex expert knowledge for real world domains. At the same time, many preprocessing techniques for classical planning were proposed to speed up the search. One of these technique, named grounding, consists in enumerating and instantiating all the possible actions from the planning problem descriptions. This technique has proven its effectiveness. Therefore, combining the expressiveness of HTN planning with the efficiency of the grounding preprocessing techniques used in classical planning is a very challenging issue. In this paper, we propose a generic algorithm to ground the domain representation for HTN planning. We show experimentally that grounding process improves the performances of state of the art HTN planners on a range of planning problems from the International Planning Competition (IPC).

scholarly journals The Metric-FF Planning System: Translating ``Ignoring Delete Lists'' to Numeric State Variables

2003 ◽  
Vol 20 ◽  
pp. 291-341 ◽  
Author(s):  
J. Hoffmann
Keyword(s):  
Natural Extension ◽  
Planning System ◽  
State Variables ◽  
Heuristic Function ◽  
Planning Task ◽  
Algorithmic Techniques ◽  
International Planning

Planning with numeric state variables has been a challenge for many years, and was a part of the 3rd International Planning Competition (IPC-3). Currently one of the most popular and successful algorithmic techniques in STRIPS planning is to guide search by a heuristic function, where the heuristic is based on relaxing the planning task by ignoring the delete lists of the available actions. We present a natural extension of ``ignoring delete lists'' to numeric state variables, preserving the relevant theoretical properties of the STRIPS relaxation under the condition that the numeric task at hand is ``monotonic''. We then identify a subset of the numeric IPC-3 competition language, ``linear tasks'', where monotonicity can be achieved by pre-processing. Based on that, we extend the algorithms used in the heuristic planning system FF to linear tasks. The resulting system Metric-FF is, according to the IPC-3 results which we discuss, one of the two currently most efficient numeric planners.

scholarly journals An Admissible HTN Planning Heuristic

2017 ◽  
Author(s):  
Pascal Bercher ◽  
Gregor Behnke ◽  
Daniel Höller ◽  
Susanne Biundo
Keyword(s):  
Search Space ◽  
Planning System ◽  
Hierarchical Task Network ◽  
Planning Approach ◽  
Htn Planning ◽  
Efficient Planning ◽  
Being There ◽  
Control Knowledge ◽  
Search Control ◽  
Domain Independent

Hierarchical task network (HTN) planning is well-known for being an efficient planning approach. This is mainly due to the success of the HTN planning system SHOP2. However, its performance depends on hand-designed search control knowledge. At the time being, there are only very few domain-independent heuristics, which are designed for differing hierarchical planning formalisms. Here, we propose an admissible heuristic for standard HTN planning, which allows to find optimal solutions heuristically. It bases upon the so-called task decomposition graph (TDG), a data structure reflecting reachable parts of the task hierarchy. We show (both in theory and empirically) that rebuilding it during planning can improve heuristic accuracy thereby decreasing the explored search space. The evaluation further studies the heuristic both in terms of plan quality and coverage.

scholarly journals The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks

2010 ◽  
Vol 39 ◽  
pp. 127-177 ◽  
Author(s):  
S. Richter ◽  
M. Westphal
Keyword(s):  
Heuristic Search ◽  
Planning System ◽  
Finite Domain ◽  
State Variables ◽  
Forward Search ◽  
Binary State ◽  
Synergy Effects ◽  
Core Feature ◽  
Binary State Variables ◽  
International Planning

LAMA is a classical planning system based on heuristic forward search. Its core feature is the use of a pseudo-heuristic derived from landmarks, propositional formulas that must be true in every solution of a planning task. LAMA builds on the Fast Downward planning system, using finite-domain rather than binary state variables and multi-heuristic search. The latter is employed to combine the landmark heuristic with a variant of the well-known FF heuristic. Both heuristics are cost-sensitive, focusing on high-quality solutions in the case where actions have non-uniform cost. A weighted A* search is used with iteratively decreasing weights, so that the planner continues to search for plans of better quality until the search is terminated. LAMA showed best performance among all planners in the sequential satisficing track of the International Planning Competition 2008. In this paper we present the system in detail and investigate which features of LAMA are crucial for its performance. We present individual results for some of the domains used at the competition, demonstrating good and bad cases for the techniques implemented in LAMA. Overall, we find that using landmarks improves performance, whereas the incorporation of action costs into the heuristic estimators proves not to be beneficial. We show that in some domains a search that ignores cost solves far more problems, raising the question of how to deal with action costs more effectively in the future. The iterated weighted A* search greatly improves results, and shows synergy effects with the use of landmarks.

scholarly journals Optiplan: Unifying IP-based and Graph-based Planning

2005 ◽  
Vol 24 ◽  
pp. 919-931 ◽  
Author(s):  
M.H.L. Van den Briel ◽  
S. Kambhampati
Keyword(s):  
Integer Programming ◽  
Planning System ◽  
Programming Formulation ◽  
Integer Programming Formulation ◽  
Recent Developments ◽  
International Planning

The Optiplan planning system is the first integer programming-based planner that successfully participated in the international planning competition. This engineering note describes the architecture of Optiplan and provides the integer programming formulation that enabled it to perform reasonably well in the competition. We also touch upon some recent developments that make integer programming encodings significantly more competitive.

scholarly journals The IBaCoP Planning System: Instance-Based Configured Portfolios

2016 ◽  
Vol 56 ◽  
pp. 657-691 ◽  
Author(s):  
Isabel Cenamor ◽  
Tomás De la Rosa ◽  
Fernando Fernández
Keyword(s):  
Predictive Models ◽  
Planning System ◽  
Pareto Dominance ◽  
Portfolio Strategies ◽  
Main Challenge ◽  
Evaluation Phase ◽  
Planning Task ◽  
International Planning ◽  
The Given ◽  
Filtering Approach

Sequential planning portfolios are very powerful in exploiting the complementary strength of different automated planners. The main challenge of a portfolio planner is to define which base planners to run, to assign the running time for each planner and to decide in what order they should be carried out to optimize a planning metric. Portfolio configurations are usually derived empirically from training benchmarks and remain fixed for an evaluation phase. In this work, we create a per-instance configurable portfolio, which is able to adapt itself to every planning task. The proposed system pre-selects a group of candidate planners using a Pareto-dominance filtering approach and then it decides which planners to include and the time assigned according to predictive models. These models estimate whether a base planner will be able to solve the given problem and, if so, how long it will take. We define different portfolio strategies to combine the knowledge generated by the models. The experimental evaluation shows that the resulting portfolios provide an improvement when compared with non-informed strategies. One of the proposed portfolios was the winner of the Sequential Satisficing Track of the International Planning Competition held in 2014.

scholarly journals Lilotane: A Lifted SAT-based Approach to Hierarchical Planning

2021 ◽  
Vol 70 ◽  
pp. 1117-1181
Author(s):  
Dominik Schreiber
Keyword(s):  
Worst Case Analysis ◽  
Propositional Formula ◽  
Layer By Layer ◽  
Worst Case ◽  
Hierarchical Task Network ◽  
Htn Planning ◽  
Preliminary Version ◽  
Sat Encoding ◽  
Task Networks ◽  
International Planning

One of the oldest and most popular approaches to automated planning is to encode the problem at hand into a propositional formula and use a Satisfiability (SAT) solver to find a solution. In all established SAT-based approaches for Hierarchical Task Network (HTN) planning, grounding the problem is necessary and oftentimes introduces a combinatorial blowup in terms of the number of actions and reductions to encode. Our contribution named Lilotane (Lifted Logic for Task Networks) eliminates this issue for Totally Ordered HTN planning by directly encoding the lifted representation of the problem at hand. We lazily instantiate the problem hierarchy layer by layer and use a novel SAT encoding which allows us to defer decisions regarding method arguments to the stage of SAT solving. We show the correctness of our encoding and compare it to the best performing prior SAT encoding in a worst-case analysis. Empirical evaluations confirm that Lilotane outperforms established SAT-based approaches, often by orders of magnitude, produces much smaller formulae on average, and compares favorably to other state-of-the-art HTN planners regarding robustness and plan quality. In the International Planning Competition (IPC) 2020, a preliminary version of Lilotane scored the second place. We expect these considerable improvements to SAT-based HTN planning to open up new perspectives for SAT-based approaches in related problem classes.

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