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 The Complexity of Planning Problems With Simple Causal Graphs

2008 ◽  
Vol 31 ◽  
pp. 319-351 ◽  
Author(s):  
O. Gimenez ◽  
A. Jonsson
Keyword(s):  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Planning Problem ◽  
State Variables ◽  
Plan Generation ◽  
Binary State ◽  
Causal Graphs ◽  
Np Complete ◽  
Binary State Variables ◽  
Planning Problems

We present three new complexity results for classes of planning problems with simple causal graphs. First, we describe a polynomial-time algorithm that uses macros to generate plans for the class 3S of planning problems with binary state variables and acyclic causal graphs. This implies that plan generation may be tractable even when a planning problem has an exponentially long minimal solution. We also prove that the problem of plan existence for planning problems with multi-valued variables and chain causal graphs is NP-hard. Finally, we show that plan existence for planning problems with binary state variables and polytree causal graphs is NP-complete.

scholarly journals Taming Numbers and Durations in the Model Checking Integrated Planning System

2003 ◽  
Vol 20 ◽  
pp. 195-238 ◽  
Author(s):  
S. Edelkamp
Keyword(s):  
Model Checking ◽  
Heuristic Search ◽  
Linear Time ◽  
Critical Path ◽  
General System ◽  
Partial Ordering ◽  
Planning System ◽  
Benchmark Problems ◽  
State Variables ◽  
Integrated Planning

The Model Checking Integrated Planning System (MIPS) is a temporal least commitment heuristic search planner based on a flexible object-oriented workbench architecture. Its design clearly separates explicit and symbolic directed exploration algorithms from the set of on-line and off-line computed estimates and associated data structures. MIPS has shown distinguished performance in the last two international planning competitions. In the last event the description language was extended from pure propositional planning to include numerical state variables, action durations, and plan quality objective functions. Plans were no longer sequences of actions but time-stamped schedules. As a participant of the fully automated track of the competition, MIPS has proven to be a general system; in each track and every benchmark domain it efficiently computed plans of remarkable quality. This article introduces and analyzes the most important algorithmic novelties that were necessary to tackle the new layers of expressiveness in the benchmark problems and to achieve a high level of performance. The extensions include critical path analysis of sequentially generated plans to generate corresponding optimal parallel plans. The linear time algorithm to compute the parallel plan bypasses known NP hardness results for partial ordering by scheduling plans with respect to the set of actions and the imposed precedence relations. The efficiency of this algorithm also allows us to improve the exploration guidance: for each encountered planning state the corresponding approximate sequential plan is scheduled. One major strength of MIPS is its static analysis phase that grounds and simplifies parameterized predicates, functions and operators, that infers knowledge to minimize the state description length, and that detects domain object symmetries. The latter aspect is analyzed in detail. MIPS has been developed to serve as a complete and optimal state space planner, with admissible estimates, exploration engines and branching cuts. In the competition version, however, certain performance compromises had to be made, including floating point arithmetic, weighted heuristic search exploration according to an inadmissible estimate and parameterized optimization.

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 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 The FF Planning System: Fast Plan Generation Through Heuristic Search

2001 ◽  
Vol 14 ◽  
pp. 253-302 ◽  
Author(s):  
J. Hoffmann ◽  
B. Nebel
Keyword(s):  
State Space ◽  
Heuristic Search ◽  
Search Strategy ◽  
Search Space ◽  
Systematic Search ◽  
Hill Climbing ◽  
Planning System ◽  
Plan Generation ◽  
State Space Search ◽  
Algorithmic Techniques

We describe and evaluate the algorithmic techniques that are used in the FF planning system. Like the HSP system, FF relies on forward state space search, using a heuristic that estimates goal distances by ignoring delete lists. Unlike HSP's heuristic, our method does not assume facts to be independent. We introduce a novel search strategy that combines hill-climbing with systematic search, and we show how other powerful heuristic information can be extracted and used to prune the search space. FF was the most successful automatic planner at the recent AIPS-2000 planning competition. We review the results of the competition, give data for other benchmark domains, and investigate the reasons for the runtime performance of FF compared to HSP.

scholarly journals Subgoaling Techniques for Satisficing and Optimal Numeric Planning

2020 ◽  
Vol 68 ◽  
pp. 691-752
Author(s):  
Enrico Scala ◽  
Patrik Haslum ◽  
Sylvie Thiébaux ◽  
Miquel Ramirez
Keyword(s):  
State Space ◽  
Heuristic Search ◽  
State Of The Art ◽  
Planning Problem ◽  
State Variables ◽  
The Core ◽  
State Space Search ◽  
Theoretical Assumptions ◽  
Good Trade ◽  
Core Idea

This paper studies novel subgoaling relaxations for automated planning with propositional and numeric state variables. Subgoaling relaxations address one source of complexity of the planning problem: the requirement to satisfy conditions simultaneously. The core idea is to relax this requirement by recursively decomposing conditions into atomic subgoals that are considered in isolation. Such relaxations are typically used for pruning, or as the basis for computing admissible or inadmissible heuristic estimates to guide optimal or satis_cing heuristic search planners. In the last decade or so, the subgoaling principle has underpinned the design of an abundance of relaxation-based heuristics whose formulations have greatly extended the reach of classical planning. This paper extends subgoaling relaxations to support numeric state variables and numeric conditions. We provide both theoretical and practical results, with the aim of reaching a good trade-o_ between accuracy and computation costs within a heuristic state-space search planner. Our experimental results validate the theoretical assumptions, and indicate that subgoaling substantially improves on the state of the art in optimal and satisficing numeric planning via forward state-space search.

scholarly journals The Fast Downward Planning System

2006 ◽  
Vol 26 ◽  
pp. 191-246 ◽  
Author(s):  
M. Helmert
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Forward Direction ◽  
Planning System ◽  
Search Performance ◽  
Full Account ◽  
Causal Graph ◽  
Alternative Representation ◽  
Best First Search ◽  
Planning Task

Fast Downward is a classical planning system based on heuristic search. It can deal with general deterministic planning problems encoded in the propositional fragment of PDDL2.2, including advanced features like ADL conditions and effects and derived predicates (axioms). Like other well-known planners such as HSP and FF, Fast Downward is a progression planner, searching the space of world states of a planning task in the forward direction. However, unlike other PDDL planning systems, Fast Downward does not use the propositional PDDL representation of a planning task directly. Instead, the input is first translated into an alternative representation called multi-valued planning tasks, which makes many of the implicit constraints of a propositional planning task explicit. Exploiting this alternative representation, Fast Downward uses hierarchical decompositions of planning tasks for computing its heuristic function, called the causal graph heuristic, which is very different from traditional HSP-like heuristics based on ignoring negative interactions of operators. In this article, we give a full account of Fast Downward's approach to solving multi-valued planning tasks. We extend our earlier discussion of the causal graph heuristic to tasks involving axioms and conditional effects and present some novel techniques for search control that are used within Fast Downward's best-first search algorithm: preferred operators transfer the idea of helpful actions from local search to global best-first search, deferred evaluation of heuristic functions mitigates the negative effect of large branching factors on search performance, and multi-heuristic best-first search combines several heuristic evaluation functions within a single search algorithm in an orthogonal way. We also describe efficient data structures for fast state expansion (successor generators and axiom evaluators) and present a new non-heuristic search algorithm called focused iterative-broadening search, which utilizes the information encoded in causal graphs in a novel way. Fast Downward has proven remarkably successful: It won the "classical'' (i.e., propositional, non-optimising) track of the 4th International Planning Competition at ICAPS 2004, following in the footsteps of planners such as FF and LPG. Our experiments show that it also performs very well on the benchmarks of the earlier planning competitions and provide some insights about the usefulness of the new search enhancements.

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.

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