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 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 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 Efficient Lifted Planning with Regression-Based Heuristics

2021 ◽  
Author(s):  
Hadi Qovaizi
Keyword(s):  
Organic Synthesis ◽  
State Of The Art ◽  
Transition System ◽  
Modern State ◽  
Synthetic Approach ◽  
Best First Search ◽  
Planning Task ◽  
International Planning ◽  
Domain Independent ◽  
Novel Algorithm

Modern state-of-the-art planners operate by generating a grounded transition system prior to performing search for a solution to a given planning task. Some tasks involve a significant number of objects or entail managing predicates and action schemas with a significant number of arguments. Hence, this instantiation procedure can exhaust all available memory and therefore prevent a planner from performing search to find a solution. This thesis explores this limitation by presenting a benchmark set of problems based on Organic Chemistry Synthesis that was submitted to the latest International Planning Competition (IPC-2018). This benchmark was constructed to gauge the performance of the competing planners given that instantiation is an issue. Furthermore, a novel algorithm, the Regression-Based Heuristic Planner (RBHP), is developed with the aim of averting this issue. RBHP was inspired by the retro-synthetic approach commonly used to solve organic synthesis problems efficiently. RBHP solves planning tasks by applying domain independent heuristics, computed by regression, and performing best-first search. In contrast to most modern planners, RBHP computes heuristics backwards by applying the goal-directed regression operator. However, the best-first search proceeds forward similar to other planners. The proposed planner is evaluated on a set of planning tasks included in previous International Planning Competitions (IPC) against a subset of the top scoring state-of-the-art planners submitted to the IPC-2018.

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 Friends or Foes? On Planning as Satisfiability and Abstract CNF Encodings

2009 ◽  
Vol 36 ◽  
pp. 415-469 ◽  
Author(s):  
C. Domshlak ◽  
J. Hoffmann ◽  
A. Sabharwal
Keyword(s):  
Point Of View ◽  
Theoretical Point ◽  
Optimal Plan ◽  
Interesting Phenomenon ◽  
State Spaces ◽  
Planning Task ◽  
Resolution Refutation ◽  
International Planning ◽  
Almost All ◽  
Planning Graph

Planning as satisfiability, as implemented in, for instance, the SATPLAN tool, is a highly competitive method for finding parallel step-optimal plans. A bottleneck in this approach is to *prove the absence* of plans of a certain length. Specifically, if the optimal plan has N steps, then it is typically very costly to prove that there is no plan of length N-1. We pursue the idea of leading this proof within solution length preserving abstractions (over-approximations) of the original planning task. This is promising because the abstraction may have a much smaller state space; related methods are highly successful in model checking. In particular, we design a novel abstraction technique based on which one can, in several widely used planning benchmarks, construct abstractions that have exponentially smaller state spaces while preserving the length of an optimal plan. Surprisingly, the idea turns out to appear quite hopeless in the context of planning as satisfiability. Evaluating our idea empirically, we run experiments on almost all benchmarks of the international planning competitions up to IPC 2004, and find that even hand-made abstractions do not tend to improve the performance of SATPLAN. Exploring these findings from a theoretical point of view, we identify an interesting phenomenon that may cause this behavior. We compare various planning-graph based CNF encodings F of the original planning task with the CNF encodings F_abs of the abstracted planning task. We prove that, in many cases, the shortest resolution refutation for F_abs can never be shorter than that for F. This suggests a fundamental weakness of the approach, and motivates further investigation of the interplay between declarative transition-systems, over-approximating abstractions, and SAT encodings.

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.

Accelerating Event-Driven Simulation of Spiking Neurons with Multiple Synaptic Time Constants

2009 ◽  
Vol 21 (4) ◽  
pp. 1068-1099 ◽  
Author(s):  
Michiel D'Haene ◽  
Benjamin Schrauwen ◽  
Jan Van Campenhout ◽  
Dirk Stroobandt
Keyword(s):  
Neuron Model ◽  
Driving Forces ◽  
Firing Time ◽  
State Variables ◽  
Neuron Models ◽  
Time Constants ◽  
Simulation Speed ◽  
Event Driven ◽  
Spiking Neuron Models ◽  
Algorithmic Techniques

The simulation of spiking neural networks (SNNs) is known to be a very time-consuming task. This limits the size of SNN that can be simulated in reasonable time or forces users to overly limit the complexity of the neuron models. This is one of the driving forces behind much of the recent research on event-driven simulation strategies. Although event-driven simulation allows precise and efficient simulation of certain spiking neuron models, it is not straightforward to generalize the technique to more complex neuron models, mostly because the firing time of these neuron models is computationally expensive to evaluate. Most solutions proposed in literature concentrate on algorithms that can solve this problem efficiently. However, these solutions do not scale well when more state variables are involved in the neuron model, which is, for example, the case when multiple synaptic time constants for each neuron are used. In this letter, we show that an exact prediction of the firing time is not required in order to guarantee exact simulation results. Several techniques are presented that try to do the least possible amount of work to predict the firing times. We propose an elegant algorithm for the simulation of leaky integrate-and-fire (LIF) neurons with an arbitrary number of (unconstrained) synaptic time constants, which is able to combine these algorithmic techniques efficiently, resulting in very high simulation speed. Moreover, our algorithm is highly independent of the complexity (i.e., number of synaptic time constants) of the underlying neuron model.

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 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.

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