scholarly journals Compilation-Based Approaches to Parallel Planning: An Empirical Comparison

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Kristýna Pantůčková ◽  
Roman Barták
Keyword(s):  
Programming Language ◽  
Constraint Satisfaction ◽  
Constraint Satisfaction Problem ◽  
Automated Planning ◽  
Boolean Satisfiability ◽  
Planning Problem ◽  
Empirical Comparison ◽  
Boolean Satisfiability Problem ◽  
International Planning ◽  
Planning Problems

Automated planning deals with finding a sequence of actions, a plan, to reach a goal. One of the possible approaches to automated planning is a compilation of a planning problem to a Boolean satisfiability problem or to a constraint satisfaction problem, which takes direct advantage of the advancements of satisfiability and constraint satisfaction solvers. This paper provides a comparison of three encodings proposed for the compilation of planning problems: Transition constraints for parallel planning (TCPP), Relaxed relaxed exist-Step encoding and Reinforced Encoding. We implemented the encodings using the programming language Picat 2.8, we suggested certain modifications, and we compared the performance of the encodings on benchmarks from international planning competitions.

Revisiting dynamic constraint satisfaction for model-based planning

2016 ◽  
Vol 31 (5) ◽  
pp. 429-439
Author(s):  
Jeremy Frank
Keyword(s):  
Constraint Satisfaction ◽  
Optimization Problems ◽  
Constraint Satisfaction Problem ◽  
Automated Planning ◽  
Planning Models ◽  
Constraint Reasoning ◽  
Dynamic Constraint ◽  
Mixed Initiative ◽  
Planning Problems

AbstractAs planning problems become more complex, it is increasingly useful to integrate complex constraints on time and resources into planning models, and use constraint reasoning approaches to help solve the resulting problems. Dynamic constraint satisfaction is a key enabler of automated planning in the presence of such constraints. In this paper, we identify some limitations with the previously developed theories of dynamic constraint satisfaction. We identify a minimum set of elementary transformations from which all other transformations can be constructed. We propose a new classification of dynamic constraint satisfaction transformations based on a formal criteria, namely the change in the fraction of solutions. This criteria can be used to evaluate elementary transformations of a constraint satisfaction problem as well as sequences of transformations. We extend the notion of transformations to include constrained optimization problems. We discuss how this new framework can inform the evolution of planning models, automated planning algorithms, and mixed-initiative planning.

scholarly journals Exploring Lifted Planning Encodings in Essence Prime

2021 ◽  
Author(s):  
Joan Espasa ◽  
Jordi Coll ◽  
Ian Miguel ◽  
Mateu Villaret
Keyword(s):  
State Space ◽  
Programming Language ◽  
Constraint Programming ◽  
Community Planning ◽  
Automated Planning ◽  
Search Method ◽  
Compact Representation ◽  
Space Planning ◽  
International Planning ◽  
Planning Problems

State-space planning is the de-facto search method of the automated planning community. Planning problems are typically expressed in the Planning Domain Definition Language (PDDL), where action and variable templates describe the sets of actions and variables that occur in the problem. Typically, a planner begins by generating the full set of instantiations of these templates, which in turn are used to derive useful heuristics that guide the search. Thanks to this success, there has been limited research in other directions. We explore a different approach, keeping the compact representation by directly reformulating the problem in PDDL into ESSENCE PRIME, a Constraint Programming language with support for distinct solving technologies including SAT and SMT. In particular, we explore two different encodings from PDDL to ESSENCE PRIME, how they represent action parameters, and their performance. The encodings are able to maintain the compactness of the PDDL representation, and while they differ slightly, they perform quite differently on various instances from the International Planning Competition.

scholarly journals REINFORCED ENCODING FOR PLANNING AS SAT

2015 ◽  
Vol 2 (2) ◽  
pp. 1-7
Author(s):  
Tomáš Balyo ◽  
Roman Barták ◽  
Otakar Trunda
Keyword(s):  
Automated Planning ◽  
The Other ◽  
Benchmark Problems ◽  
Planning Problem ◽  
Encoding Scheme ◽  
International Planning ◽  
Planning Problems

Solving planning problems via translation to satisfiability (SAT) is one of the most successful approaches to automated planning. We propose a new encoding scheme, called Reinforced Encoding, which encodes a planning problem represented in the SAS+ formalism into SAT. The Reinforced Encoding is a combination of the transition-based SASE encoding with the classical propositional encoding. In our experiments we compare our new encoding to other known SAS+ based encodings. The results indicate, that he Reinforced encoding performs well on the benchmark problems of the 2011 International Planning Competition and can outperform all the other known encodings for several domains.

scholarly journals The Computational Complexity of Probabilistic Planning

1998 ◽  
Vol 9 ◽  
pp. 1-36 ◽  
Author(s):  
M. L. Littman ◽  
J. Goldsmith ◽  
M. Mundhenk
Keyword(s):  
Computational Complexity ◽  
Efficient Algorithms ◽  
Boolean Satisfiability ◽  
Complexity Classes ◽  
Probabilistic Planning ◽  
Plan Evaluation ◽  
Partially Ordered ◽  
Boolean Satisfiability Problem ◽  
Complete Problem ◽  
Planning Problems

We examine the computational complexity of testing and finding small plans in probabilistic planning domains with both flat and propositional representations. The complexity of plan evaluation and existence varies with the plan type sought; we examine totally ordered plans, acyclic plans, and looping plans, and partially ordered plans under three natural definitions of plan value. We show that problems of interest are complete for a variety of complexity classes: PL, P, NP, co-NP, PP, NP^PP, co-NP^PP, and PSPACE. In the process of proving that certain planning problems are complete for NP^PP, we introduce a new basic NP^PP-complete problem, E-MAJSAT, which generalizes the standard Boolean satisfiability problem to computations involving probabilistic quantities; our results suggest that the development of good heuristics for E-MAJSAT could be important for the creation of efficient algorithms for a wide variety of problems.

Planning with Concurrency, Time and Resources

2005 ◽  
pp. 259-295 ◽  
Author(s):  
Amedeo Cesta ◽  
Simone Fratini ◽  
Angelo Oddi
Keyword(s):  
Constraint Satisfaction ◽  
Resource Availability ◽  
Constraint Satisfaction Problem ◽  
Satellite System ◽  
Domain Model ◽  
Planning Problem ◽  
Planning And Scheduling ◽  
Planning Framework ◽  
Current Implementation ◽  
Domain Description

This chapter proposes to model a planning problem (e.g., the control of a satellite system) by identifying a set of relevant components in the domain (e.g., communication channels, on-board memory or batteries), which need to be controlled to obtain a desired temporal behavior. The domain model is enriched with the description of relevant constraints with respect to possible concurrency, temporal limits and scarce resource availability. The paper proposes a planning framework based on this view that relies on a formalization of the problem as a Constraint Satisfaction Problem (CSP) and defines an algorithmic template in which the integration of planning and scheduling is a fundamental feature. In addition, the paper describes the current implementation of a constraint-based planner called OMP that is grounded on these ideas and shows the role constraints have in this planner, both at domain description level and as a guide for problem solving.

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 Weighted Constraint Satisfaction for Smart Home Automation and Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-15
Author(s):  
Noel Nuo Wi Tay ◽  
János Botzheim ◽  
Naoyuki Kubota
Keyword(s):  
Constraint Satisfaction ◽  
Smart Home ◽  
Service Oriented Architecture ◽  
Constraint Satisfaction Problem ◽  
Automated Planning ◽  
Loose Coupling ◽  
Web Technologies ◽  
Hard Constraints ◽  
Service Oriented ◽  
Weighted Constraint

Automation of the smart home binds together services of hardware and software to provide support for its human inhabitants. The rise of web technologies offers applicable concepts and technologies for service composition that can be exploited for automated planning of the smart home, which can be further enhanced by implementation based on service oriented architecture (SOA). SOA supports loose coupling and late binding of devices, enabling a more declarative approach in defining services and simplifying home configurations. One such declarative approach is to represent and solve automated planning through constraint satisfaction problem (CSP), which has the advantage of handling larger domains of home states. But CSP uses hard constraints and thus cannot perform optimization and handle contradictory goals and partial goal fulfillment, which are practical issues smart environments will face if humans are involved. This paper extends this approach to Weighted Constraint Satisfaction Problem (WCSP). Branch and bound depth first search is used, where its lower bound is estimated by bacterial memetic algorithm (BMA) on a relaxed version of the original optimization problem. Experiments up to 16-step planning of home services demonstrate the applicability and practicality of the approach, with the inclusion of local search for trivial service combinations in BMA that produces performance enhancements. Besides, this work aims to set the groundwork for further research in the field.

scholarly journals Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models

2018 ◽  
Author(s):  
Buser Say ◽  
Scott Sanner
Keyword(s):  
State Transition ◽  
Boolean Satisfiability ◽  
Planning Problem ◽  
Transition Structure ◽  
Complex State ◽  
Constraint Generation ◽  
Transition Models ◽  
Network Transition ◽  
Action Spaces ◽  
Planning Problems

In this paper, we leverage the efficiency of Binarized Neural Networks (BNNs) to learn complex state transition models of planning domains with discretized factored state and action spaces. In order to directly exploit this transition structure for planning, we present two novel compilations of the learned factored planning problem with BNNs based on reductions to Boolean Satisfiability (FD-SAT-Plan) as well as Binary Linear Programming (FD-BLP-Plan). Experimentally, we show the effectiveness of learning complex transition models with BNNs, and test the runtime efficiency of both encodings on the learned factored planning problem. After this initial investigation, we present an incremental constraint generation algorithm based on generalized landmark constraints to improve the planning accuracy of our encodings. Finally, we show how to extend the best performing encoding (FD-BLP-Plan+) beyond goals to handle factored planning problems with rewards.

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