Constraint Satisfaction Techniques for Planning and Scheduling Problems (COPLAS-15) (original) (raw)
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Processes and continuous change in a SAT-based planner
Artificial Intelligence, 2005
The TM-LPSAT planner can construct plans in domains containing atomic actions and durative actions; events and processes; discrete, real-valued, and interval-valued fluents; reusable resources, both numeric and interval-valued; and continuous linear change to quantities. It works in three stages. In the first stage, a representation of the domain and problem in an extended version of PDDL+ is compiled into a system of Boolean combinations of propositional atoms and linear constraints over numeric variables. In the second stage, a SAT-based arithmetic constraint solver, such as LPSAT or MathSAT, is used to find a solution to the system of constraints. In the third stage, a correct plan is extracted from this solution. We discuss the structure of the planner and show how planning with time and metric quantities is compiled into a system of constraints. The proofs of soundness and completeness over a substantial subset of our extended version of PDDL+ are presented.
Lemma reusing for SAT based planning and scheduling
2006
In this paper, we propose a new approach, called lemma-reusing, for accelerating SAT based planning and scheduling. Generally, SAT based approaches generate a sequence of SAT problems which become larger and larger. A SAT solver needs to solve the problems until it encounters a satisfiable SAT problem. Many state-of-the-art SAT solvers learn lemmas called conflict clauses to prune redundant search space, but lemmas deduced from a certain SAT problem can not apply to solve other SAT problems. However, in certain SAT encodings of planning and scheduling, we prove that lemmas generated from a SAT problem are reusable for solving larger SAT problems. We implemented the lemma-reusing planner (LRP) and the lemma-reusing job shop scheduling problem solver (LRS). The experimental results show that LRP and LRS are faster than lemma-no-reusing ones. Our approach makes it possible to use the latest SAT solvers more efficiently for the SAT based planning and scheduling.
A Compact and Efficient SAT Encoding for Planning
2008
In the planning-as-SAT paradigm there have been numerous recent developments towards improving the speed and scalability of planning at the cost of finding a step-optimal parallel plan. These developments have been towards: (1) Query strategies that efficiently yield approximately optimal plans, and (2) Having a SAT procedure compute plans from relaxed encodings of the corresponding decision problems in such a way that conflicts in a plan arising from the relaxation are resolved cheaply during a post-processing phase. In this paper we examine a third direction of tightening constraints in order to achieve a more compact, efficient, and scalable SAT-based encoding of the planning problem. For the first time, we use lifting (i.e., operator splitting) and factoring to encode the corresponding n-step decision problems with a parallel action semantics. To ensure compactness we exploit reachability and neededness analysis of the plangraph. Our encoding also captures state-dependent mutex constraints computed during that analysis. Because we adopt a lifted action representation, our encoding cannot generally support full action parallelism. Thus, our approach could be termed approximate, planning for a number of steps between that required in the optimal parallel case and the optimal linear case. We perform a detailed experimental analysis of our approach with 3 state-of-the-art SAT-based planners using benchmarks from recent international planning competitions. We find that our approach dominates optimal SAT-based planners, and is more efficient than the relaxed planners for domains where the plan existence problem is hard.
2008
Automated planning is one of the most important problems in artificial intelligence. We present a new refinement of the classical planning algorithm that formulates the planning problem as a satisfiability problem. Compared with previous techniques, the solution of the planning problem is identified using the number of truth assignments of the corresponding propositional formula and their actions' utilities. Our approach eliminates backtracking and supports efficient planners that consider additional subformulas without the need to recompute solutions for previously provided subformulas. The experimental results show that our approach can help existing SAT-based state-of-the-art planners to find the solution plan more efficiently. * This paper is an improved and unified presentation of conference papers [2] and . It contains a new section (Section 5) and complete proofs for the theoretical results (e.g., Lemma 4.1, 4.2, and Theorem 4.1), new definitions and more comprehensive experimental results.
Continuous Time in a SAT-Based Planner
2004
The TM-LPSAT planner can construct plans in domains containing atomic actions and durative actions; events and processes; discrete, real-valued, and interval-valued fluents; and continuous linear change to quantities. It works in three stages. In the first stage, a representation of the domain and problem in an extended version of PDDL+ is compiled into a system of propositional combinations of propositional variables and linear constraints over numeric variables. In the second stage, the LPSAT constraint engine (Wolfman & Weld 2000) is used to find a solution to the system of constraints. In the third stage, a correct parallel plan is extracted from this solution. We discuss the structure of the planner and show how a real-time temporal model is compiled into LPSAT constraints.
Constraint satisfaction techniques in planning and scheduling
Journal of Intelligent Manufacturing, 2010
Over the last few years constraint satisfaction, planning, and scheduling have received increased attention, and substantial effort has been invested in exploiting constraint satisfaction techniques when solving real life planning and scheduling problems. Constraint satisfaction is the process of finding a solution to a set of constraints. Planning is the process of finding a sequence of actions that transfer the world from some initial state to a desired state. Scheduling is the problem of assigning a set of tasks to a set of resources subject to a set of constraints. In this paper, we introduce the main definitions and techniques of constraint satisfaction, planning and scheduling from the Artificial Intelligence point of view.
Introduction to planning, scheduling and constraint satisfaction
Journal of Intelligent Manufacturing, 2010
Planning, scheduling and constraint satisfaction are important areas in Artificial Intelligence (AI). Many real-world problems are known as AI planning and scheduling problems, where resources must be allocated so as to optimize overall performance objectives. Therefore, solving these problems requires an adequate mixture of planning, scheduling and resource allocation to competing goal activities over time in the presence of complex statedependent constraints. Constraint satisfaction plays also an important role to solve real-life problems, so that integrated techniques that manage planning and scheduling with constraint satisfaction remains necessary.
2010
The area of AI planning and scheduling has seen important advances thanks to the application of constraint satisfaction and optimization techniques. Efficient constraint handling is important for real-world problems in planning, scheduling, and resource allocation to competing goal activities over time in the presence of complex state-dependent constraints. Approaches to these problems must integrate resource allocation and plan synthesis capabilities. We need to manage complex problems where planning, scheduling, and constraint satisfaction must be interrelated, which entail a great potential of application. The workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems, or COPLAS, aims at providing a forum for meeting and exchanging ideas and novel works in the field of AI planning, scheduling, and constraint satisfaction techniques, and the many relationships that exist among them. In fact, most of the accepted papers are based on combined approaches of constraint satisfaction for planning, scheduling, and mixing planning and scheduling. This makes the COPLAS workshop an attractive place for both researchers and practitioners (COPLAS is ranked as CORE B in ERA Conference Ranking). The sixth edition of the workshop, COPLAS 2011, was held in June 2011 in Freiburg, Germany during the International Conference on Automated Planning and Scheduling (ICAPS'11). All the submissions were reviewed by at least three anonymous referees from the program committee. The nine papers accepted for oral presentation in the workshop, provide a mix of constraint satisfaction and optimization techniques for planning, scheduling, and related topics, as well as their applications to real-world problems. We hope that the ideas and approaches presented in the papers and presentations will lead to a valuable discussion and will inspire future research and developments for all the readers. The Organizing Committee.
Constraint satisfaction for planning and scheduling problems
Constraints - An International Journal, 2011
The areas of planning and scheduling (from the Artificial Intelligence point of view) have seen important advances thanks to application of constraint satisfaction techniques. Currently, many important real-world problems require efficient constraint handling for planning, scheduling and resource allocation to competing goal activities over time in the presence of complex state-dependent constraints. Solutions to these problems require integration of resource
Numeric State Variables in Constraint-Based Planning
Lecture Notes in Computer Science, 2000
We extend a planning algorithm to cover simple forms of arithmetics. The operator preconditions can refer to the values of numeric variables and the operator postconditions can modify the values of numeric variables. The basis planning algorithm is based on techniques from propositional satisfiability testing and does not restrict to forward or backward chaining. When several operations affect a numeric variable by increasing and decreasing its value in parallel, the effects have to be combined in a meaningful way. This problem is especially acute in planning algorithms that maintain an incomplete state description of every time point of a plan execution. The approach we take requires that for operators that are executed in parallel, all linearizations of the operations to total orders behave equivalently. We provide an efficient and general solution to the problem.