Processes and continuous change in a SAT-based planner
Artificial Intelligence
Continuous time in a SAT-based planner
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Processes and continuous change in a SAT-based planner
Artificial Intelligence
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In any domain with change, the dimension of time is inherently involved. Whether the domain should be modeled in discrete time or continuous time depends on aspects of the domain to be modeled. Many complex real-world domains involve continuous time, resources, metric quantities and concurrent actions. Planning in such domains must necessarily go beyond simple discrete models of time and change. In this thesis, we show how the SAT-based planning framework can be extended to generate plans of concurrent asynchronous actions that may depend on or make change piecewise linear metric constraints in continuous time. In the SAT-based planning framework, a planning problem is formulated as a satisfiability problem of a set of propositional constraints (axioms) such that any model of the axioms corresponds to a valid plan. There are two parameters to a SAT-based planning system: an encoding scheme for representing plans of bounded length and a propositional SAT solver to search for a model. The LPSAT architecture is composed of a SAT solver integrated with a linear arithmetic constraint solver in order to deal with metric aspects of domains. We present encoding schemes for temporal models of continuous time defined in PDDL+: (i) Durative actions with discrete and/or continuous changes; (ii) Real-time temporal model with exogenous events and autonomous processes capturing continuous changes. The encoding represents, in a CNF formula over arithmetic constraints and propositional fluents, time-stamped parallel plans possibly with concurrent continuous and discrete changes. In addition, we present encoding schemes for multi-capacity resources, partitioned interval resources, and metric quantities in intervals. An interval type can be used as a parameter to action as well as a fluent type. Based on the LPSAT engine, the TM-LPSAT temporal metric planner has been implemented: Given a PDDL+ representation of a planning problem, the TM-LPSAT compiler generates its encoding, which is fed into the LPSAT to find a solution corresponding to a plan for the planning problem. We also have experimented on our temporal metric encodings with another decision procedure, MathSAT. The results show that there is plenty of room to push the limits of the SAT-based approach.