41 research outputs found
Taming Numbers and Durations in the Model Checking Integrated Planning System
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
Partial-Order Planning with Concurrent Interacting Actions
In order to generate plans for agents with multiple actuators, agent teams,
or distributed controllers, we must be able to represent and plan using
concurrent actions with interacting effects. This has historically been
considered a challenging task requiring a temporal planner with the ability to
reason explicitly about time. We show that with simple modifications, the
STRIPS action representation language can be used to represent interacting
actions. Moreover, algorithms for partial-order planning require only small
modifications in order to be applied in such multiagent domains. We demonstrate
this fact by developing a sound and complete partial-order planner for planning
with concurrent interacting actions, POMP, that extends existing partial-order
planners in a straightforward way. These results open the way to the use of
partial-order planners for the centralized control of cooperative multiagent
systems
A hierarchical task-network planner based on symbolic model checking
Although several approaches have been developed for planning in nondeterministic domains, solving large planning problems is still quite difficult. In this work, we present a novel algorithm, called YoYo, for planning in nondeterministic domains under the assumption of full observability. This algorithm enables us to combine the power of search-control strategies as in Planning with Hierarchical Task Networks (HTNs) with techniques from the Planning via Symbolic Model-Checking (SMC). Our experimental evaluation confirms the potentialities of our approach, demonstrating that it combines the advantages of these paradigms
A decomposed symbolic approach to reactive planning
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2003.Includes bibliographical references (p. 105-108).by Seung H. Chung.S.M
Temporal Logic Motion Planning for Mobile Robots
In this paper, we consider the problem of robot motion planning in order to satisfy formulas expressible in temporal logics. Temporal logics naturally express traditional robot specifications such as reaching a goal or avoiding an obstacle, but also more sophisticated specifications such as sequencing, coverage, or temporal ordering of different tasks. In order to provide computational solutions to this problem, we first construct discrete abstractions of robot motion based on some environmental decomposition. We then generate discrete plans satisfying the temporal logic formula using powerful model checking tools, and finally translate the discrete plans to continuous trajectories using hybrid control. Critical to our approach is providing formal guarantees ensuring that if the discrete plan satisfies the temporal logic formula, then the continuous motion also satisfies the exact same formula