145,016 research outputs found
Theory and Algorithms for Partial Order Based Reduction in Planning
Search is a major technique for planning. It amounts to exploring a state
space of planning domains typically modeled as a directed graph. However,
prohibitively large sizes of the search space make search expensive. Developing
better heuristic functions has been the main technique for improving search
efficiency. Nevertheless, recent studies have shown that improving heuristics
alone has certain fundamental limits on improving search efficiency. Recently,
a new direction of research called partial order based reduction (POR) has been
proposed as an alternative to improving heuristics. POR has shown promise in
speeding up searches.
POR has been extensively studied in model checking research and is a key
enabling technique for scalability of model checking systems. Although the POR
theory has been extensively studied in model checking, it has never been
developed systematically for planning before. In addition, the conditions for
POR in the model checking theory are abstract and not directly applicable in
planning. Previous works on POR algorithms for planning did not establish the
connection between these algorithms and existing theory in model checking.
In this paper, we develop a theory for POR in planning. The new theory we
develop connects the stubborn set theory in model checking and POR methods in
planning. We show that previous POR algorithms in planning can be explained by
the new theory. Based on the new theory, we propose a new, stronger POR
algorithm. Experimental results on various planning domains show further search
cost reduction using the new algorithm
Survey on Directed Model Checking
International audienceThis article surveys and gives historical accounts to the algorithmic essentials of directed model checking, a promising bug-hunting technique to mitigate the state explosion problem. In the enumeration process, successor selection is prioritized. We discuss existing guidance and methods to automatically generate them by exploiting system abstractions. We extend the algorithms to feature partial-order reduction and show how liveness problems can be adapted by lifting the search Space. For deterministic, finite domains we instantiate the algorithms to directed symbolic, external and distributed search. For real-time domains we discuss the adaption of the algorithms to timed automata and for probabilistic domains we show the application to counterexample generation. Last but not least, we explain how directed model checking helps to accelerate finding solutions to scheduling problems
Symmetry reduction and heuristic search for error detection in model checking
The state explosion problem is the main limitation of model checking. Symmetries in the system being verified can be exploited in order to avoid this problem by defining an equivalence (symmetry) relation on the states of the system, which induces a semantically equivalent quotient system of smaller size. On the other hand, heuristic search algorithms can be applied to improve the bug finding capabilities of model checking. Such algorithms use
heuristic functions to guide the exploration. Bestfirst
is used for accelerating the search, while A* guarantees optimal error trails if combined with admissible estimates. We analyze some aspects of combining both approaches, concentrating on the problem of finding the optimal path to the equivalence class of a given error state. Experimental
results evaluate our approach
Symmetry Reduction in the ProB Model Checker
Model checking suffers from the state space explosion problem. One method to alleviate this problem is to exploit symmetries in the system, such that duplicate symmetric components of the state space are not explored – saving time during the checking process. This paper identifies symmetries in typical structures of the formal language of B, including relations, powersets and elements of sets, and presents a method for finding them through the modification of the well known graph isomorphism program, NAUTY. This work has been implemented in the ProB model checker and preliminary experiments indicate the idea holds much potential for improving the performance of model checking for B
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
Platform Dependent Verification: On Engineering Verification Tools for 21st Century
The paper overviews recent developments in platform-dependent explicit-state
LTL model checking.Comment: In Proceedings PDMC 2011, arXiv:1111.006
The Complexity of Planning Revisited - A Parameterized Analysis
The early classifications of the computational complexity of planning under
various restrictions in STRIPS (Bylander) and SAS+ (Baeckstroem and Nebel) have
influenced following research in planning in many ways. We go back and
reanalyse their subclasses, but this time using the more modern tool of
parameterized complexity analysis. This provides new results that together with
the old results give a more detailed picture of the complexity landscape. We
demonstrate separation results not possible with standard complexity theory,
which contributes to explaining why certain cases of planning have seemed
simpler in practice than theory has predicted. In particular, we show that
certain restrictions of practical interest are tractable in the parameterized
sense of the term, and that a simple heuristic is sufficient to make a
well-known partial-order planner exploit this fact.Comment: (author's self-archived copy
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