288 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
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
An Integrated Toolkit for Modern Action Planning
BĂŒtzken M, Edelkamp S, Elalaoui A, et al. An Integrated Toolkit for Modern Action Planning. In: 19th Workshop on New Results in Planning, Scheduling and Design (PUK). 2005: 1-11.In this paper we introduce to the architecture and the abilities of our
design and analysis workbench for modern action planning. The toolkit provides
automated domain analysis tools together with PDDL learning capabilities. New
optimal and suboptimal planners extend state-of-the-art technology. With the
tool, domain experts assist solving hard combinatorial problems. Approximate or
incremental solutions provided by the system are supervised. Intermediate results
are accessible to improve domain modeling and to tune exploration in generating
high quality plans, which, in turn, can be bootstrapped for domain inference
Temporal Planning with extended Timed Automata
International audienceWe consider a system modeled as a set of interacting agents evolving along time according to explicit timing constraints. In this kind of system, the planning task consists in selecting and organizing actions in order to reach a goal state in a limited time and in an optimal manner, assuming actions have a cost. We propose to reformulate the planning problem in terms of model-checking and controller synthesis on interacting agents such that the state to reach is expressed using temporal logic. We have chosen to represent each agent using the formalism of Priced Timed Game Automata (PTGA). PTGA is an extension of Timed Automata that allows the representation of cost on actions and uncontrollable actions. Relying on this domain description, we define a planning algorithm that computes the best strategy to achieve the goal. This algorithm is based on recognized model-checking and synthesis tools from the UPPAAL suite. The expressivity of this approach is evaluated on the classical Transport Domain which is extended in order to include timing constraints, cost values and uncontrollable actions. This work has been implemented and performances evaluated on benchmarks
Engineering Benchmarks for Planning: the Domains Used in the Deterministic Part of IPC-4
In a field of research about general reasoning mechanisms, it is essential to
have appropriate benchmarks. Ideally, the benchmarks should reflect possible
applications of the developed technology. In AI Planning, researchers more and
more tend to draw their testing examples from the benchmark collections used in
the International Planning Competition (IPC). In the organization of (the
deterministic part of) the fourth IPC, IPC-4, the authors therefore invested
significant effort to create a useful set of benchmarks. They come from five
different (potential) real-world applications of planning: airport ground
traffic control, oil derivative transportation in pipeline networks,
model-checking safety properties, power supply restoration, and UMTS call
setup. Adapting and preparing such an application for use as a benchmark in the
IPC involves, at the time, inevitable (often drastic) simplifications, as well
as careful choice between, and engineering of, domain encodings. For the first
time in the IPC, we used compilations to formulate complex domain features in
simple languages such as STRIPS, rather than just dropping the more interesting
problem constraints in the simpler language subsets. The article explains and
discusses the five application domains and their adaptation to form the PDDL
test suites used in IPC-4. We summarize known theoretical results on structural
properties of the domains, regarding their computational complexity and
provable properties of their topology under the h+ function (an idealized
version of the relaxed plan heuristic). We present new (empirical) results
illuminating properties such as the quality of the most wide-spread heuristic
functions (planning graph, serial planning graph, and relaxed plan), the growth
of propositional representations over instance size, and the number of actions
available to achieve each fact; we discuss these data in conjunction with the
best results achieved by the different kinds of planners participating in
IPC-4
The 3rd International Planning Competition: Results and Analysis
This paper reports the outcome of the third in the series of biennial
international planning competitions, held in association with the International
Conference on AI Planning and Scheduling (AIPS) in 2002. In addition to
describing the domains, the planners and the objectives of the competition, the
paper includes analysis of the results. The results are analysed from several
perspectives, in order to address the questions of comparative performance
between planners, comparative difficulty of domains, the degree of agreement
between planners about the relative difficulty of individual problem instances
and the question of how well planners scale relative to one another over
increasingly difficult problems. The paper addresses these questions through
statistical analysis of the raw results of the competition, in order to
determine which results can be considered to be adequately supported by the
data. The paper concludes with a discussion of some challenges for the future
of the competition series
Snazer: the simulations and networks analyzer
<p>Abstract</p> <p>Background</p> <p>Networks are widely recognized as key determinants of structure and function in systems that span the biological, physical, and social sciences. They are static pictures of the interactions among the components of complex systems. Often, much effort is required to identify networks as part of particular patterns as well as to visualize and interpret them.</p> <p>From a pure dynamical perspective, simulation represents a relevant <it>way</it>-<it>out</it>. Many simulator tools capitalized on the "noisy" behavior of some systems and used formal models to represent cellular activities as temporal trajectories. Statistical methods have been applied to a fairly large number of replicated trajectories in order to infer knowledge.</p> <p>A tool which both graphically manipulates reactive models and deals with sets of simulation time-course data by aggregation, interpretation and statistical analysis is missing and could add value to simulators.</p> <p>Results</p> <p>We designed and implemented <it>Snazer</it>, the simulations and networks analyzer. Its goal is to aid the processes of visualizing and manipulating reactive models, as well as to share and interpret time-course data produced by stochastic simulators or by any other means.</p> <p>Conclusions</p> <p><it>Snazer </it>is a solid prototype that integrates biological network and simulation time-course data analysis techniques.</p
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