1,297 research outputs found
Reformulation in planning
Reformulation of a problem is intended to make the problem more amenable to efficient solution. This is equally true in the special case of reformulating a planning problem. This paper considers various ways in which reformulation can be exploited in planning
Marvin: A Heuristic Search Planner with Online Macro-Action Learning
This paper describes Marvin, a planner that competed in the Fourth
International Planning Competition (IPC 4). Marvin uses
action-sequence-memoisation techniques to generate macro-actions, which are
then used during search for a solution plan. We provide an overview of its
architecture and search behaviour, detailing the algorithms used. We also
empirically demonstrate the effectiveness of its features in various planning
domains; in particular, the effects on performance due to the use of
macro-actions, the novel features of its search behaviour, and the native
support of ADL and Derived Predicates
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 Metric-FF Planning System: Translating "Ignoring Delete Lists" to Numeric State Variables
Planning with numeric state variables has been a challenge for many years,
and was a part of the 3rd International Planning Competition (IPC-3). Currently
one of the most popular and successful algorithmic techniques in STRIPS
planning is to guide search by a heuristic function, where the heuristic is
based on relaxing the planning task by ignoring the delete lists of the
available actions. We present a natural extension of ``ignoring delete lists''
to numeric state variables, preserving the relevant theoretical properties of
the STRIPS relaxation under the condition that the numeric task at hand is
``monotonic''. We then identify a subset of the numeric IPC-3 competition
language, ``linear tasks'', where monotonicity can be achieved by
pre-processing. Based on that, we extend the algorithms used in the heuristic
planning system FF to linear tasks. The resulting system Metric-FF is,
according to the IPC-3 results which we discuss, one of the two currently most
efficient numeric planners
Progress in AI Planning Research and Applications
Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning
The FF Planning System: Fast Plan Generation Through Heuristic Search
We describe and evaluate the algorithmic techniques that are used in the FF
planning system. Like the HSP system, FF relies on forward state space search,
using a heuristic that estimates goal distances by ignoring delete lists.
Unlike HSP's heuristic, our method does not assume facts to be independent. We
introduce a novel search strategy that combines hill-climbing with systematic
search, and we show how other powerful heuristic information can be extracted
and used to prune the search space. FF was the most successful automatic
planner at the recent AIPS-2000 planning competition. We review the results of
the competition, give data for other benchmark domains, and investigate the
reasons for the runtime performance of FF compared to HSP
Efficient Implementation of the Plan Graph in STAN
STAN is a Graphplan-based planner, so-called because it uses a variety of
STate ANalysis techniques to enhance its performance. STAN competed in the
AIPS-98 planning competition where it compared well with the other competitors
in terms of speed, finding solutions fastest to many of the problems posed.
Although the domain analysis techniques STAN exploits are an important factor
in its overall performance, we believe that the speed at which STAN solved the
competition problems is largely due to the implementation of its plan graph.
The implementation is based on two insights: that many of the graph
construction operations can be implemented as bit-level logical operations on
bit vectors, and that the graph should not be explicitly constructed beyond the
fix point. This paper describes the implementation of STAN's plan graph and
provides experimental results which demonstrate the circumstances under which
advantages can be obtained from using this implementation
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