135 research outputs found
Planning Graph as a (Dynamic) CSP: Exploiting EBL, DDB and other CSP Search Techniques in Graphplan
This paper reviews the connections between Graphplan's planning-graph and the
dynamic constraint satisfaction problem and motivates the need for adapting CSP
search techniques to the Graphplan algorithm. It then describes how explanation
based learning, dependency directed backtracking, dynamic variable ordering,
forward checking, sticky values and random-restart search strategies can be
adapted to Graphplan. Empirical results are provided to demonstrate that these
augmentations improve Graphplan's performance significantly (up to 1000x
speedups) on several benchmark problems. Special attention is paid to the
explanation-based learning and dependency directed backtracking techniques as
they are empirically found to be most useful in improving the performance of
Graphplan
The GRT Planning System: Backward Heuristic Construction in Forward State-Space Planning
This paper presents GRT, a domain-independent heuristic planning system for
STRIPS worlds. GRT solves problems in two phases. In the pre-processing phase,
it estimates the distance between each fact and the goals of the problem, in a
backward direction. Then, in the search phase, these estimates are used in
order to further estimate the distance between each intermediate state and the
goals, guiding so the search process in a forward direction and on a best-first
basis. The paper presents the benefits from the adoption of opposite directions
between the preprocessing and the search phases, discusses some difficulties
that arise in the pre-processing phase and introduces techniques to cope with
them. Moreover, it presents several methods of improving the efficiency of the
heuristic, by enriching the representation and by reducing the size of the
problem. Finally, a method of overcoming local optimal states, based on domain
axioms, is proposed. According to it, difficult problems are decomposed into
easier sub-problems that have to be solved sequentially. The performance
results from various domains, including those of the recent planning
competitions, show that GRT is among the fastest planners
AltAltp: Online Parallelization of Plans with Heuristic State Search
Despite their near dominance, heuristic state search planners still lag
behind disjunctive planners in the generation of parallel plans in classical
planning. The reason is that directly searching for parallel solutions in state
space planners would require the planners to branch on all possible subsets of
parallel actions, thus increasing the branching factor exponentially. We
present a variant of our heuristic state search planner AltAlt, called AltAltp
which generates parallel plans by using greedy online parallelization of
partial plans. The greedy approach is significantly informed by the use of
novel distance heuristics that AltAltp derives from a graphplan-style planning
graph for the problem. While this approach is not guaranteed to provide optimal
parallel plans, empirical results show that AltAltp is capable of generating
good quality parallel plans at a fraction of the cost incurred by the
disjunctive planners
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
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
Planning Graph Heuristics for Belief Space Search
Some recent works in conditional planning have proposed reachability
heuristics to improve planner scalability, but many lack a formal description
of the properties of their distance estimates. To place previous work in
context and extend work on heuristics for conditional planning, we provide a
formal basis for distance estimates between belief states. We give a definition
for the distance between belief states that relies on aggregating underlying
state distance measures. We give several techniques to aggregate state
distances and their associated properties. Many existing heuristics exhibit a
subset of the properties, but in order to provide a standardized comparison we
present several generalizations of planning graph heuristics that are used in a
single planner. We compliment our belief state distance estimate framework by
also investigating efficient planning graph data structures that incorporate
BDDs to compute the most effective heuristics.
We developed two planners to serve as test-beds for our investigation. The
first, CAltAlt, is a conformant regression planner that uses A* search. The
second, POND, is a conditional progression planner that uses AO* search. We
show the relative effectiveness of our heuristic techniques within these
planners. We also compare the performance of these planners with several state
of the art approaches in conditional 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
Filtering, Decomposition and Search Space Reduction for Optimal Sequential Planning
International audienceWe present in this paper a hybrid planning system which combines constraint satisfaction techniques and planning heuris-tics to produce optimal sequential plans. It integrates its own consistency rules and filtering and decomposition mechanisms suitable for planning. Given a fixed bound on the plan length, our planner works directly on a structure related to Graphplan's planning graph. This structure is incrementally built: Each time it is extended, a sequential plan is searched. Different search strategies may be employed. Currently, it is a forward chaining search based on problem decomposition with action sets partitioning. Various techniques are used to reduce the search space, such as memorizing nogood states or estimating goals reachability. In addition, the planner implements two different techniques to avoid enumerating some equivalent action sequences. Empirical evaluation shows that our system is very competitive on many problems, especially compared to other optimal sequential planners
Where 'Ignoring Delete Lists' Works: Local Search Topology in Planning Benchmarks
Between 1998 and 2004, the planning community has seen vast progress in terms
of the sizes of benchmark examples that domain-independent planners can tackle
successfully. The key technique behind this progress is the use of heuristic
functions based on relaxing the planning task at hand, where the relaxation is
to assume that all delete lists are empty. The unprecedented success of such
methods, in many commonly used benchmark examples, calls for an understanding
of what classes of domains these methods are well suited for. In the
investigation at hand, we derive a formal background to such an understanding.
We perform a case study covering a range of 30 commonly used STRIPS and ADL
benchmark domains, including all examples used in the first four international
planning competitions. We *prove* connections between domain structure and
local search topology -- heuristic cost surface properties -- under an
idealized version of the heuristic functions used in modern planners. The
idealized heuristic function is called h^+, and differs from the practically
used functions in that it returns the length of an *optimal* relaxed plan,
which is NP-hard to compute. We identify several key characteristics of the
topology under h^+, concerning the existence/non-existence of unrecognized dead
ends, as well as the existence/non-existence of constant upper bounds on the
difficulty of escaping local minima and benches. These distinctions divide the
(set of all) planning domains into a taxonomy of classes of varying h^+
topology. As it turns out, many of the 30 investigated domains lie in classes
with a relatively easy topology. Most particularly, 12 of the domains lie in
classes where FFs search algorithm, provided with h^+, is a polynomial solving
mechanism. We also present results relating h^+ to its approximation as
implemented in FF. The behavior regarding dead ends is provably the same. We
summarize the results of an empirical investigation showing that, in many
domains, the topological qualities of h^+ are largely inherited by the
approximation. The overall investigation gives a rare example of a successful
analysis of the connections between typical-case problem structure, and search
performance. The theoretical investigation also gives hints on how the
topological phenomena might be automatically recognizable by domain analysis
techniques. We outline some preliminary steps we made into that direction
- ā¦