12,055 research outputs found
Additive Pattern Database Heuristics
We explore a method for computing admissible heuristic evaluation functions
for search problems. It utilizes pattern databases, which are precomputed
tables of the exact cost of solving various subproblems of an existing problem.
Unlike standard pattern database heuristics, however, we partition our problems
into disjoint subproblems, so that the costs of solving the different
subproblems can be added together without overestimating the cost of solving
the original problem. Previously, we showed how to statically partition the
sliding-tile puzzles into disjoint groups of tiles to compute an admissible
heuristic, using the same partition for each state and problem instance. Here
we extend the method and show that it applies to other domains as well. We also
present another method for additive heuristics which we call dynamically
partitioned pattern databases. Here we partition the problem into disjoint
subproblems for each state of the search dynamically. We discuss the pros and
cons of each of these methods and apply both methods to three different problem
domains: the sliding-tile puzzles, the 4-peg Towers of Hanoi problem, and
finding an optimal vertex cover of a graph. We find that in some problem
domains, static partitioning is most effective, while in others dynamic
partitioning is a better choice. In each of these problem domains, either
statically partitioned or dynamically partitioned pattern database heuristics
are the best known heuristics for the problem
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
GAMES: A new Scenario for Software and Knowledge Reuse
Games are a well-known test bed for testing search algorithms and learning methods, and many authors have presented numerous reasons for the research in this area. Nevertheless, they have not received the attention they deserve as software projects.
In this paper, we analyze the applicability of software
and knowledge reuse in the games domain. In spite of the
need to find a good evaluation function, search algorithms
and interface design can be said to be the primary concerns.
In addition, we will discuss the current state of the main
statistical learning methods and how they can be addressed
from a software engineering point of view. So, this paper
proposes a reliable environment and adequate tools, necessary in order to achieve high levels of reuse in the games domain
Faster optimal and suboptimal hierarchical search
In problem domains for which an informed admissible heuristic function is not available, one attractive approach is hierarchical search. Hierarchical search uses search in an abstracted version of the problem to dynamically generate heuristic values. This thesis makes three contributions to hierarchical search. First, we propose a simple modification to the state-of-the-art algorithm Switchback that reduces the number of expansions (and hence the running time) by approximately half, while maintaining its guarantee of optimality. Second, we propose a new algorithm for suboptimal hierarchical search, called Switch. Empirical results suggest that Switch yields faster search than straightforward modifications of Switchback, such as weighting the heuristic. Finally, we propose a modification to our optimal algorithm that uses multiple additive abstractions in order to improve performance of both optimal and suboptimal hierarchical search on some domains
Analyzing aggregate real exchange rate persistence through the lens of sectoral data.
In this paper we analyze the persistence of aggregate real exchange rates (RERs) for a group of EU-15 countries by using sectoral data. The tight relation between aggregate and sectoral persistence recently investigated by Mayoral (2008) allows us to decompose aggregate RER persistence into the persistence of its different subcomponents. We show that the distribution of sectoral persistence is highly heterogeneous and very skewed to the right, and that a limited number of sectors are responsible for the high levels of persistence observed at the aggregate level. We use quantile regression to investigate whether the traditional theories proposed to account for the slow reversion to parity (lack of arbitrage due to nontradibilities or imperfect competition and price stickiness) are able to explain the behavior of the upper quantiles of sectoral persistence. We conclude that pricing to market in the intermediate goods sector together with price stickiness have more explanatory power than variables related to the tradability of the goods or their inputs.PPP puzzle, real exchange rates, persistence, heterogeneous dynamics, aggregation bias, nontradability, imperfect competition, pricing-to-market.
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