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55
How good is almost perfect
 In Proceedings of AAAI08
, 2008
"... Abstract Heuristic search using algorithms such as A * and IDA * is the prevalent method for obtaining optimal sequential solutions for classical planning tasks. Theoretical analyses of these classical search algorithms, such as the wellknown results of Pohl, Gaschnig and Pearl, suggest that such ..."
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Cited by 65 (4 self)
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Abstract Heuristic search using algorithms such as A * and IDA * is the prevalent method for obtaining optimal sequential solutions for classical planning tasks. Theoretical analyses of these classical search algorithms, such as the wellknown results of Pohl, Gaschnig and Pearl, suggest that such heuristic search algorithms can obtain better than exponential scaling behaviour, provided that the heuristics are accurate enough. Here, we show that for a number of common planning benchmark domains, including ones that admit optimal solution in polynomial time, general search algorithms such as A * must necessarily explore an exponential number of search nodes even under the optimistic assumption of almost perfect heuristic estimators, whose heuristic error is bounded by a small additive constant. Our results shed some light on the comparatively bad performance of optimal heuristic search approaches in "simple" planning domains such as GRIPPER. They suggest that in many applications, further improvements in runtime require changes to other parts of the search algorithm than the heuristic estimator.
State abstraction in realtime heuristic search
 Journal of Artificial Intelligence Research
, 2006
"... Realtime heuristic search methods are used by situated agents in applications that require the amount of planning per move to be constantbounded regardless of the problem size. Such agents plan only a few actions in a local search space and avoid getting trapped in heuristic local minima by improv ..."
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Cited by 34 (10 self)
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Realtime heuristic search methods are used by situated agents in applications that require the amount of planning per move to be constantbounded regardless of the problem size. Such agents plan only a few actions in a local search space and avoid getting trapped in heuristic local minima by improving their heuristic function over time. We extend a wide class of realtime search algorithms with automatically built graph abstraction. Extensive empirical evaluation in the domain of goaldirected navigation demonstrates that the use of abstraction accelerates learning of the heuristic function while maintaining realtime performance. The resulting algorithm outperforms virtually all tested algorithms simultaneously along negatively correlated performance measures.
Limited discrepancy beam search
 In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI
, 2005
"... Beam search reduces the memory consumption of bestfirst search at the cost of finding longer paths but its memory consumption can still exceed the given memory capacity quickly. We therefore develop BULB (Beam search Using Limited discrepancy Backtracking), a complete memorybounded search method th ..."
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Cited by 29 (2 self)
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Beam search reduces the memory consumption of bestfirst search at the cost of finding longer paths but its memory consumption can still exceed the given memory capacity quickly. We therefore develop BULB (Beam search Using Limited discrepancy Backtracking), a complete memorybounded search method that is able to solve more problem instances of large search problems than beam search and does so with a reasonable runtime. At the same time, BULB tends to find shorter paths than beam search because it is able to use larger beam widths without running out of memory. We demonstrate these properties of BULB experimentally for three standard benchmark domains. 1
BestFirst Heuristic Search for MultiCore Machines
"... eaburns, seth.lemons, ruml at cs.unh.edu rzhou at parc.com To harness modern multicore processors, it is imperative to develop parallel versions of fundamental algorithms. In this paper, we present a general approach to bestfirst heuristic search in a sharedmemory setting. Each thread attempts to ..."
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Cited by 27 (7 self)
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eaburns, seth.lemons, ruml at cs.unh.edu rzhou at parc.com To harness modern multicore processors, it is imperative to develop parallel versions of fundamental algorithms. In this paper, we present a general approach to bestfirst heuristic search in a sharedmemory setting. Each thread attempts to expand the most promising open nodes. By using abstraction to partition the state space, we detect duplicate states without requiring frequent locking. We allow speculative expansions when necessary to keep threads busy. We identify and fix potential livelock conditions in our approach, verifying its correctness using temporal logic. In an empirical comparison on STRIPS planning, grid pathfinding, and sliding tile puzzle problems using an 8core machine, we show that A * implemented in our framework yields faster search than improved versions of previous parallel search proposals. Our approach extends easily to other bestfirst searches, such as Anytime weighted A*. 1
Maximizing over multiple pattern databases speeds up heuristic search
, 2006
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Recent progress in heuristic search: A case study of the fourpeg Towers of Hanoi problem
 in: International Joint Conference on Artificial Intelligence (IJCAI07
"... We integrate a number of recent advances in heuristic search, and apply them to the fourpeg Towers of Hanoi problem. These include frontier search, diskbased search, multiple compressed disjoint additive pattern database heuristics, and breadthfirst heuristic search. The main new idea we introduc ..."
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Cited by 22 (9 self)
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We integrate a number of recent advances in heuristic search, and apply them to the fourpeg Towers of Hanoi problem. These include frontier search, diskbased search, multiple compressed disjoint additive pattern database heuristics, and breadthfirst heuristic search. The main new idea we introduce here is the use of pattern database heuristics to search for any of a number of explicit goal states, with no overhead compared to a heuristic for a single goal state. We perform the first complete breadthfirst searches of the 21 and 22disc fourpeg Towers of Hanoi problems, and extend the verification of a “presumed optimal solution ” to this problem from 24 to 30 discs, a problem that is 4096 times larger. Fourpeg Towers of Hanoi Problem The threepeg Towers of Hanoi problem is well known in
External A*
 IN GERMAN CONFERENCE ON ARTIFICIAL INTELLIGENCE (KI
, 2004
"... In this paper we study External A*, a variant of the conventional (internal) A* algorithm that makes use of external memory, e.g., a hard disk. The approach applies to implicit, undirected, unweighted state space problem graphs with consistent estimates. It combines all three aspects of bestfirs ..."
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Cited by 20 (9 self)
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In this paper we study External A*, a variant of the conventional (internal) A* algorithm that makes use of external memory, e.g., a hard disk. The approach applies to implicit, undirected, unweighted state space problem graphs with consistent estimates. It combines all three aspects of bestfirst search, frontier search and delayed duplicate detection and can still operate on very small internal memory. The complexity of the external algorithm is almost linear in external sorting time and accumulates to O(sort(E) + scan(V I/O operations, where V and E are the set of nodes and edges in the explored portion of the state space graph. Given that delayed duplicate elimination has to be performed, the established bound is I/O optimal. In contrast to the internal algorithm, we exploit memory locality to allow blockwise rather than random access. The algorithmic design refers to external shortest path search in explicit graphs and extends the strategy of delayed duplicate detection recently suggested for breadthfirst search to bestfirst search. We conduct experiments with slidingtile puzzle instances.
ExternalMemory Pattern Databases using Structured Duplicate Detection
, 2005
"... A pattern database is a lookup table that stores an exact evaluation function for a relaxed search problem, which provides an admissible heuristic for the original search problem. In general, the larger the pattern database, the more accurate the heuristic function. We consider how to build large pa ..."
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Cited by 16 (5 self)
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A pattern database is a lookup table that stores an exact evaluation function for a relaxed search problem, which provides an admissible heuristic for the original search problem. In general, the larger the pattern database, the more accurate the heuristic function. We consider how to build large pattern databases that are stored in external memory, such as disk, and how to use an external memory pattern database efficiently in heuristic search. To limit the number of slow disk I/O operations needed to construct and query an externalmemory pattern database, we adapt an approach to externalmemory graph search called structured duplicate detection that localizes memory references by leveraging an abstraction of the state space. We present results that show this approach increases the scalability of heuristic search by allowing larger and more accurate pattern database heuristics.
Duality in Permutation State Spaces and the Dual Search Algorithm
, 2007
"... Geometrical symmetries are commonly exploited to improve the efficiency of search algorithms. A new type of symmetry in permutation state spaces, duality, is introduced. Each state has a dual state. Both states share important attributes such as their distance to the goal. Given a state S, it is sho ..."
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Cited by 13 (10 self)
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Geometrical symmetries are commonly exploited to improve the efficiency of search algorithms. A new type of symmetry in permutation state spaces, duality, is introduced. Each state has a dual state. Both states share important attributes such as their distance to the goal. Given a state S, it is shown that an admissible heuristic of the dual state of S is an admissible heuristic for S. This provides opportunities for additional heuristic evaluations. An exact definition of the class of problems where duality exists is provided. A new search algorithm, dual search, is presented which switches between the original state and the dual state when it seems likely that the switch will improve the chance of reaching the goal faster. The decision of when to switch is very important and several policies for doing this are investigated. Experimental results show significant improvements for a number of applications, for using the dual state’s heuristic evaluation and/or dual search.