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Evolving Solvers for FreeCell and the SlidingTile Puzzle
"... We use genetic algorithms to evolve highly successful solvers for two puzzles: FreeCell and SlidingTile Puzzle. Discrete puzzles, also known as singleplayer games, are an excellent problem domain for artificial intelligence research, because they can be parsimoniously described yet are often hard ..."
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We use genetic algorithms to evolve highly successful solvers for two puzzles: FreeCell and SlidingTile Puzzle. Discrete puzzles, also known as singleplayer games, are an excellent problem domain for artificial intelligence research, because they can be parsimoniously described yet are often
Multiple Symmetries in SlidingTile Puzzles: First Experiments
"... Since their introduction, symmetries have proven to be very powerful for the solution of different tasks related to heuristic search on slidingtile puzzles. The most relevant being the boost of the heuristic values stored in Pattern Databases (PDBs), the construction time and storing size of PDBs, ..."
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Since their introduction, symmetries have proven to be very powerful for the solution of different tasks related to heuristic search on slidingtile puzzles. The most relevant being the boost of the heuristic values stored in Pattern Databases (PDBs), the construction time and storing size of PDBs
A Selective Macrolearning Algorithm and its Application to the NxN SlidingTile Puzzle
 Journal of Artificial Intelligence Research
, 1998
"... One of the most common mechanisms used for speeding up problem solvers is macrolearning. Macros are sequences of basic operators acquired during problem solving. Macros are used by the problem solver as if they were basic operators. The major problem that macrolearning presents is the vast number o ..."
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Cited by 11 (3 self)
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acquisition of macros. Solvable training problems are generated in increasing order of difficulty. The only macros acquired are those that take the problem solver out of a local minimum to a better state. The utility of the method is demonstrated in several domains, including the domain of N \Theta N slidingtile
The branching factor of regular search spaces
 Proceedings of AAAI98
, 1998
"... Many problems, such as the slidingtile puzzles, generate search trees where different nodes have different numbers of children, in this case depending on the position of the blank. We show how to calculate the asymptotic branching factors of such problems, and how to efficiently compute the exact n ..."
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Cited by 8 (4 self)
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Many problems, such as the slidingtile puzzles, generate search trees where different nodes have different numbers of children, in this case depending on the position of the blank. We show how to calculate the asymptotic branching factors of such problems, and how to efficiently compute the exact
Finding Optimal Solutions to the TwentyFour Puzzle
 In Proc. AAAI96
, 1996
"... We have found the first optimal solutions to a complete set of random instances of the TwentyFour Puzzle, the 5 \Theta 5 version of the wellknown slidingtile puzzles. Our new contribution to this problem is a more powerful admissible heuristic function. We present a general theory for the automat ..."
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Cited by 59 (3 self)
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We have found the first optimal solutions to a complete set of random instances of the TwentyFour Puzzle, the 5 \Theta 5 version of the wellknown slidingtile puzzles. Our new contribution to this problem is a more powerful admissible heuristic function. We present a general theory
A genetic approach to planning in heterogeneous computing environments
 IN THE 12TH HETEROGENEOUS COMPUTING WORKSHOP (HCW 2003), CDROM PROC. OF THE 17TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS 2003). IEEE COMPUTER SOCIETY PRESS, LOS ALAMITOS, CA, ISBN
, 2003
"... Abstract Planning is an artificial intelligence problem with a widerange of realworld applications. Genetic algorithms, neural networks, and simulated annealing are heuristic searchmethods often used to solve complex optimization problems. In this paper, we propose a genetic approach to planningin ..."
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Cited by 4 (2 self)
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the context of workflow management and process coordination on a heterogenous grid. We report results for twoplanning problems, the Towers of Hanoi and the Slidingtile puzzle. 1.
Efficiently Searching the 15Puzzle
, 1994
"... The A* algorithm for singleagent search has attracted considerable attention in recent years due to Korf's iterative deepening improvement (IDA*). The algorithm's efficiency depends on the quality of the lower bound estimates of the solution cost. For sliding tile puzzles, reduction datab ..."
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Cited by 26 (4 self)
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The A* algorithm for singleagent search has attracted considerable attention in recent years due to Korf's iterative deepening improvement (IDA*). The algorithm's efficiency depends on the quality of the lower bound estimates of the solution cost. For sliding tile puzzles, reduction
Disjoint pattern database heuristics
 Artificial Intelligence
, 2002
"... We explore a method for computing admissible heuristic evaluation functions for search problems. It utilizes pattern databases (Culberson & Schaeffer, 1998), which are precomputed tables of the exact cost of solving various subproblems of an existing problem. Unlike standard pattern database heu ..."
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Cited by 140 (35 self)
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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 slidingtile puzzles, the 4peg Towers of Hanoi problem, and finding
GeneticBased Planning with Recursive Subgoals
"... Abstract—In this paper, we introduce an effective strategy for subgoal division and ordering based upon recursive subgoals and combine this strategy with a geneticbased planning approach. This strategy can be applied to domains with conjunctive goals. The main idea is to recursively decompose a goa ..."
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goal into a set of serializable subgoals and to specify a strict ordering among the subgoals. Empirical results show that the recursive subgoal strategy reduces the size of the search space and improves the quality of solutions to planning problems. Keywords—Planning, recursive subgoals, Slidingtile
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"... Abstract—In this paper, we introduce an effective strategy for subgoal division and ordering based upon recursive subgoals and combine this strategy with a geneticbased planning approach. This strategy can be applied to domains with conjunctive goals. The main idea is to recursively decompose a goa ..."
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goal into a set of serializable subgoals and to specify a strict ordering among the subgoals. Empirical results show that the recursive subgoal strategy reduces the size of the search space and improves the quality of solutions to planning problems. Keywords—Planning, recursive subgoals, Slidingtile
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