10,008 research outputs found
Automatic frequency assignment for cellular telephones using constraint satisfaction techniques
We study the problem of automatic frequency assignment for cellular telephone
systems. The frequency assignment problem is viewed as the problem
to minimize the unsatisfied soft constraints in a constraint satisfaction problem
(CSP) over a finite domain of frequencies involving co-channel, adjacent
channel, and co-site constraints. The soft constraints are automatically derived
from signal strength prediction data. The CSP is solved using a generalized
graph coloring algorithm. Graph-theoretical results play a crucial
role in making the problem tractable. Performance results from a real-world
frequency assignment problem are presented.
We develop the generalized graph coloring algorithm by stepwise refinement,
starting from DSATUR and augmenting it with local propagation,
constraint lifting, intelligent backtracking, redundancy avoidance, and iterative
deepening
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EASe : integrating search with learned episodes
Weak methods are insufficient to solve complex problems. Constrained weak methods, like hill-climbing, search too little of the problem space. Unconstrained weak methods, like breadth-first search, are intractable. Fortunately, through the integration of multiple weak methods more powerful problem solvers can be created. We demonstrate that augmenting a weak constrained search method with episodes provides a tractable method for solving a large class of problems. We demonstrate that these episodes can be generated using an unconstrained weak method while solving simple problems from a domain. We provide an analytical model of our approach and empirical results from the logic synthesis domain of VLSI design as well as the classic tile-sliding domain
A new paradigm for minimax search
This paper introduces a new paradigm for minimax game-tree search algorithms. MT is a memory-enhanced version of Pearl's Test procedure. By changing the way MT is called, a number of best-first game-tree search algorithms can be simply and elegantly constructed (including SSS*).
Most of the assessments of minimax search algorithms have been based on simulations.
However, these simulations generally do not address two of the key ingredients of high
performance game-playing programs: iterative deepening and memory usage. This paper
presents experimental data from three game-playing programs (checkers, Othello and chess),
covering the range from low to high branching factor. The improved move ordering due to
iterative deepening and memory usage results in significantly different results from those
portrayed in the literature. Whereas some simulations show alpha-beta expanding almost
100% more leaf nodes than other algorithms [Marsland, Reinefeld & Schaeffer, 1987],
our results showed variations of less than 20%.
One new instance of our framework MTD(f) out-performs our best alpha-beta searcher
(aspiration NegaScout) on leaf nodes, total nodes and execution time. To our knowledge,
these are the first reported results that compare both depth-first and best-first algorithms given the same amount of memory
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