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Choosing Search Heuristics by NonStationary Reinforcement Learning
"... Search decisions are often made using heuristic methods because realworld applications can rarely be tackled without any heuristics. In many cases, multiple heuristics can potentially be chosen, and it is not clear a priori which would perform best. In this article, we propose a procedure that learn ..."
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Cited by 32 (1 self)
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Search decisions are often made using heuristic methods because realworld applications can rarely be tackled without any heuristics. In many cases, multiple heuristics can potentially be chosen, and it is not clear a priori which would perform best. In this article, we propose a procedure that learns, during the search process, how to select promising heuristics. The learning is based on weight adaptation and can even switch between di#erent heuristics during search. Di#erent variants of the approach are evaluated within a constraintprogramming environment.
Heuristic search applied to abstract combat games
 In Canadian Conference on AI 2005
, 2005
"... Abstract. Creating strong AI forces in military war simulations or RTS video games poses many challenges including partially observable states, a possibly large number of agents and actions, and simultaneous concurrent move execution. In this paper we consider a tactical subproblem that needs to be ..."
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Abstract. Creating strong AI forces in military war simulations or RTS video games poses many challenges including partially observable states, a possibly large number of agents and actions, and simultaneous concurrent move execution. In this paper we consider a tactical subproblem that needs to be addressed on the way to strong computer generated forces: abstract combat games in which a small number of inhomogeneous units battle with each other in simultaneous move rounds until all members of one group are eliminated. We present and test several adversarial heuristic search algorithms that are able to compute reasonable actions in those scenarios using short time controls. Tournament results indicate that a new algorithm for simultaneous move games which we call “randomized alphabeta search ” (RAB) can be used effectively in the abstract combat application we consider. In this application it outperforms the other algorithms we implemented. We also show that RAB’s performance is correlated with the degree of simultaneous move interdependence present in the game. 1
A framework for online adaptive control of problem solving
 In Proc. of CP2001 workshop on OnLine combinatorial problem solving and Constraint Programming, Paphos
, 2001
"... The design of a problem solver for a particular problem depends on the problem type, the system resources, and the application requirements, as well as the specific problem instance. The difficulty in matching a solver to a problem can be ameliorated through the use of online adaptive control of sol ..."
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Cited by 5 (1 self)
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The design of a problem solver for a particular problem depends on the problem type, the system resources, and the application requirements, as well as the specific problem instance. The difficulty in matching a solver to a problem can be ameliorated through the use of online adaptive control of solving. In this approach, the solver or problem representation selection and parameters are defined appropriately to the problem structure, environment models, and dynamic performance information, and the rules or model underlying this decision are adapted dynamically. This paper presents a general framework for the adaptive control of solving and discusses the relationship of this framework both to adaptive techniques in control theory and to the existing adaptive solving literature. Experimental examples are presented to illustrate the possible uses of solver control. 1
Heuristic Search in Boundeddepth Trees: BestLeafFirst Search
, 2002
"... Many combinatorial optimization and constraint satisfaction problems can be formulated as a search for the best leaf in a tree of bounded depth. When exhaustive enumeration is infeasible, a rational strategy visits leaves in increasing order of predicted cost. Previous systematic algorithms for this ..."
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Cited by 3 (2 self)
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Many combinatorial optimization and constraint satisfaction problems can be formulated as a search for the best leaf in a tree of bounded depth. When exhaustive enumeration is infeasible, a rational strategy visits leaves in increasing order of predicted cost. Previous systematic algorithms for this setting follow a predetermined search order, making strong implicit assumptions about predicted cost and using problemspecific information inefficiently. We introduce a framework, bestleaffirst search (BLFS), that employs an explicit model of leaf cost. BLFS is complete and visits leaves in an order that efficiently approximates increasing predicted cost. Different algorithms can be derived by incorporating different sources of information into the cost model. We show how previous algorithms are special cases of BLFS.
Stochastic tree search: Where to put the randomness
 Proceedings of the IJCAI01 Workshop on Stochastic Search
"... In this short note, I argue against two commonlyheld biases. The first is that stochastic search is applicable only to improvement search over complete solutions. On the contrary, many problems have effective greedy heuristics for constructing solutions, making a treestructured search space more ap ..."
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Cited by 2 (1 self)
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In this short note, I argue against two commonlyheld biases. The first is that stochastic search is applicable only to improvement search over complete solutions. On the contrary, many problems have effective greedy heuristics for constructing solutions, making a treestructured search space more appropriate. The second is that stochastic tree search algorithms should explore the same space of decisions as systematic methods. Constructing search trees in the traditional manner, by choosing the default variable at the parent and valuing it differently at each child, makes sense for efficient complete search, but is not necessarily the best choice for incomplete methods. In an empirical study using the combinatorial optimization problem of number partitioning, I show that the opposite approach, choosing a different variable at each child and giving it the default value, can be a good choice for incomplete stochastic algorithms. 1 Stochastic Tree Search A large number of papers have appeared in recent years (including at AI conferences such as IJCAI and AAAI) devoted to stochastic improvement search for optimization problems, in which an algorithm attempts to improve a complete but potentially suboptimal solution. Many of these ‘local search’ methods, such as tabu search or simulated annealing, are completely general and use as their only source of problemspecific information the ability to evaluate the objective function on a complete solution. Others, such as WalkSAT, take advantage of heuristic guidance in the form of a function that identifies variables that might be profitably changed. Improvement methods are often contrasted with complete search methods, which use techniques such as branchandbound or dynamic backtracking [Ginsberg, 1993] to systematically extend an empty solution in all possible ways, implicitly traversing a tree containing all possible solutions. When run to completion, such methods guarantee an optimal solution. But it
A Hybrid Search Algorithm for Solving Constraint Satisfaction Problems
"... Abstract—In this paper we present a hybrid search algorithm for solving constraint satisfaction and optimization problems. This algorithm combines ideas of two basic approaches: complete and incomplete algorithms which also known as systematic search and local search algorithms. Different characteri ..."
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Cited by 1 (0 self)
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Abstract—In this paper we present a hybrid search algorithm for solving constraint satisfaction and optimization problems. This algorithm combines ideas of two basic approaches: complete and incomplete algorithms which also known as systematic search and local search algorithms. Different characteristics of systematic search and local search methods are complementary. Therefore we have tried to get the advantages of both approaches in the presented algorithm. The major advantage of presented algorithm is finding partial sound solution for complicated problems which their complete solution could not be found in a reasonable time. This algorithm results are compared with other algorithms using the well known nqueens problem.
A TwoPhase Optimization Algorithm For Mastermind
, 2007
"... This paper presents a systematic model, twophase optimization algorithms (TPOA), for Mastermind. TPOA is not only able to efficiently obtain approximate results but also effectively discover results that are getting closer to the optima. This systematic approach could be regarded as a general impro ..."
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This paper presents a systematic model, twophase optimization algorithms (TPOA), for Mastermind. TPOA is not only able to efficiently obtain approximate results but also effectively discover results that are getting closer to the optima. This systematic approach could be regarded as a general improver for heuristics. That is, given a constructive heuristic, TPOA has a higher chance to obtain results better than those obtained by the heuristic. Moreover, it sometimes can achieve optimal results that are difficult to find by the given heuristic. Experimental results show that (i) TPOA with parameter setting (k, d) 5 (1, 1) is able to obtain the optimal result for the game in the worst case, where k is the branching factor and d is the exploration depth of the search space. (ii) Using a simple heuristic, TPOA achieves the optimal result for the game in the expected case with (k, d) 5 (180, 2). This is the first approximate approach to achieve the optimal result in the expected case.
Constructive vs Perturbative Local Search for General Integer Linear Programming ⋆
"... Abstract. Most local search algorithms are “perturbative”, incrementally moving from a search state to a neighbouring state while performing noisy hillclimbing. An alternative form of local search is “constructive”, repeatedly building partial solutions using greedy or other heuristics. Both forms ..."
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Abstract. Most local search algorithms are “perturbative”, incrementally moving from a search state to a neighbouring state while performing noisy hillclimbing. An alternative form of local search is “constructive”, repeatedly building partial solutions using greedy or other heuristics. Both forms have been combined with constraint propagation, and they can be hybridised with each other by perturbing partial solutions. We design a new hybrid constructive local search algorithm for general (nonbinary) integer linear programs, combining techniques from constraint programming, boolean satisfiability, numerical optimisation and scheduling. On a hard design problem it scales better to large instances than both a perturbative algorithm and a Benders decomposition algorithm. 1
Learning How to Propagate Using Random Probing
"... Abstract. In constraint programming there are often many choices regarding the propagation method to be used on the constraints of a problem. However, simple constraint solvers usually only apply a standard method, typically (generalized) arc consistency, on all constraints throughout search. Advanc ..."
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Abstract. In constraint programming there are often many choices regarding the propagation method to be used on the constraints of a problem. However, simple constraint solvers usually only apply a standard method, typically (generalized) arc consistency, on all constraints throughout search. Advanced solvers additionally allow for the modeler to choose among an array of propagators for certain (global) constraints. Since complex interactions exist among constraints, deciding in the modelling phase which propagation method to use on given constraints can be a hard task that ideally we would like to free the user from. In this paper we propose a simple technique towards the automation of this task. Our approach exploits information gathered from a random probing preprocessing phase to automatically decide on the propagation method to be used on each constraint. As we demonstrate, data gathered though probing allows for the solver to accurately differentiate between constraints that offer little pruning as opposed to ones that achieve many domain reductions, and also to detect constraints and variables that are amenable to certain propagation methods. Experimental results from an initial evaluation of the proposed method on binary CSPs demonstrate the benefits of our approach. 1
A Hybrid Search Algorithm for Solving Constraint Satisfaction Problems
"... Abstract—In this paper we present a hybrid search algorithm for solving constraint satisfaction and optimization problems. This algorithm combines ideas of two basic approaches: complete and incomplete algorithms which also known as systematic search and local search algorithms. Different characteri ..."
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Abstract—In this paper we present a hybrid search algorithm for solving constraint satisfaction and optimization problems. This algorithm combines ideas of two basic approaches: complete and incomplete algorithms which also known as systematic search and local search algorithms. Different characteristics of systematic search and local search methods are complementary. Therefore we have tried to get the advantages of both approaches in the presented algorithm. The major advantage of presented algorithm is finding partial sound solution for complicated problems which their complete solution could not be found in a reasonable time. This algorithm results are compared with other algorithms using the well known nqueens problem.