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See:
Description
| Interface Summary | |
|---|---|
| AlgorithmicConfiguration | Algorithmic configurations provide the glue between a problem and an algorithm used for solving it. |
| AlgorithmicProblem | AlgorithmicProblem interface used for tagging hook class interfaces for algorithmic templates. |
| AlgorithmicTemplate | Base interface for algorithmic template frameworks. |
| BacktrackingProblem | Hook class for Backtracking Algorithms |
| DivideAndConquerProblem | Hook class for problems solved by DivideAndConquer. |
| DynamicProgrammingProblem | Hook class for problems solved by DynamicProgramming. |
| EvaluativeAlgorithm | Interface for evaluative algorithms. |
| GeneralSearchProblem | Hook class for GeneralSearch algorithm. |
| GreedyProblem | Hook class for Greedy Algorithms. |
| HeuristicAlgorithm | Interface for heuristic (search) algorithms. |
| MarkovDecisionProblem | Hook class for MarkovDecisionProcess algorithm. |
| MarkovDecisionProblem.Transition | Represents a transition option during a Markov decision process. |
| ProbabilisticAlgorithm | Tags probabilistic algorithms leading to approximative solutions. |
| TransitionModel | Represents a transition model. |
| TransitionModel.Transition | Represents (information about) a transition option during a transition model. |
| Class Summary | |
|---|---|
| AlgorithmicTemplate.Configuration | Default implementation of algorithmic configuration objects. |
| AStar | A* search class. |
| Backtracking | Framework (template class) for Backtracking Algorithms. |
| BestFirstSearch | BestFirstSearch class (BFS). |
| BestFirstSearch.OptionIterator | An iterator over a state space in best-first order. |
| BranchAndBound | Branch-and-bound (B&B). |
| BreadthFirstSearch | BreadthFirstSearch class (BrFS). |
| BreadthFirstSearch.OptionIterator | An iterator over a state space in breadth-first order. |
| DelegateGeneralSearchProblem | Delegates to a GeneralSearchProblem. |
| DepthFirstSearch | DepthFirstSearch class (DFS). |
| DepthFirstSearch.OptionIterator | An iterator over a state space in depth-first order. |
| DivideAndConquer | Framework (template class) for Divide and Conquer Algorithms. |
| DynamicProgramming | Framework (template class) for Dynamic Programming Algorithms. |
| DynamicProgrammingOptimizingProblem | Base hook class for problems solved by DynamicProgramming for optimization. |
| EvaluativeAlgorithm.EvaluationComparator | The canonical comparator induced by the evaluation function f(n). |
| GaussSeidelDynamicProgramming | Gauß-Seidel dynamic programming. |
| GeneralBoundingSearch | Abstract general bounding search scheme. |
| GeneralSearch | Abstract general search algorithm scheme. |
| GeneralSearch.OptionIterator | Abstract implementation base class for iterators determining the traversal order. |
| GeneralSearchProblem.Transition | Represents an option node during a search problem. |
| Greedy | Framework (template class) for Greedy Algorithms. |
| HeuristicAlgorithm.Configuration | Algorithmic configuration objects for heuristic algorithms. |
| HeuristicAlgorithm.PatternDatabaseHeuristic | An heuristic function that uses a pattern database. |
| HillClimbing | Hill-climbing search. |
| IterativeBroadening | Iterative Broadening (IB). |
| IterativeDeepening | Iterative Deepening (ID). |
| IterativeDeepeningAStar | Iterative Deepening A* (IDA*). |
| IterativeExpansion | Iterative Expansion (IE). |
| LocalOptimizerSearch | General search scheme for local optimizing search. |
| LocalOptimizerSearch.LocalSelection | The local selection mechanism used to evaluate states. |
| LocalOptimizerSearch.OptionIterator | An iterator over a state space in "choosy" random order. |
| MarkovDecisionProblem.DefaultTransition | Default implementation of transition options for Markov decision processes. |
| MarkovDecisionProcess | Markov decision process (MDP). |
| MarkovDecisionProcess.DynamicProgramming | Abstract base class for Markov decision processes solved per dynamic programming. |
| OpenClosedGeneralSearchProblem | GeneralSearchProblem wrapper keeping track of open and closed sets. |
| ParallelBranchAndBound | Parallel branch-and-bound algorithm. |
| RealTimeDynamicProgramming | Real-Time Dynamic Programming (RTDP). |
| SimulatedAnnealing | Simulated Annealing (SA) search. |
| ThresholdAccepting | Threshold Accepting (TA) search. |
| TransitionPath | A (randomized) path through state space S by transition τ of a TransitionModel. |
| WAStar | WA* search class. |
| Exception Summary | |
|---|---|
| InapplicableActionException | Thrown to indicate that an action was not applicable in the state. |
A framework for general algorithmic evaluation schemes including search and planning algorithms. This package contains algorithmic template classes (or algorithmic patterns) for rapid-prototyping and sophisticated search strategies and planning in state spaces.
Algorithmic templates get called along with a problem-specific implementation of the corresponding hook class to solve the problem. The hook class interface can be implemented in order to apply one of the prefabricated algorithms. Apart from the relief from implementing the algorithm itself, using algorithmic templates often has the powerful advantage that several different implementations can be interchanged freely without the need to change more than just a single constructor call to the concrete algorithm. Once a problem has been modeled accordingly by implementing its hook class, concrete solution algorithms can even be interchanged at runtime.
This freedom of choice concerning the concrete algorithm enables clients to find the optimal solution algorithm for the actual problem experimentally. Very sophisticated problems may even be tackled by switching algorithms, depending on the dynamic behaviour and progress of the solution process. Because of the common interface to all algorithms solving a specific problem class, switching algorithms is simple. You may also profit massively from nested search or nested optimization where one algorithm works on the solutions of another algorithm.
Important classes are:
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Orbital library 1.3.0: 11 Apr 2009 |
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