study on realization of generalized quantum

Agents operating in the real world often have limited time available for planning their next actions. Producing optimal plans is infeasible in these scenarios. Instead, agents must be satisfied with the best plans they can generate within the time available. One class of planners well-suited to this task are anytime planners, which quickly find an initial, highly suboptimal plan, and then improve this plan until time runs out. A second challenge associated with planning in the real world is that models are usually imperfect and environments are often dynamic. Thus, agents need to update their models and consequently plans over time. Incremental planners, which make use of the results of previous planning efforts to generate a new plan, can substantially speed up each planning episode in such cases. In this paper, we present an A*-based anytime search algorithm that produces significantly better solutions than current approaches, while also providing suboptimality bounds on the quality of the solution at any point in time. We also present an extension of this algorithm that is both anytime and incremental. This extension improves its current solution while deliberation time allows and is able to incrementally repair its solution when changes to the world model occur. We provide a number of theoretical and experimental results and demonstrate the effectiveness of the approaches in a robot navigation domain involving two physical systems. We believe that the simplicity, theoretical properties, and generality of the presented methods make them well suited to a range of search problems involving large, dynamic graphs.

Multiple sequence alignment (MSA) is a ubiquitous problem in computational biology. Although it is NP-hard to find an optimal solution for an arbitrary number of sequences, due to the importance of this problem researchers are trying to push the limits of exact algorithms further. Since MSA can be cast as a classical path finding problem, it is attracting a growing number of AI researchers interested in heuristic search algorithms as a challenge with actual practical relevance. In this paper, we first review two previous, complementary lines of research. Based on Hirschberg’s algorithm, Dynamic Programming needs O(kN k−1) space to store both the search frontier and the nodes needed to reconstruct the solution path, for k sequences of length N. Best first search, on the other hand, has the advantage of bounding the search space that has to be explored using a heuristic. However, it is necessary to maintain all explored nodes up to the final solution in order to prevent the search from re-expanding them at higher cost. Earlier approaches to reduce the Closed list are either incompatible with pruning methods for the Open list, or must retain at least the boundary of the Closed

Breadth-first and depth-first search are basic search strategies upon which many other search algorithms are built. In this paper, we describe an approach to integrating these two strategies in a single algorithm that combines the complementary strengths of both. We show the benefits of this approach using the treewidth problem as an example. 1

We present a new technique to compress consistent pattern databases without loss of information by storing the heuristic estimate modulo three, requiring only two bits per entry, or in a more compact representation only 1.6 bits. This enables us to store a pattern database with four or five times as many entries in the same amount of memory as an uncompressed pattern database. We compare both methods to the best existing compression methods for the Top-Spin puzzle, Rubik’s cube, the 4-peg Towers of Hanoi Problem, and the 24-puzzle. For the Top-Spin puzzle and Rubik’s cube we also compare our best implementation to the respective state of the art solvers. This compression technique is most useful where methods for lossy compression fail, for example where patterns mapping to adjacent entries in the pattern database are not reachable from each other by one move, such as in the Top-Spin puzzle and Rubik’s cube.