Weiya Yue

Northeast Institute of Geography and Agroecology, Beijing, Beijing Shi, China

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Publications (7)0.48 Total impact

  • Yanyan Xu, Weiya Yue
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    ABSTRACT: Recently, it has been suggested that BDD-based RePlanning A* (BDDRPA*), a BDD-based incremental version of A*, might be an efficient search method for solving path-planning problems in artificial intelligence. BDDRPA* combines ideas of BDD-based search and incremental search to repeatedly find shortest paths from a start vertex to a goal vertex while the topology of the graph changes. However, BDDRPA* only works well when vertices are added or deleted but does't consider the weighted edges. When the edge costs are changed, it doesn't work, and moreover, in BDDRPA*, the heuristic function h is set to 0, so BDDRPA* is degenerated to BDD-based incremental breadth-first search. In this article, we consider BDD-based weighted and heuristic search methods and generalize BDDRPA* to be a real BDD-based incremental heuristic search algorithm (GBDDRPA*). We then show experimentally that GBDDRPA* indeed speeds BDDRPA* up on gridworlds and thus promises to provide a good foundation for building incremental heuristic BDD-search-based replanners.
    Software Engineering, Artificial Intelligences, Networking and Parallel/Distributed Computing, 2009. SNPD '09. 10th ACIS International Conference on; 06/2009
  • Yanyan Xu, Weiya Yue
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    ABSTRACT: Incremental search reuses information from previous searches to find solutions to a series of similar search problems. It is potentially faster than solving each search problem from scratch. This is very important because many artificial intelligence systems have to adapt their plans continuously to changes in the world. If the changes are small, incremental search will be very efficient. BDD (binary decision diagram)-based heuristic search combines the advantages of BDD-based search and heuristic search. Heuristic search impacts the size of the resulting search trees and BDDs can be used to efficiently describe the sets of states based on their binary encodings. This article first introduces BDD-based heuristic search and incremental search. Combining the two methods, it then gives a BDD-based incremental heuristic search algorithm BDDRPA*. The experimental results show that BDDRPA* is a very efficient incremental heuristic search algorithm. It can be used to solve many problems like symbolic replanning and robot navigation problems and so on.
    Journal of Software 01/2009; 20(9).
  • Yanyan Xu, Weiya Yue, Kaile Su
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    ABSTRACT: Finding optimal path through a graph efficiently is central to many problems, including route planning for a mobile robot. BDD-based incremental heuristic search method uses heuristics to focus their search and reuses BDD-based information from previous searches to find solutions to series of similar search problems much faster than solving each search problem from scratch. In this paper, we apply BDD-based incremental heuristic search to robot navigation in unknown terrain, including goal-directed navigation in unknown terrain and mapping of unknown terrain. The resulting BDD-based dynamic A* (BDDD*) algorithm is capable of planning paths in unknown, partially known and changing environments in an efficient, optimal, and complete manner. We present properties about BDDD* and demonstrate experimentally the advantages of combining BDD-based incremental and heuristic search for the applications studied. We believe that our experimental results will make BDD-based D* like replanning algorithms more popular and enable robotics researchers to adapt them to additional applications.
    Frontiers in Algorithmics, Third International Workshop, FAW 2009, Hefei, China, June 20-23, 2009. Proceedings; 01/2009
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    ABSTRACT: Traditional knowledge reasonings rely on the general theorem provers and may suffer the state explosion problem and can only deal with toy examples. A more concrete model of knowledge called knowledge structure has been introduced by us (Su et al., 2004), which presents a BDD-based approach for computing knowledge and shows great improvement. But this BDD-based approach still has a substantial state explosion problem. In this paper, based on the knowledge structure, we illustrate an alternative and effective way by SAT solving for the knowledge reasoning in a group of agents, since SAT can be much more powerful in dealing with the state explosion problem than BDDs
    Computational Intelligence and Security, 2006 International Conference on; 12/2006
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    ABSTRACT: We present a new model of knowledge, belief, desire and intention, called the interpreted KBDI-system model (or KBDI-model for short). The key point of the interpreted KBDI-system model is that we express an agent's knowledge, belief, desire and intention as a set of runs (computing paths), which is exactly a system in the interpreted system model, a well-known agent model due to Halpern and his colleagues. Our KBDI-model is computationally grounded in that we are able to associate a KBDI-model with a computer program, and formulas, involving agents' knowledge, belief, desire (goal) and intention, can be understood as properties of program computations. With KBDI-model, we have two different semantics to interpret our logic of knowledge, belief, desire and intention. Moreover, with respect to each semantics, we present a sound and complete proof system. Yes Yes
    01/2006;
  • Weiya Yue, Yanyan Xu, Kaile Su
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    ABSTRACT: We introduce a new algorithm, BDDRPA*, which is an efficient BDD-based incremental heuristic search algorithm for replanning. BDDRPA* combines the incremental heuristic search with BDD-based search to efficiently solve replanning search problems in artificial intelligence. We do a lot of experiments and our experiment evaluation proves BDDRPA* to be a powerful incremental search algorithm. BDDRPA* outperforms breadth-first search by several orders of magnitude for huge size search problems. When the changes to the search problems are small, BDDRPA* needs less runtime by reusing previous information, and even when the changes reach to 20 percent of the size of the problems, BDDRPA* still works more efficiently. Yes Yes
    01/2006;
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    ABSTRACT: This paper introduces a new methodology that uses knowledge structures, a specific form of Kripke semantics for epistemic logic, to analyze communication protocols over hostile networks. The paper particularly focuses on automatic verification of authentication protocols. Our approach is based on the actual definitions of a protocol, not on some difficult-to-establish justifications. The proposed methodology is different from many previous approaches to automatic verification of security protocols in that it is justification-oriented instead of falsification-oriented, i.e., finding bugs in a protocol. The main idea is based on observations: separating a principal executing a run of protocol from the role in the protocol, and inferring a principal’s knowledge from the local observations of the principal. And we show analytically and empirically that this model can be easily reduced to Satisfiability (SAT) problem and efficiently implemented by a modern SAT solver.
    Journal of Computer Science and Technology 01/2006; 21:932-943. · 0.48 Impact Factor

Publication Stats

7 Citations
0.48 Total Impact Points

Institutions

  • 2009
    • Northeast Institute of Geography and Agroecology
      • State Key Laboratory of Computer Science
      Beijing, Beijing Shi, China
    • University of Cincinnati
      Cincinnati, Ohio, United States
  • 2006
    • Sun Yat-Sen University
      • Department of Computer Science
      Guangzhou, Guangdong Sheng, China