Weiya Yue

University of Cincinnati, Cincinnati, Ohio, United States

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Publications (12)

  • Weiya Yue · John Franco · Qiang Han · Weiwei Cao
    [Show abstract] [Hide abstract] ABSTRACT: In the dynamic domain, agents often operate in the terrain which is only incompletely known and can be dynamically updated on the fly. In this case, dynamic navigation algorithm, which is required to find out an optimal solution to its goal, has been an important component in planning. However, under the environments where time is more critical than optimality, a sub-optimal solution is required. Therefore the challenge for practical applications is to find a high sub-optimal solution in limited time. The dynamic algorithm Anytime D*(AD*) is currently the best anytime algorithm which aims to return a high sub-optimal solution with short corresponding time and control of sub-optimality. In this chapter, a new algorithm named Improved Anytime D*(IAD*) is introduced. By reducing the search space, experiment results show IAD*better outperforms Anytime D*in various random benchmarks.
    Article · Jan 2014 · Lecture Notes in Electrical Engineering
  • Weiya Yue · John Franco · Weiwei Cao · Hongwei Yue
    [Show abstract] [Hide abstract] ABSTRACT: Robot navigation has been of great importance especially under unknown or keep-changing environment. In order to solve this kind of problems, many algorithms have been brought up. D* Lite is generally considered as one of the most functional ones. The better performance of D* Lite largely depends on relatively less updating rather than recalculating terrain cost from scratch between robot movements. However, D* Lite still needs updating, i.e. recalculation, every time a terrain change is discovered. In this paper, we give an efficient method to check out when such recalculation can be fully or partially avoided. Experimental results show that it speeds up to 5 times for a variety of benchmarks including a novel and realistic benchmark. Our idea results in an improved version of D* Lite which we call ID* Lite. Moreover, it can be easily embedded into D* Lite variants such as DD* Lite and anytime D* etc.
    Conference Paper · Jan 2011
  • Weiya Yue · Weiwei Cao
    [Show abstract] [Hide abstract] ABSTRACT: In this paper, we give a proof for four color theorem(four color conjecture). Our proof does not involve computer assistance and the most important is that it can be generalized to prove Hadwiger Conjecture. Moreover, we give algorithms to color and test planarity of planar graphs, which can be generalized to graphs containing $K_x(x>5)$ minor. There are four parts of this paper: Part-1: To Prove Four Color Theorem Part-2: An Equivalent Statement of Hadwiger Conjecture when $k=5$ Part-3: A New Proof of Wagner's Equivalence Theorem Part-4: A Geometric View of Outerplanar Graph
    Article · Oct 2010
  • Source
    Weiya Yue · John Franco · Weiwei Cao
    [Show abstract] [Hide abstract] ABSTRACT: In this paper, we use a new method to decrease the parameterized complexity bound for finding the minimum vertex cover of connected max-degree-3 undirected graphs. The key operation of this method is reduction of the size of a particular subset of edges which we introduce in this paper and is called as "real-cycle" subset. Using "real-cycle" reductions alone we compute a complexity bound $O(1.15855^k)$ where $k$ is size of the optimal vertex cover. Combined with other techniques, the complexity bound can be further improved to be $O(1.1504^k)$. This is currently the best complexity bound.
    Full-text Article · Oct 2010
  • Yanyan Xu · Weiya Yue
    [Show abstract] [Hide abstract] 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. © by Institute of Software, the Chinese Academy of Sciences. All rights reserved.
    Article · Oct 2009 · Journal of Software
  • Yanyan Xu · Weiya Yue
    [Show abstract] [Hide abstract] 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.
    Conference Paper · Jun 2009
  • Yanyan Xu · Weiya Yue · Kaile Su
    [Show abstract] [Hide abstract] 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.
    Conference Paper · Jan 2009
  • Weiya Yue · Yanyan Xu · Kaile Su
    [Show abstract] [Hide abstract] 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
    Article · Dec 2006
  • [Show abstract] [Hide abstract] 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
    Conference Paper · Dec 2006
  • Source
    [Show abstract] [Hide abstract] 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.
    Full-text Article · Nov 2006 · Journal of Computer Science and Technology
  • Kaile Su · Weiya Yue · Abdul Sattar · [...] · Xiangyu Luo
    [Show abstract] [Hide abstract] 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
    Article · Aug 2006
  • Guanfeng Lv · Kaile Su · Han Lin · [...] · Weiya Yue
    Article · Jan 2006 · Zhongshan Daxue Xuebao/Acta Scientiarum Natralium Universitatis Sunyatseni