We present a new string method for finding the most probable transition pathway and optimal reaction coordinate in complex chemical systems. Our approach evolves an analytic parametric curve, known as a Bézier curve, to the most probable transition path between metastable regions in configuration space. In addition, we demonstrate that the geometric properties of the Bézier curve can be used to...

The selection of optimal path is one of the classic problems in graph theory. Its utilization have various practical uses ranging from the transportation, civil engineering and other applications. Rarely those applications take into account the uncertainty of the weights of the graph. However this uncertainty can have high impact on the results. Several studies offer solution by implementing th...

We consider the problem of finding and ranking paths in semistructured data without necessarily knowing its full structure. The query language we adopt comprises conjunctions of regular path queries, allowing path variables to appear in the bodies and the heads of rules, so that paths can be returned to the user. We propose an approximate query matching semantics which adapts standard notions o...

Biswas, S. S. , Alam, B. , Doja, M. N. 2013. An algorithm for extracting intuitionistic fuzzy shortest path in a graph. Applied Computational Intelligence and Soft Computing, 17. Dubois, D. and Prade, H. 1980. Fuzzy Sets and Systems: Theory and Applications, Academic Press, New York. De, P. K. and Das, D. 2012 Ranking of trapezoidal intuitionistic fuzzy numbers, Intelligent Systems Design and A...

In this paper we present new improvement ideas of the original PageRank algorithm. The first idea is to introduce an evaluation of the statistical reliability of the ranking score of each node based on the local graph property and the second one is to introduce the notion of the path diversity. The path diversity can be exploited to dynamically modify the increment value of each node in the ran...

We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its path. In contrast to prio...

We study the problem of efficiently predicting a correct program from a large set of programs induced from few input-output examples in Programming-byExample (PBE) systems. This is an important problem for making PBE systems usable so that users do not need to provide too many examples to learn the desired program. We first formalize the two classes of sharing that occurs in version-space algeb...