Babak Behsaz

Learn More
The MAXQ hierarchical reinforcement learning method is computationally expensive in applications with deep hierarchy. In this paper, we propose a new learning algorithm for MAXQ method to address the open problem of reducing its computational complexity. While the computational cost of the algorithm is considerably decreased, the required storage of new(More)
Given a metric $$(V,d)$$ ( V , d ) and an integer $$k$$ k , we consider the problem of partitioning the points of $$V$$ V into at most $$k$$ k clusters so as to minimize the sum of radii or the sum of diameters of these clusters. The former problem is called the minimum sum of radii (MSR) problem and the latter is the minimum sum of diameters (MSD) problem.(More)
In recent years, global and grid computing emerge as two powerful technology trends. In this paper, we compare these two approaches of distributed computing. First, we present a definition for global computing that accentuates the key point in this trend. This key point distinguishes global computing from other trends and covers many such systems. Second,(More)
Determining the size of minimum vertex cover of a graph G, denoted by β(G), is an NP-complete problem. Also, for only few families of graphs, β(G) is known. We study the size of minimum vertex cover in generalized Petersen graphs. For each n and k (n > 2k), a generalized Petersen graph P (n, k), is defined by vertex set {ui, vi} and edge set {uiui+1, uivi,(More)
We consider two closely related fundamental clustering problems in this paper. In the min-<lb>sum k-clustering one is given a metric space and has to partition the points into k clusters<lb>while minimizing the sum of pairwise distances between the points within the clusters. In the<lb>Balanced k-Median problem the instance is the same and one has to obtain(More)
In this paper, we present two approximation algorithms for near-optimal design of hierarchical wireless sensor networks (WSNs) in environmental monitoring applications. Since the problem of our interest is NP-hard, we design two approximation algorithms for this problem. The first algorithm is a natural bottom-up algorithm that uses an approximation(More)
In this paper, we propose a new neuro-fuzzy system for episodic reinforcement learning tasks, NEFRL. While NEFRL has all benefits of a neuro-fuzzy architecture, it has the additional advantage that it can learn with a numerical evaluation of performance and there is no need for training input-output pairs. Also, we show that the learning algorithm of this(More)
In this paper, we consider the Unsplittable (hard) Capacitated Facility Location Problem (UCFLP) with uniform capacities and present new approximation algorithms for it. This problem is a generalization of the classical facility location problem where each facility can serve at most u units of demand and each client must be served by exactly one facility.(More)