The eld of computer vision has made signi cant advances over the past twenty years, yet we still have not developed a theoretical or practical understanding of how the many components of vision are combined into coherent, functioning systems. As a result, there are few applications of computer vision technology in the real world, even though the library of available computer vision techniques k...

Building construction sites are enclosed and restricted environments with minimal space to maneuver objects. Thus the organisational management of cost control, time and quality of construction projects is a huge challenge. Methods of project management commonly used by companies in developing countries are essentially planning schedules. Often, these methods are based on the assumption that co...

We consider a problem in which we seek to optimally design a Markov decision process (MDP). That is, subject to resource constraints we first design the action sets that will be available in each state when we later optimally control the process. The control policy is subject to additional constraints governing state-action pair frequencies, and we allow randomized policies. When the design dec...

Web service which is a kind of component on the Internet shows good encapsulation, loosely coupled and cross-platform. However, the single Web service has inevitable limitations in functionality; it can’t provide people with more function and complex service. In order to reduce the cost and time of developing, and achieve services value-added and reuse, we need to combine existing services to f...

A general class of stochastic optimal control problems has recently been reduced to computing the principle eigenfunction of a linear operator. Here we present an approximation framework for solving such problems by using soft state aggregation over a continuous space. This approach enables us to avoid matrix factorization and take advantage of sparsity by using efficient iterative solvers. Ada...

Many control problems in environments that can be modeled as Markov decision processes (MDPs) concern infinite-time horizon specifications. The classical aim in this context is to compute a control policy that maximizes the probability of satisfying the specification. In many scenarios, there is however a non-zero probability of failure in every step of the system’s execution. For infinite-time...

In this paper, we develop a stylized partially observed Markov decision process (POMDP) framework, to study a dynamic pricing problem faced by sellers of fashion-like goods. We consider a retailer that plans to sell a given stock of items during a finite sales season. The objective of the retailer is to dynamically price the product in a way that maximizes expected revenues. Our model brings to...

Learning trajectories for a robot in unknown environments which can be generalized to different types of robots is a challenging problem. The input space is very high dimensional and any type of algorithm and computation has a very high time complexity and challenging to define problems in such a high state space. Applying low-dimensional reduction to such problems does the trick for us. We can...

Honeypots, which are traps designed to resemble easy to compromise computer systems, have become essential tools for security professionals and researchers because of their significant contribution in disclosing the underworld of cybercirmes. However, recent years have witnessed the development of several anti-honeypot technologies. Botmasters can exploit the fact that honeypots should not part...