An stochastic version of the classical shortest path problem whereby for each node of a graph, a probability distribution over the set of successor nodes must be chosen so as to reach a certain destination node with minimum expected cost. In this paper, we propose a new algorithm based on Particle Swarm Optimization (PSO) for solving Stochastic Shortest Path Problem (SSPP). The comparison of ou...

In this paper, we introduce a Monte Carlo simulation method based on distributed learning automata for solving the stochastic shortest path problem. We give an iterative stochastic algorithm that find the minimum expected value of set of random variables representing cost of paths in a stochastic graph by taking sufficient samples from them. In the given algorithm, the sample size is determined...

First, we define a stochastic shortest path problem (SSP) on Borel space considering the distribution of total cost. Next, for this SSP, policy classes are set number steps to reach terminal state first time, and is designed each class. There no guarantee that probability large cost small with expected value (expected cost). The minimizes or Conditional Value at Risk (CVaR) may not state. propo...

CASPA is a stochastic process algebra tool for performance and dependability modelling, analysis and verification. It is based entirely on the symbolic data structure MTBDD (Multi-Terminal Binary Decision Diagram) which enables the tool to handle models with very large state space. This paper describes an extension of CASPA’s solving engine for path-based approximation of MTTFF (Mean Time To Fi...

This paper presents a novel formulation for the problem of finding objects in a known environment while minimizing the search cost. Our approach consists in formalizing this class of problems as Stochastic Shortest Path (SSP) problems, a decision-theoretic framework for probabilistic environments. The obtained problems are solved by using offthe-shelf domain-independent probabilistic planners. ...

This paper considers a stochastic version of the shortest path problem, the Distributionally Robust Stochastic Shortest Path Problem(DRSSPP) on directed graphs. In this model, the arc costs are deterministic, while each arc has a random delay. The mean vector and the second-moment matrix of the uncertain data are assumed known, but the exact information of the distribution is unknown. A penalty...

Algorithms to solve probabilistic planning problems can be classified in probabilistic planners and replanners. Probabilistic planners invest significant computational effort to generate a closed policy, i.e., a mapping function from every state to an action, and these solutions never “fail” if the problem correctly models the environment. Alternatively, replanners computes a partial policy, i....