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Solving Markov decision processes by d-graph algorithms

Kátai Z.

Control and Cybernetics

2012

Vol. 41, no. 3

577-593

Markov decision processes (MDPs) provide a mathematical model for sequential decisionmaking (sMDP/dMDP: stochastic/ deterministic MDP). We introduce the concept of generalized dMDP (g-dMDP) where each action may result in more than one next (parallel or clone) state. The common tools to represent dMDPs are digraphs, but these are inadequate for sMDPs and g-dMDPs. We introduce d-graphs as general tools to represent all the above mentioned processes (stationary versions). We also present a combined d-graph algorithm that implements dynamic programming strategies to find optimal policies for the finite/infinite horizon versions of these Markov processes. (The preliminary version of this paper was presented at the Conference MACRo 2011.)

On the generation of graph representation of hand postures for syntactic pattern recognition

Myśliński Sz.

Journal of Applied Computer Science

2009

Vol. 17, nr 1

71-84

The results of research into construction of a method that generates an IE-graph [8] representation of hand postures are presented in the paper. The method allows one to represent hand postures of the Polish Sign Language with a class of graphs that can be parsed with an efficient ETPL(k) graph syntax analyzer introduced in [6].

A Note on Disjunctive Graph Representation

Błażewicz J., Pesch E., Sterna M.

Bulletin of the Polish Academy of Sciences. Technical Sciences

1999

Vol. 47, nr 1

103-114

The paper is concerned with a new time and memory efficient representation of a disjunctive graph. The disjunctive graph is a popular model used for describing instances of the job shop scheduling problem. The proposed data structure has the form of a graph matrix and combines advantages of a few classical graph representations.