Jacek Mandziuk

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Several problems in statistical physics, information sciences or neural computing require probabilistic modeling of multivariable systems composed of a large number of variables. Typically, the exact calculation of such models is computationally infeasible, hence there is a strong need for efficient, approximate methods in this area. One type of such(More)
This paper is focused on a Double Dummy Bridge Problem (DDBP) which consists in answering the question of how many tricks are to be taken by a pair of players assuming perfect play of all four sides with all cards being revealed. Several experiments are also presented in a variant of DDBP in which the information about to whom of the two players in a given(More)
Incremental Class Learning (ICL) provides a feasible framework for the development of scalable learning systems. Instead of learning a complex problem at once, ICL focuses on learning subproblems incrementally, one at a time — using the results of prior learning for subsequent learning — and then combining the solutions in an appropriate manner. With(More)
The term general game playing (GGP) refers to a subfield of AI which aims at developing agents able to effectively play many games from a particular class (finite, deterministic). It is also the name of the annual competition proposed by Stanford Logic Group at Stanford University (Stanford, CA, USA), which provides a framework for testing and evaluating(More)
The paper presents results of experiments of estimating the number of tricks to be taken by one pair of bridge players in so-called Double Dummy Bridge Problem, using artificial neural networks. In addition to deals presented to neural network's inputs, also some human methods of estimating strength of a hand were applied. Influence of human knowledge on(More)
In this paper a new 2-phase multi-swarm Particle Swarm Optimization approach to solving Dynamic Vehicle Routing Problem is proposed and compared with our previous single-swarm approach and with the PSO-based method proposed by other authors. Furthermore, several evaluation functions and problem encodings are proposed and experimentally verified on a set of(More)
Artificial neural networks, trained only on sample deals, without presentation of any human knowledge or even rules of the game, are used to estimate the number of tricks to be taken by one pair of bridge players in the so-called double dummy bridge problem (DDBP). Four representations of a deal in the input layer were tested leading to significant(More)
Artificial neural networks, trained only on sample bridge deals, without presentation of any human knowledge as well as the rules of the game, are applied to solving the Double Dummy Bridge Problem (DDBP). The problem, in its basic form, consist in estimation of the number of tricks to be taken by one pair of bridge players.