This paper deals with cooperative games with n players and r alternatives which are called multi-alternative games. In the conventional multi-alternative games initiated by Bolger, each player can choose any alternative with equal possibilities. In actual social life, there exist situations in which players have some restrictions on their choice of alternatives. Considering such situations, we study restricted multi-alternative games. A value for a given game is proposed.
The differential evolution (DE) algorithm is a powerful population-based stochastic technique to search for global optimum in the continuous search space. Success of DE algorithm strongly depends on choosing its parameters. The competition in differential evolution was shown to be an efficient instrument to avoid time-consuming process of tuning control parameters. A new variant of competitive DE algorithm, called BEBERAN, was proposed and tested on benchmark functions at four levels of the search space dimension. The BEBERAN was compared with the most promising competitive variant, DEBR18. BEBERAN, in contrast to DEBR18, includes in addition the exponential crossover.
There are two kinds of universal schemes for estimating residual waiting times, those where the error tends to zero almost surely and those where the error tends to zero in some integral norm. Usually these schemes are different because different methods are used to prove their consistency. In this note we will give a single scheme where the average error is eventually small for all time instants, while the error itself tends to zero along a sequence of stopping times of density one.
We address here the problem of scale and rotation invariant object recognition, making use of a correspondence-based mechanism, in which the identity of an object represented by sensory signals is determined by matching it to a representation stored in memory. The sensory representation is in general affected by various transformations, notably scale and rotation, thus giving rise to the fundamental problem of invariant object recognition. We focus here on a neurally plausible mechanism that deals simultaneously with identification of the object and detection of the transformation, both types of information being important for visual processing. Our mechanism is based on macrocolumnar units. These evaluate identity- and transformation-specific feature similarities, performing competitive selection of the alternatives of their own subtask, and cooperate to make a coherent global decision for the identity, scale and rotation of the object.