Investigations of one hand reaching tasks suggest that humans use some optimal criterion to select the trajectory for the motion. In this paper, the minimum jerk model that was proposed to account for single-hand reaching tasks is extended to the two-arm case. A set of experiments was performed to verify the model. The repeatability of the experimental results was investigated. The data shows t...

The paper describes how to take into consideration the presence of transmissible loads in a topology optimization method based on optimality criteria. The optimization problem has been defined as a total potential energy maximization problem with stress, displacement or stiffness constraints. The final volume of the optimal structural configuration has not to be specified a priori and is a cons...

In this paper, we study the nth-bias optimality problem for finite continuous-time Markov decision processes (MDPs) with a multichain structure. We first provide nth-bias difference formulas for two policies and present some interesting characterizations of an nth-bias optimal policy by using these difference formulas. Then, we prove the existence of an nth-bias optimal policy by using nth-bias...

We consider an optimal design problem with a hyperbolic initial boundary value problem as the state equation. As a possible application consider a body made from a mixture of different materials which is vibrating under the given external force, subject to a prescribed boundary and initial values. The control function (the distribution of given materials in the given domain) uniquely determines...

We consider the problem of selecting an optimality criterion, when total costs diverge, in deterministic infinite horizon optimization over discrete time. Our formulation allows for both discrete and continuous state and action spaces, as well as time-varying, that is, nonstationary, data. The task is to choose a criterion that is neither too overselective, so that no policy is optimal, nor too...

We present an anytime multiagent learning approach to satisfy any given optimality criterion in repeated game self-play. Our approach is opposed to classical learning approaches for repeated games: namely, learning of equilibrium, Pareto-efficient learning, and their variants. The comparison is given from a practical (or engineering) standpoint, i.e., from a point of view of a multiagent system...

Central to the various purposes for which general classification is practiced is the preservation, or summarizing, of information (Michener, 1963; Sneath, 1957; Sokal, 1962; Farris, 1967; Johnson, 1968; Rogers, 1961; Wirth, Estabrook, and Rogers, 1966). This discussion will present one method in which this fundamental concept can be made more precise and, within that context, suggest possible l...

In this paper, we propose two simple yet efficient computational algorithms to obtain approximate optimal designs for multi-dimensional linear regression on a large variety of design spaces. We focus the commonly used criteria, D- and A-optimal criteria. For D-optimality, provide an alternative proof monotonic convergence D-optimal criterion algorithm design. further show that proposed converge...

Task scheduling is a wide research area with several scheduling techniques, each of which being based on a specific algorithmic approach. Although these techniques are often adjusted to the specifics of the various scheduling problems, there are few that are optimal for real-time domain. The aim of this paper is to give an overview the main algorithmic approaches and optimality criteria that co...

We consider the following scheduling setting: a set of tasks have to be executed on a set of identical machines. It is well known that shortest processing time (SPT) schedules are optimal for the problem of minimizing the total sum of completion times of the tasks. In this paper, we measure the quality of SPT schedules, from an approximation point of view, with respect to the following optimali...