Abstract
In this paper we introduce a framework to represent robot task plans based on Petri nets. Our approach enables modelling a robot task, analysing its qualitative and quantitative properties and using the Petri net representation for actual plan execution. The overall model is obtained from the composition of simple models, leading to a modular approach. Analysis is applied to a closed loop between the robot controller and the environment Petri net models. We focus here on the quantitative properties, captured by stochastic Petri net models. Furthermore, we introduce a method to identify the environment and action layer parameters of the stochastic Petri net models from real data, improving the significance of the model. The framework building blocks and a single-robot task model are detailed. Results of a case study with simulated soccer robots show the ability of the framework to provide a systematic modelling tool, and of determining, through well-known analysis methods for stochastic Petri nets, relevant properties of the task plan applied to a particular environment.

Abstract
Diffusion of innovation is a research topic which has been subject to several works in the last years. The diffusion of innovation theory aims to explain how new ideas and practices are disseminated between the members of a social system. A significant part of the existing models are based on the use of parameters which determine the process of innovation adoption, and rely on simple mathematical functions centered in the observation and description of diffusion patterns. This models enable a more explicit diffusion process study, but its use involves the estimation of diffusion coefficients, usually obtained from historical data or chronological series. This raises some problems, for instance when there is no data or it is insufficient. This paper proposes the use of evolutionary computation is an alternative approach for the simulation of innovation diffusion within organizations, in order to overcome some of the problems inherent to the existing models.

Abstract
The scheduling problem is considered in complexity theory as a NP-hard combinatorial optimization problem. Meta-heuristics proved to be very useful in the resolution of this class of problems. However, these techniques require parameter tuning which is a very hard task to perform. A Case-based Reasoning module is proposed in order to solve the parameter tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance.

Abstract
Modeling discrete event systems with sequential behavior can be a very hard and complex task. Some formalisms are used in this context, such as: Petri Nets, Statecharts, Finite automata, Grafcet and others. Among these, Grafcet seems to be a good choice because it is easy: to learn, to understand and to use. Teaching Grafcet is then relevant within engineering courses concerned with Industrial Automation. A virtual laboratory, e-GRAFCET, developed and first tested in UTAD University; it is a new, easy-to-use multimedia e-educational tool to support the self-learning process of Grafcet. This paper, reports a study of e-GRAFCET use by the students of University of Minho. A questionnaire was prepared and students asked to fulfill it in a volunteer basis. The results were statistically analyzed and the scores compared. The overall objective is to understand how the tool helps students in their study, and consequently improve their learning off Grafcet, independently of their engineering background.

Abstract
The paper addresses the problem of decreasing the overshoot for underdamped second-order systems. A new technique to control the overshoot is proposed, which is based on Posicast control and proportional integral and derivative (PID) control, which performs switching between two controllers. The aim is to use open-loop feedforward control to increase tracking performance and PID control to deal with disturbance rejection. It has been shown that the proposed control scheme can have some advantages over the classical approaches without switching capabilities.

Abstract
In practice, there are several processes which are exhibiting oscillatory behaviour. Some representatives are disk-drive heads, robot arms, cranes and power-electronics. One of techniques, aimed at reducing the oscillations, is Posicast Input Command Shaping (PICS) method. The paper combines the PICS method and Magnitude Optimum Multiple Integration (MOMI) tuning method for PID controllers. The combination of both methods significantly improves the speed and stability of the closed-loop tracking responses. Moreover, the proposed approach is relatively simple for implementation in practice and can be used either on process time-response data or on the process model in frequency-domain.