Abstract
Creating and using software documentation presents numerous challenges, namely in what concerns the expression of knowledge structures, consistency maintenance and classification. Adaptive Software Artifacts is a flexible approach to expressing structured contents that tackles these concerns, and that is being realized in the context of a Software Forge. Copyright © 2013 by the Association for Computing Machinery, Inc. (ACM).

Abstract
As video games, particularly, social games are growing in popularity and number of users, there has been an increasing interest in its potential as innovative teaching tools. Gamification is a new concept intending to use elements from video games in non-game applications. Education is an area with high potential for application of this concept since it seeks to promote people's motivation and engagement. The research in progress will try to find how to apply social gamification in education, testing and validating the results of that application. To fulfil these objectives, this paper presents the guidelines and main features of a social gamification framework to be applied in an existent K-6 social learning environment.

Abstract
In complexity theory, scheduling problem is considered as a NP-complete combinatorial optimization problem. Since Multi-Agent Systems manage complex, dynamic and unpredictable environments, in this work they are used to model a scheduling system subject to perturbations. Meta-heuristics proved to be very useful in the resolution of NP-complete problems. However, these techniques require extensive parameter tuning, which is a very hard and time-consuming task to perform. Based on Multi-Agent Learning concepts, this article propose a Case-based Reasoning module 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. © 2013 Springer-Verlag Berlin Heidelberg.

Abstract
This paper proposes a novel agent-based approach to Meta-Heuristics self-configuration. Meta-heuristics are algorithms with parameters which need to be set up as efficient as possible in order to unsure its performance. A learning module for self-parameterization of Meta-heuristics (MH) in a Multi-Agent System (MAS) for resolution of scheduling problems is proposed in this work. The learning module is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. Finally, some conclusions are reached and future work outlined. © 2013, Springer Science+Business Media Dordrecht.

Abstract
In this paper an Artificial Bee Colony Approach for Scheduling Optimization is presented. The adequacy of the proposed approach is validated on the minimization of the total weighted tardiness for a set of jobs to be processed on a single machine and on a set of instances for Job-Shop scheduling problem. The obtained computational results allowed concluding about their efficiency and effectiveness. The ABC performance and respective statistical significance was evaluated.

Abstract
This paper addresses the developing of Learning-Assisted Intelligent Scheduling Systems that uses active learning by accumulation and interpretation of scheduling experience or even by observation of expert's decisions. The design of intelligent systems (IS) that learn with experts is a very hard and challenging domain because current systems are becoming more and more complex and subject to rapid changes. The model for the proposed system will be presented.