Recommender systems aim to support decision-makers by providing decision advice. We review briefly tools of Multi-Criteria Decision Analysis (MCDA), including aggregation operators, that could be the basis for a recommender system. Then we develop a multi-criteria recommender system, STROMa (SysTem of RecOmmendation Multi-criteria), to support decisions by aggregating measures of performance contained in a performance matrix. The system makes inferences about preferences using a partial order on criteria input by the decision-maker. To determine a total ordering of the alternatives, STROMa uses a multi-criteria aggregation operator, the Choquet integral of a fuzzy measure. Thus, recommendations are calculated using partial preferences provided by the decision maker and updated by the system. An integrated web platform is under development.

Link?ping University Electronic Press Conference Proceedings

Purpose: The design of tests is a very important step in the software development process because it allows us to match the users’ expectations with the finished product. Considered as a cumbersome activity, efforts have been made to automatize and alleviate the burden of test generation, but it is still a largely neglected step. The study aims to propose taking advantage of existing requirement artifacts, like scenarios that describe the dynamic of the domain in a very early stage of software development, to obtain tests from them. Design/methodology/approach: In particular, the approach proposed complement the scenarios that are textually described with a glossary, the language extended lexicon. Thus, a set of rules to derive tests from scenarios is also proposed. The tests are then described using the task/method model. Findings: The main findings of this study consist of an extension of a previously presented set of rules. And, a tool based on a media wiki platform that makes possible to record scenarios and the language extended lexicon and implement the rules to obtain the tests. Originality/value: The main originality of this study is the glossary which complements scenarios, the semantic support to obtain tests and the tool to automatize the approach.

Existing electrical networks are going through a transition and distributed energy resource, if not managed properly, can hinder this transition. Uncontrolled introduction of photovoltaics and electric vehicles in distribution networks would lead to substantial issues such as commitment mismatches, line congestions, voltage deviations, etc. This paper presents the use of a classical approach, mixed integer linear programming optimization, and a novel approach, adaptive multi-agent system, to solve the highlighted distribution side challenges by utilizing electric vehicles' storage capacity. This comparison serves as a great tool to benchmark the performance of the under-development adaptive multi-agent system methodology.