Software Quality for AI : Where We Are Now?

Articial Intelligence is getting more and more popular, being adopted in a large number of applications and technology we use on a daily basis. However, a large number of Articial Intelligence applications are produced by developers without proper training on software quality practices or processes, and in general, lack in-depth knowledge regarding software engineering processes. The main reason is due to the fact that the machine-learning engineer profession has been born very recently, and currently there is a very limited number of training or guidelines on issues (such as code quality or testing) for machine learning and applications using machine learning code. In this work, we aim at highlighting the main software quality issues of Articial Intelligence systems, with a central focus on machine learning code, based on the experience of our four research groups. Moreover, we aim at dening a shared research road map, that we would like to discuss and to follow in collaboration with the workshop participants. As a result, the software quality of AI-enabled systems is often poorly tested and of very low quality.acceptedVersionPeer reviewe

Epistemological Debate Underlying Computer Simulations Used in Science Teaching: The Designers’ Perspective

Computer simulations are widely used in many research areas and their role in the production of scientific knowledge is nowadays the subject of debate in philosophy of science. However, there hasn´t been such debate regarding their use in science teaching. This work presents the results of a phenomenographic case study involving three researchers that design and use computer simulations in physics. The study analyzes these designers view on simulations and on the role of simulations in physics teaching. The results show that they agree on the fact that computer simulations have changed the way we do science and that they share many characteristics with the classical models: they derive from theories, they help to predict and explain phenomena, and their results need to be empirically validated. They consider simulations used in science teaching ?that differ from those used in research in their objectives as well as their design? to be useful as they allow students to visualize and/or work on a phenomenon from the viewpoint of the mathematical model, the physical, and the virtual one in an interrelated way. In general, the designers views on simulations and their use in science and education were more complex and meaningful than those conveyed by novel researchers in science teaching or found in research articles on secondary education that look at this subject.Fil: Seoane, María Eugenia. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas; ArgentinaFil: Arriassecq, Irene. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; ArgentinaFil: Greca, Ileana. Universidad de Burgos; Españ

Conceptual modelling for simulation Part I: definition and requirements

Conceptual modelling is probably the most important aspect of a simulation study. It is also the most difficult and least understood. Over 40 years of simulation research and practice have provided only limited information on how to go about designing a simulation conceptual model. This paper, the first of two, discusses the meaning of conceptual modelling and the requirements of a conceptual model. Founded on existing literature, a definition of a conceptual model is provided. Four requirements of a conceptual model are described: validity, credibility, utility and feasibility. The need to develop the simplest model possible is also discussed. Owing to a paucity of advice on how to design a conceptual model, the need for a conceptual modelling framework is proposed. Built on the foundations laid in this paper, a conceptual modelling framework is described in the paper that follows

Recognizing and learning models of social exchange strategies for the regulation of social interactions in open agent societies

Regulation of social exchanges refers to controlling social exchanges between agents so that the balance of exchange values involved in the exchanges are continuously kept—as far as possible—near to equilibrium. Previous work modeled the social exchange regulation problem as a POMDP (Partially Observable Markov Decision Process), and defined the policyToBDIplans algorithm to extract BDI (Beliefs, Desires, Intentions) plans from POMDP models, so that the derived BDI plans can be applied to keep in equilibrium social exchanges performed by BDI agents. The aim of the present paper is to extend that BDI-POMDP agent model for self-regulation of social exchanges with a module, based on HMM (Hidden Markov Model), for recognizing and learning partner agents’ social exchange strategies, thus extending its applicability to open societies, where new partner agents can freely appear at any time. For the recognition problem, patterns of refusals of exchange pro- posals are analyzed, as such refusals are produced by the partner agents. For the learning problem, HMMs are used to capture probabilistic state transition and observation functions that model the social exchange strategy of the partner agent, in order to translate them into POMDP’s actionbased state transition and observation functions. The paper formally addresses the problem of translating HMMs into POMDP models and vice versa, introducing the translation algorithms and some examples. A discussion on the results of simulations of strategy-based social exchanges is presented, together with an analysis about related work on social exchanges in multiagent systems