Teaching in Ill-Defined Domains Using ITS and AI Appraoches

Ill-defined domains offer many challenges to computer scientists. Developing intelligent tutoring systems (ITSs) in these domains is a very challenging task due to the difficulty in modeling these domains, answers to ill-defined problems are ambiguously identified as right or wrong, and no generally accepted architecture is currently existed. This paper presents general guidelines for the development of ITSs in ill-defined domains, such as Argumentation and Ethics. This is instantiated in the two example systems AEINS and ALES. These systems offer adaptive learning processes and personalized feedback aiming to transfer the required skills to the learners and develop their reasoning

Cultivating intelligent tutoring cognizing agents in ill-defined domains using hybrid approaches

Cognizing agents are those systems that can perceive information from the external environment and can adapt to the changing conditions of that environment. Along the adaptation process a cognizing agent perceives information about the environment and generates reactions. An intelligent tutoring cognizing agent should deal not only with the tutoring system’s world but also with the learner-it should infer and predict new information about the learner and tailor the learning process to fit this specific learner. This paper shows how intelligent tutoring cognizing agents can be cultivated in ill-defined domains using hybrid techniques instantiated in the two example agents AEINS-CA and ALES-CA. These agents offer adaptive learning process and personalized feedback aiming to transfer certain cognitive skills, such as problem solving skills to the learners and develop their reasoning in the two ill-defined domains of ethics and argumentation. The paper focuses on the internal structure of each agent and the reasoning methodology, in which, the cognizing agent administration and construction along with the pedagogical scenarios are described

Aggregating and Analysing Opinions for Argument-based Relations

We present measurements of hadronic resonance, strange and multi-strange particle production in collisions of Xe-Xe and Pb-Pb at the center-of-mass energies of √sNN = 5.44 and 5.02 TeV, respectively, by the ALICE collaboration at the LHC. Particle ratios are presented as a function of multiplicity for K0 s , Λ, Ξ−, Ξ¯ +, Ω−, Ω¯ +, ρ(770)0, K∗(892)0, φ(1020) and Λ(1520). Our results are discussed and compared with predictions of QCD-inspired event generators. Additionally, comparisons with lower energy measurements and smaller systems are also presented