On the Right Path: A Modal Logic for Supervised Learning

Formal learning theory formalizes the process of inferring a general result from examples, as in the case of inferring grammars from sentences when learning a language. Although empirical evidence suggests that children can learn a language without responding to the correction of linguistic mistakes, the importance of Teacher in many other paradigms is significant. Instead of focusing only on learner(s), this work develops a general framework---the supervised learning game (SLG)---to investigate the interaction between Teacher and Learner. In particular, our proposal highlights several interesting features of the agents: on the one hand,Learner may make mistakes in the learning process, and she may also ignore the potential relation between different hypotheses; on the other hand, Teacher is able to correct Learner's mistakes, eliminate potential mistakes and point out the facts ignored by Learner. To reason about strategies in this game, we develop a modal logic of supervised learning (SLL). Broadly, this work takes a small step towards studying the interaction between graph games, logics and formal learning theory.Comment: The paper was accepted by LORI 2019. But due to the page-limit constraints, that Proceedings version does not include any proofs. In this version, we show the proofs for the result

RAGE Reusable Game Software Components and Their Integration into Serious Game Engines

This paper presents and validates a methodology for integrating reusable software components in diverse game engines. While conforming to the RAGE com-ponent-based architecture described elsewhere, the paper explains how the interac-tions and data exchange processes between a reusable software component and a game engine should be implemented for procuring seamless integration. To this end, a RAGE-compliant C# software component providing a difficulty adaptation routine was integrated with an exemplary strategic tile-based game “TileZero”. Implementa-tions in MonoGame, Unity and Xamarin, respectively, have demonstrated successful portability of the adaptation component. Also, portability across various delivery platforms (Windows desktop, iOS, Android, Windows Phone) was established. Thereby this study has established the validity of the RAGE architecture and its un-derlying interaction processes for the cross-platform and cross-game engine reuse of software components. The RAGE architecture thereby accommodates the large scale development and application of reusable software components for serious gaming

Learning by Erasing in Dynamic Epistemic Logic

Abstract. This work provides a comparison of learning by erasing [1] and iterated epistemic update [2] as analyzed in dynamic epistemic logic (see e.g. [3]). We show that finite identification can be modelled in dy-namic epistemic logic and that the elimination process of learning by erasing can be seen as iterated belief-revision modelled in dynamic dox-astic logic. Key words: identification in the limit, learning by erasing, induction, learning by elimination, co-learning, finite identifiability, dynamic epis-temic logic, epistemic update, belief revision