Towards the automatic generation of card games through Grammar-Guided Genetic Programming

We demonstrate generating complete and playable card games using evolutionary algorithms. Card games are represented in a previously devised card game description language, a context-free grammar. The syntax of this language allows us to use grammar-guided genetic programming. Candidate card games are evaluated through a cascading evaluation function, a multi-step process where games with undesired properties are progressively weeded out. Three representa- tive examples of generated games are analysed. We observed that these games are reasonably balanced and have skill ele- ments, they are not yet entertaining for human players. The particular shortcomings of the examples are discussed in re- gard to the generative process to be able to generate quality game

Antz Game for Mobile Devices

Bakalářská práce popisuje problematiku vývoje her pro mobilní zařízení. Obsahuje popis dnes nejvíce používaných platforem mobilních zařízení a snaží se vybrat jednu z nich, vhodnou pro vývoj jednoduché karetní hry Mravenci. Dále popisuje návrh a implementaci této hry na platformě Windows Mobile v programovacím jazyce C#. Podstatná část práce se zabývá tvorbou uživatelského rozhraní. V závěru jsou diskutována možná budoucí rozšíření hry a obecné problémy provázející vývoj her pro mobilní zařízení.This Bachelor's thesis describes the basics of game development for mobile devices. It contains a description of common mobile platforms and tries to choose one of them, suitable for developing of simple card game Antz. Next it describes design and implementation of this game on platform Windows Mobile and programming language C#. Major part of this document dwells on graphical user interface development. At the end it discusses future extensions of the game and common problems of game development for mobile devices.

Ludii -- The Ludemic General Game System

While current General Game Playing (GGP) systems facilitate useful research in Artificial Intelligence (AI) for game-playing, they are often somewhat specialised and computationally inefficient. In this paper, we describe the "ludemic" general game system Ludii, which has the potential to provide an efficient tool for AI researchers as well as game designers, historians, educators and practitioners in related fields. Ludii defines games as structures of ludemes -- high-level, easily understandable game concepts -- which allows for concise and human-understandable game descriptions. We formally describe Ludii and outline its main benefits: generality, extensibility, understandability and efficiency. Experimentally, Ludii outperforms one of the most efficient Game Description Language (GDL) reasoners, based on a propositional network, in all games available in the Tiltyard GGP repository. Moreover, Ludii is also competitive in terms of performance with the more recently proposed Regular Boardgames (RBG) system, and has various advantages in qualitative aspects such as generality.Comment: Accepted at ECAI 202

I linguaggi specialistici in Habemus Papam (2011). Analisi dei sottotitoli rovesciati in spagnolo e tedesco con incursioni nel doppiaggio

Habemus papam (2011) by Nanni Moretti, a well-known auteur film, is a multilingual movie that allows an analysis of different linguistic subcodes: from religious to psychoanalytic language, from medical to theatrical discourse, from the lexicon of card games to the language of sport. Specifically, the essay examines the Italian original dialogues and then focuses on the results of the study carried out on the Spanish and German interlingual subtitles (reversed subtitling) in three selected scenes in which special languages (in particular psychoanalytic, medical and card game discourse) play a key role in characters’ description. The research highlights some difficulties encountered by audiovisual translators who resorted to differing solutions both in the subtitles and in significant passages of dubbing in the two target languages. The study aims at identifying the translation trends of language variation, in particular as far as diaphasic variation is concerned

A Syntactic Neural Model for General-Purpose Code Generation

We consider the problem of parsing natural language descriptions into source code written in a general-purpose programming language like Python. Existing data-driven methods treat this problem as a language generation task without considering the underlying syntax of the target programming language. Informed by previous work in semantic parsing, in this paper we propose a novel neural architecture powered by a grammar model to explicitly capture the target syntax as prior knowledge. Experiments find this an effective way to scale up to generation of complex programs from natural language descriptions, achieving state-of-the-art results that well outperform previous code generation and semantic parsing approaches.Comment: To appear in ACL 201