Videogame designers hope to sculpt gameplay, but actually work in the concrete medium of computation. What they create is code, artwork, dialogue---everything that goes inside a videogame cartridge. In other materially constrained design domains, design-support tools help bridge this gap by automating portions of a design in some cases, and helping a designer understand the implications of their design decisions in others. I investigate AI-based videogame-design support, and do so from the perspective of putting knowledge-representation and reasoning (KRR) at the front. The KRR-centric approach starts by asking whether we can formalize an aspect of the game-design space in a way suitable for automated or semi-automated analysis, and if so, what can be done with the results. It begins with the question, "what could a computer possibly do here?", attempts to show that the computer actually can do so, and then looks at the implications of the computer doing so for design support. To organize the space of game-design knowledge, I factor the broad notion of game mechanics mechanics into four categories: abstract mechanics, concrete audiovisual representations, thematic mappings, and input mappings. Concretely, I investigate KRR-centric formalizations in three domains, which probe into different portions of the four quadrants of game-design knowledge: 1. using story graphs and story-quality functions for writing interactive stories, 2. automatic game design focused on the "aboutness" of games, which auto-reskins videogames by formalizing generalized spaces of thematic references, and 3. enhancing mechanics-oriented videogame prototypes by encoding the game mechanics in temporal logic, so that they can be both played and queried.

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Orientador: Guilherme Targino Valente / Resumo: A intensa utilização de combustíveis fósseis gerapreocupações constantes devido aos impactos de sua combustão ao meio ambiente. Os biocombustíveis são uma alternativa viável aos combustíveis fósseis por apresentarem vantagens como serem menos agressivos ao meio ambiente. O bioetanol é um dos biocombustíveis mais utilizados no mundo e sua produção pode ser feita pela fermentação realizada pela levedura Saccharomyces cerevisiae. No entanto, altas concentrações de etanol inibem diversos mecanismos biológicos da levedura, causando a diminuição da produtividade. A partir de resultados prévios, observou-se que o ciclo celular é uma das vias mais afetadas pelo etanol e, além disso, constatou-se a presença de lncRNAs regulando esta via emduas linhagens de S. cerevisiae, a BY4742 e SEY6210. Utilizando operadores Booleanos, um modelo lógico discreto foi desenvolvido para o ciclo celular no qual os nós do sistema assumem até quatro valores discretos que representam a quantidade ou o graude ativaçãodesses nós. O modelo desenvolvido apresentou boa performance preditiva, acertando 87.27% dos 109 fenótipos obtidosda literatura, tornando possível a simulação de novos elementos. Experimentos prévios demonstraram que as leveduras de baixatolerância ao etanol conseguem retomar o crescimento mais rápido do que as de alta tolerância. Nesse trabalho, simulações feitas com dados de expressão diferencial via RNA-Seq permitiu inferir que isso ocorre porque as linhagens de baixa tolerância sofrem arre... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The intense use of fossil fuels raised concern about the future due to their negative environmental impact. Bio-fuels are alternatives to the fossil fuels due to be biodegradable and less environmentally harmful. The bio-ethanol is one of the most popular bio-fuel. It can be produced by fermentation using the yeast Saccharomyces cereviae. However, high ethanol concentration inhibits the yeast decreasing the ethanol yield. Previous data of our groups showed the cell cycle is one of most affected pathways during ethanol stress. Moreover, it was found lncRNAs regulating this pathway in the BY4742 and SEY6210 strains. Using Boolean operators the discrete logical model of the cell cycle was developed. The nodes may get up to four discrete values to represent theirs abundance of activation degree. This model correctly modeled around 87.27% of correct predictions based on 109 phenotypes from the literature, hence, this model is desirable to predict cell cycle behavior after addition of new elements. According to previous data of our group, the lower tolerant strains recover the normal growth faster than higher tolerant strains after stress relief. The simulations here presented by adding RNASeq information into the model, showed a cell cycle arrest at final phase of the cell cycle (M phase) in lower tolerant strains whereas in the higher tolerant ones this arrest occurs at the first phase (G1 phase) during the ethanol treatment. The simulations also indicated that in SEY6210 (low to... (Complete abstract click electronic access below) / Mestre

Analogies are considered a cognitive core mechanism, that is applied in many everday reasoning processes. Heuristic-driven theory projection (HDTP) is a logic-based framework that allows to model analogies with the aim of making analogical reasoning techniques available for artificial intelligence systems. The formal properties of HDTP are investigated, refining and extending some of the original ideas. A special form of restricted higher-order anti-unification is proposed as a means for the generalization process, allowing to account for flexibility in the mapping while staying computationally tractable. Concerning the semantics, it is argued that a sensible interpretation can be given to the syntactic processes, based on an understanding of the involved mappings as a decent type of theory morphisms. The logical nature of HDTP also allows for a notion of re-representation that is discussed from a theoretical and algorithmic point of view. Moreover, the framework of HDTP is also analyzed from the abstract perspective of institution theory, suggesting that the main ideas can be spelled out in other logical formalisms as well. To collect support for the practical utility of HDTP, it is applied to different fields in a series of studies. The domain of geometric analogy serves as an arena to demonstrate the operation of HDTP, including the treatment of ambiguous problems based on thoughts from Gestalt psychology. Another line of research explores how the idea of conceptual blending can be related to analogies and a formalization building on HDTP is presented, leading to the notion of theory blending. These ideas are applied to a classical problem of the field, the interpretation of noun-noun compounds, but they proof to be applicable in other areas as well, demonstrated by a framework for counterfactual reasoning. Furthermore, applications of analogical reasoning and theory blending in mathematics are discussed, including the formal modeling of an example from the history of mathematics and a framework to support mathematical discovery.

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Human immunodeficiency virus (HIV-1) is the pathogenic agent of HIV infection thatprecedes the total breakdown of cellular immunity, a condition known as acquiredimmunodeficiency syndrome (AIDS). The pandemic nature of the disease has promptedintense research into its biology. Already, much is known about HIV-1 infection, lifecycle,and progression to aids. Systems biology enables the combination of complex data fromthese studies into a framework where their effect on the various levels of cellularorganization (i.e. Pathways, cells, tissues, organs and the whole body) could be studied insilico. In this thesis, first, we reviewed our knowledge of the HIV-1 Human InteractionDatabase. We examined its contents and identified processes that HIV-1 was not previouslyknown to interact with. Then, we attempted an in silico dynamic model of HIV-1 interaction. We built a model of HIV-1 interaction with the CD4 T cell activation pathway comprised of137 nodes (16 HIV-1, 121 human) and 336 interactions. The model reproduced expectedpatterns of T cell activation. Using interaction graph properties, we identified 26 host cellfactors, including MAPK1&3, Ikkb-Ikky-Ikka and PKA, which contribute to the net activationor inhibition of viral proteins. By following a logical Boolean formalism, we identified 9 hostcell factors essential to the functions of viral proteins in the activation pathway. This wasthe first attempt to model dynamic viral-host interaction relationships. Then, we organize HIV-1 interacting host genes into modules to represent cellular processesneeded by the virus. We combined HIV-1 interactions with host gene GO annotations toclassify host genes according to these needed cellular processes. We obtained 201 modulesand found the same set of viral proteins do not interact with host genes having similarmodules suggesting intelligence in its co-ordination of host processes. This work is one of agrowing list that explores coordination of HIV-1 interactions. But more importantly, it would bebeneficial to functionally downsize the large dynamic HIV-1 interaction network. Finally, in our discussion, we discuss our results and suggest possible ways in which our workon dynamic models could be improved. This work is opening up a new field of systems virologythat studies the effect of viruses on the host in terms of its temporal and spatial aspects.