Independent learners in abstract traffic scenarios

Traffic is a phenomena that emerges from individual, uncoordinatedand, most of the times, selfish route choice made by drivers. In general, this leads topoor global and individual performance, regarding travel times and road network loadbalance. This work presents a reinforcement learning based approach for route choicewhich relies solely on drivers experience to guide their decisions. There is no coordinatedlearning mechanism, thus driver agents are independent learners. Our approachis tested on two abstract traffic scenarios and it is compared to other route choice methods.Experimental results show that drivers learn routes in complex scenarios with noprior knowledge. Plus, the approach outperforms the compared route choice methodsregarding drivers’ travel time. Also, satisfactory performance is achieved regardingroad network load balance. The simplicity, realistic assumptions and performance ofthe proposed approach suggests that it is a feasible candidate for implementation innavigation systems for guiding drivers decision regarding route choice

Evolutionary Tabletop Game Design: A Case Study in the Risk Game

Creating and evaluating games manually is an arduous and laborious task. Procedural content generation can aid by creating game artifacts, but usually not an entire game. Evolutionary game design, which combines evolutionary algorithms with automated playtesting, has been used to create novel board games with simple equipment; however, the original approach does not include complex tabletop games with dice, cards, and maps. This work proposes an extension of the approach for tabletop games, evaluating the process by generating variants of Risk, a military strategy game where players must conquer map territories to win. We achieved this using a genetic algorithm to evolve the chosen parameters, as well as a rules-based agent to test the games and a variety of quality criteria to evaluate the new variations generated. Our results show the creation of new variations of the original game with smaller maps, resulting in shorter matches. Also, the variants produce more balanced matches, maintaining the usual drama. We also identified limitations in the process, where, in many cases, where the objective function was correctly pursued, but the generated games were nearly trivial. This work paves the way towards promising research regarding the use of evolutionary game design beyond classic board games.Comment: 11 pages, 8 figures, accepted for publication at the XXII Braziliam Simposium on Games and Digital Entertainment (SBGames 2023

Hybrid Minimax-MCTS and Difficulty Adjustment for General Game Playing

Board games are a great source of entertainment for all ages, as they create a competitive and engaging environment, as well as stimulating learning and strategic thinking. It is common for digital versions of board games, as any other type of digital games, to offer the option to select the difficulty of the game. This is usually done by customizing the search parameters of the AI algorithm. However, this approach cannot be extended to General Game Playing agents, as different games might require different parametrization for each difficulty level. In this paper, we present a general approach to implement an artificial intelligence opponent with difficulty levels for zero-sum games, together with a propose of a Minimax-MCTS hybrid algorithm, which combines the minimax search process with GGP aspects of MCTS. This approach was tested in our mobile application LoBoGames, an extensible board games platform, that is intended to have an broad catalog of games, with an emphasis on accessibility: the platform is friendly to visually-impaired users, and is compatible with more than 92\% of Android devices. The tests in this work indicate that both the hybrid Minimax-MCTS and the new difficulty adjustment system are promising GGP approaches that could be expanded in future work

Algorithms or Actions?:A Study in Large-Scale Reinforcement Learning

Large state and action spaces are very challenging to reinforcement learning. However, in many domains there is a set of algorithms available, which estimate the best action given a state. Hence, agents can either directly learn a performance-maximizing mapping from states to actions, or from states to algorithms. We investigate several aspects of this dilemma, showing sufficient conditions for learning over algorithms to outperform over actions for a finite number of training iterations. We present synthetic experiments to further study such systems. Finally, we propose a function approximation approach, demonstrating the effectiveness of learning over algorithms in real-time strategy games

PLANO NACIONAL DE EDUCAÇÃO 2014-2024 E A FORMAÇÃO DE PROFESSORES

O objetivo deste trabalho consistiu em discutir as Metas 15 e 16 do PNE 2014-2024 por meio de revisão documental. O PNE 2001-2010 projetava 70% dos professores de educação infantil e de ensino fundamental com formação específica em nível superior (licenciatura) no respectivo decênio e, em cinco anos, aos professores do ensino médio, porém isso não aconteceu. Agora, o atual Plano ampliou suas metas incluindo formação em pós-graduação dos professores da educação básica. Parece-nos estarmos diante de números incoerentes quando comparamos o respectivo PNE às estratégias adotadas nos últimos anos pelas políticas educacionais. Os investimentos e os processos formativos não dão conta dos problemas e protelam resultados próximos aos projetados. Justificam o insucesso com o discurso da gestão complexa num país heterogêneo, somado à herança de governos anteriores. Em tese, objetivos que não levam em consideração as realidades do país colaboram para o fracasso e para metas inatingíveis

Portuguese culture and legal consciousness: a discussion of immigrant women’s perceptions of and reactions to domestic violence

This article uses legal consciousness to discuss the influence of Portuguese culture on women’s perceptions of and reactions to domestic violence. It is based on an in-depth small-scale study of Portuguese women living in England, and proposes that culture is central in shaping their behaviour, regardless of whether they experienced violence or not. The cultural characteristics that influence women the most are analysed here under the themes of ‘familism’, ‘shame and community pressure’, and ‘acculturation’. These do not operate all at the same level and their influence can change according to structural and individual circumstances. As such, the article suggests that immigrant women’s perceptions of and reactions to domestic violence can only be fully understood by articulating national culture with other structural and individual variables; this will enable a multi-layered and situated understanding of women’s legality that avoids a simplistic attribution of their behaviour to national or ethnic provenance

Enhancing deep reinforcement learning for scale flexibility in real-time strategy games

Real-time strategy (RTS) games present a unique challenge for AI agents due to the combination of several fundamental AI problems. While Deep Reinforcement Learning (DRL) has shown promise in the development of autonomous agents for the genre, existing architectures often struggle with games featuring maps of varying dimensions. This limitation hinders the agent’s ability to generalize its learned strategies across different scenarios. This paper proposes a novel approach that overcomes this problem by incorporating Spatial Pyramid Pooling (SPP) within a DRL framework. We leverage the GridNet architecture’s encoder–decoder structure and integrate an SPP layer into the critic network of the Proximal Policy Optimization (PPO) algorithm. This SPP layer dynamically generates a standardized representation of the game state, regardless of the initial observation size. This allows the agent to effectively adapt its decision-making process to any map configuration. Our evaluations demonstrate that the proposed method significantly enhances the model’s flexibility and efficiency in training agents for various RTS game scenarios, albeit with some discernible limitations when applied to very small maps. This approach paves the way for more robust and adaptable AI agents capable of excelling in sequential decision problems with variable-size observations

A NECESSIDADE E OS DESAFIOS DA APROPRIAÇÃO DAS TECNOLOGIAS VERDES PELA COMUNIDADE DE BAIXA RENDA: o caso dos telhados verdes ecológicos

Cada vez mais, a sociedade, principalmente nos centros urbanos, vem transformando espaços naturais em espaços artificiais para atendimento às demandas de bem-estar, trabalho e moradia.  Em consequência, observa-se a diminuição da biodiversidade e de todos os benefícios associados ao bem-estar de se estar cercado de áreas verdes. Nesse contexto, que bem descreve o cenário da urbanização, tem sido recorrente a tentativa de se resgatar o cenário pré-urbanização, ou seja a criação de áreas verdes, com o objetivo de minimizar os efeitos do aumento de temperatura e da escassez de água. Em algumas localidades, os impactos da urbanização podem comprometer ainda mais a qualidade de vida da população. É o caso da região Semiárida brasileira, onde, naturalmente, já existe a necessidade do uso de alternativas de convivência com a escassez hídrica e com as altas temperaturas. Para reduzir os impactos ambientais, as tecnologias verdes constituem alternativas promissoras para minimização do escoamento superficial em eventos pluviométricos extremos e para regulação térmica de ambientes externos e internos às edificações. Por outro lado, o emprego de tecnologias verdes, na maioria das vezes, confere um acréscimo ao custo do projeto original, tornando a técnica inacessível às pessoas em situação financeira pouco privilegiada. Assim sendo, uma reflexão sobre essa lacuna técnico-social é necessária. Apresenta-se, neste trabalho, através de tecnologias cientificamente investigadas, subsídios para popularização das tecnologias verdes. Afinal, a concreta apropriação pelas tecnologias verdes de forma a alcançar toda as camadas sociais, com gama de produtos acessíveis e funcionamento técnico adequado para que os benefícios esperados atinjam todos os usuários