On Realization of Intelligent Decision-Making in the Real World: A Foundation Decision Model Perspective

Our situated environment is full of uncertainty and highly dynamic, thus hindering the widespread adoption of machine-led Intelligent Decision-Making (IDM) in real world scenarios. This means IDM should have the capability of continuously learning new skills and efficiently generalizing across wider applications. IDM benefits from any new approaches and theoretical breakthroughs that exhibit Artificial General Intelligence (AGI) breaking the barriers between tasks and applications. Recent research has well-examined neural architecture, Transformer, as a backbone foundation model and its generalization to various tasks, including computer vision, natural language processing, and reinforcement learning. We therefore argue that a foundation decision model (FDM) can be established by formulating various decision-making tasks as a sequence decoding task using the Transformer architecture; this would be a promising solution to advance the applications of IDM in more complex real world tasks. In this paper, we elaborate on how a foundation decision model improves the efficiency and generalization of IDM. We also discuss potential applications of a FDM in multi-agent game AI, production scheduling, and robotics tasks. Finally, through a case study, we demonstrate our realization of the FDM, DigitalBrain (DB1) with 1.2 billion parameters, which achieves human-level performance over 453 tasks, including text generation, images caption, video games playing, robotic control, and traveling salesman problems. As a foundation decision model, DB1 would be a baby step towards more autonomous and efficient real world IDM applications.Comment: 26 pages, 4 figure

Evaluating the application of Reinforcement Learning algorithms on video games

Artificial Intelligence has become part of our everyday for quite some time now: movies have portrayed it in its histories, news have reported of its advancements and we have seen its results in our electronics and machinery. In the latest years a new term started to gain traction, Machine Learning, with many articles, companies and media covering it, opening possibilities of what could be achieved with the ability to train computers using all the data generated nowadays. This work gives an overview of a few current Machine Learning techniques, aiming in the application of automated video game playing. In particular, it uses the Starcraft II Reinforcement Environment as a testbed for evaluating the selected automated learning strategies

Deep Learning: Our Miraculous Year 1990-1991

In 2020, we will celebrate that many of the basic ideas behind the deep learning revolution were published three decades ago within fewer than 12 months in our "Annus Mirabilis" or "Miraculous Year" 1990-1991 at TU Munich. Back then, few people were interested, but a quarter century later, neural networks based on these ideas were on over 3 billion devices such as smartphones, and used many billions of times per day, consuming a significant fraction of the world's compute.Comment: 37 pages, 188 references, based on work of 4 Oct 201

Playful mapping in the digital age:The Playful Mapping Collective

From Mah-Jong, to the introduction of Prussian war-games, through to the emergence of location-based play: maps and play share a long and diverse history. This monograph shows how mapping and playing unfold in the digital age, when the relations between these apparently separate tropes are increasingly woven together. Fluid networks of interaction have encouraged a proliferation of hybrid forms of mapping and playing and a rich plethora of contemporary case-studies, ranging from fieldwork, golf, activism and automotive navigation, to pervasive and desktop-based games evidences this trend. Examining these cases shows how mapping and playing can form productive synergies, but also encourages new ways of being, knowing and shaping our everyday lives. The chapters in this book explore how play can be more than just an object or practice, and instead focus on its potential as a method for understanding maps and spatiality. They show how playing and mapping can be liberating, dangerous, subversive and performative