Biologically-inspired AI methods like evolutionary algorithms have shown great promise in creating complex structures yet these structures still pale in comparison to their natural counterparts. The recently introduced generative encoding compositional pattern producing networks (CPPNs), which is based on the principles of how natural organisms develop, narrowed this gap by showing that it is possible to artificially evolve life-like patterns with regularities at a high-level of abstraction. As these generative and developmental systems (GDS) are asked to evolve increasingly complex structures, the question of how to start evolution from a promising part of the search space becomes more and more important. To address this challenge, we introduce the concept of a CPPN-Compiler, which allows the user to directly compile a high-level description of the desired starting structure into the CPPN itself. In this paper, as proof of concept, the CPPN-Compiler is able to generate CPPN-encoded representations from vector-based images that can serve as the starting point for further evolution. Importantly, the offspring of these compiled CPPNs show meaningful variations because they directly embody important domain-specific regularities like symmetry or repetition. Thus the results presented in this paper open up a new research direction in GDS, in which specialized CPPN-Compilers for different domains could help to overcome the black box of evolutionary optimization.

Subfields of artificial intelligence often diversify from a core idea. For example, deep learning networks, models in computational neuroscience, and neuroevolution all take inspiration from biological neural networks as a potential pathway to AI. Most researchers choose to pursue the subfield (and by extension, abstraction) they see as most promising for leading to AI, which naturally results in significant debate and disagreement among researchers as to what abstraction is best. A better understanding and less polarized debate may result from a clear presentation and discussion of abstractions by their most knowledgeable proponents. These insights motivated bringing together researchers from fields that abstract AI at different levels or in different ways to disperse knowledge, and to critically examining the value and promise of different abstractions. Thus this AAAI symposium, How Intelligence Should be Abstracted in AI, consisted of a diverse and multidisciplinary group of AI researchers interested in discussing and comparing different abstractions of both intelligence and processes that might create it.

List of organizers of the AAAI Fall Symposium, How SHould Intelligence Be Abstracted in AI Research: MDPs, Symbolic Representations, Artificial Neural Networks, or _____?

Search-based procedural content generation methods allow video games to introduce new content continually, thereby engaging the player for a longer time while reducing the burden on developers. However, games so far have not explored the potential economic value of unique evolved artifacts. Building on this insight, this paper presents for the first time a Facebook game called Petalz in which players can share flowers they breed themselves with other players through a global marketplace. In particular, the market in this social game allows players to set the price of their evolved aesthetically-pleasing flowers in virtual currency. Furthermore, the transaction in which one player buys seeds from another creates a new social element that links the players in the transaction. The combination of unique user-generated content and social gaming in Petalz facilitates meaningful collaboration between users, positively influences the dynamics of the game, and opens new possibilities in digital entertainment.

While doing ML algorithms like linear Regression, logistic regression, etc. the algorithm uses one or more independent variables for predicting the dependent variable. The data set should be balanced to avoid predicting the results incorrectly. But it is not necessary that the data set is balanced all the time. In some cases like fraudulent data, cancer patient records, etc the data set may be imbalanced. Today we will discuss about how to clean an Imbalanced data set.

Macro-management is an important problem in StarCraft, which has been studied for a long time. Various datasets together with assorted methods have been proposed in the last few years. But these datasets have some defects for boosting the academic and industrial research: 1) There're neither standard preprocessing, parsing and feature extraction procedures nor predefined training, validation and test set in some datasets. 2) Some datasets are only specified for certain tasks in macro-management. 3) Some datasets are either too small or don't have enough labeled data for modern machine learning algorithms such as deep neural networks. So most previous methods are trained with various features, evaluated on different test sets from the same or different datasets, making it difficult to be compared directly. To boost the research of macro-management in StarCraft, we release a new dataset MSC based on the platform SC2LE. MSC consists of well-designed feature vectors, pre-defined high-level actions and final result of each match. We also split MSC into training, validation and test set for the convenience of evaluation and comparison. Besides the dataset, we propose a baseline model and present initial baseline results for global state evaluation and build order prediction, which are two of the key tasks in macro-management. Various downstream tasks and analyses of the dataset are also described for the sake of research on macro-management in StarCraft II. Homepage: https://github.com/wuhuikai/MSC.

AI researchers do love their games and two papers have shown that they can use general adversarial networks (GANs) to make old favorites a lot more interesting. In two separate papers, AI researchers built general adversarial networks to construct new video game levels for Super Mario Bros, a popular platform game controlling a mustachioed man in red overalls to collect coins and avoid enemies to reach a princess, and DOOM, the classic first person shooter from the early 1990s. GANs were first introduced in 2014. The system is made up of two networks: a generator and a discriminator. The generator creates fake samples of training data, and a discriminator tries to determine if the samples are real or fake. Both networks spar with one another, and over time the generator learns to forge more realistic samples to trick the discriminator.

From wire to finished product in less than 13 minutes: a new robot-builder is faster than most takeaways. It works by bending wire that already has motors attached into different shapes, using a process its designers call 1D printing. Once the robot has performed its job, it can simply be flattened and fed back into the system to be recycled into a new type of robot. "The idea is that you analyse the current situation, then make a robot on the fly that can deal with it," says Sebastian Risi at the IT University of Copenhagen in Denmark, a member of the team that came up with the system. If you need a robot that can fit through a small space or around an odd-shaped corner, you input those constraints into the software and it will deliver something suitable.