A large class of systems of biological and technological relevance can be described as analog networks, that is, collections of dynamic devices interconnected by links of varying strength. Some examples of analog networks are genetic regulatory networks, metabolic networks, neural networks, analog electronic circuits, and control systems. Analog networks are typically complex systems that include nonlinear feedback loops and possess temporal dynamics at different time scales. Both the synthesis and reverse engineering of analog networks are recognized as knowledge-intensive activities, for which few systematic techniques exist. In this paper we will discuss the general relevance of the analog network concept and describe an evolutionary approach to the automatic synthesis and the reverse engineering of analog networks.

Soil characteristics are extremely important when determining yield potential. Fertilization and liming are commonly used to adapt soils to the nutritional requirements of the crops to be cultivated. Planting the crop that will best fit the soil characteristics is an interesting alternative to minimize the need for soil treatment, reducing costs and potential environmental damages. In addition, farmers usually look for investments that offer the greatest potential earnings with the least possible risks. Regarding the objectives to be considered, the crop-selection problem may be difficult to solve using traditional tools.

Neuromorphic, evolutionary, or fuzzylike systems have been developed by many research groups in the Spanish computer sciences. It is no surprise, then, that these naturegrounded efforts start to emerge, enriching the AI catalogue of research projects and publications and, eventually, leading to new directions of basic or applied research. In this article, we review the contribution of Melomics in computational creativity. In Spain there are 74 universities, many of which have computer science departments that host AIrelated research groups. AEPIA, the Spanish society for AI research, was founded in 1983 and has been vigorously promoting the advancement of AI since then. Along with several other societies and communities of interest, it promotes various periodic conferences and workshops. The Artificial Intelligence Research Institute (IIIA) of the Spanish National Research Council constitutes one of the flagships of local AI research. Ramón López de Mántaras, IIIA's renowned director, was one of the pioneers of AI in Spain, and he also was the recipient of the prestigious AAAI Englemore Award in 2011. Other researchers that have reached an outstanding position, and lead important research groups in Spain, include Antonio Bahamonde (University of Oviedo), Federico Barber (Polytechnic University of Madrid), Vicent Botti (Polytechnic University of Valencia), and Amparo Vila (University of Granada). This department, with more than one hundred faculty members, is organized in several research groups, three of which maintain active AI research lines. Melomics is a new approach in artificial creativity (for a perspective on this discipline, see the 2009 fall issue of AI Magazine). More specifically, it focuses on algorithmic composition and aims at the full automation of the composition process of professional music.

It's an aspect of normal human intelligence, not a special faculty granted to a tiny elite. There are three forms: combinational, exploratory, and transformational. All three can be modeled by AI--in some cases, with impressive results. AI techniques underlie various types of computer art. Whether computers could "really" be creative isn't a scientific question but a philosophical one, to which there's no clear answer.

Unlike other entrants that fashioned good softbot teams from a battery of relatively wellunderstood robotics techniques, our goal was to see if it was even possible to use evolutionary computation to develop high-level soccer behaviors that were competitive with the human-crafted strategies of other teams. Although evolutionary computation has been successful in many fields, evolving a computer algorithm has proven challenging, especially in a domain such as robot soccer. Our approach was to evolve a population of teams of Lisp s-expression algorithms, evaluating each team by attaching its algorithms to robot players and trying them out in the simulator. Early experiments tested individual players, but ultimately, the final runs pitted whole teams against each other using coevolution. After evaluation, a team's fitness assessment was based on its success relative to its opponent.

The decision-making process of computer-controlled opponents in video games is called game AI. Adaptive game AI can improve the entertainment value of games by allowing computer-controlled opponents to fix weaknesses automatically in the game AI and to respond to changes in human-player tactics. Dynamic scripting is a reinforcement learning approach to adaptive game AI that learns, during gameplay, which game tactics an opponent should select to play effectively. In previous work, the tactics used by dynamic scripting were designed manually. We introduce the evolutionary state-based tactics generator (ESTG), which uses an evolutionary algorithm to generate tactics automatically.

Equipping machine learning models with ethical and legal constraints is a serious issue; without this, the future of machine learning is at risk. This paper takes a step forward in this direction and focuses on ensuring machine learning models deliver fair decisions. In legal scholarships, the notion of fairness itself is evolving and multi-faceted. We set an overarching goal to develop a unified machine learning framework that is able to handle any definitions of fairness, their combinations, and also new definitions that might be stipulated in the future. To achieve our goal, we recycle two well-established machine learning techniques, privileged learning and distribution matching, and harmonize them for satisfying multi-faceted fairness definitions. We consider protected characteristics such as race and gender as privileged information that is available at training but not at test time; this accelerates model training and delivers fairness through unawareness. Further, we cast demographic parity, equalized odds, and equality of opportunity as a classical two-sample problem of conditional distributions, which can be solved in a general form by using distance measures in Hilbert Space. We show several existing models are special cases of ours. Finally, we advocate returning the Pareto frontier of multi-objective minimization of error and unfairness in predictions. This will facilitate decision makers to select an operating point and to be accountable for it.

Recently, a new class of machine learning algorithms has emerged, where models and discriminators are generated in a competitive setting. The most prominent example is Generative Adversarial Networks (GANs). In this paper we examine how these algorithms relate to the Turing test, and derive what - from a Turing perspective - can be considered their defining features. Based on these features, we outline directions for generalizing GANs - resulting in the family of algorithms referred to as Turing Learning. One such direction is to allow the discriminators to interact with the processes from which the data samples are obtained, making them "interrogators", as in the Turing test. We validate this idea using two case studies. In the first case study, a computer infers the behavior of an agent while controlling its environment. In the second case study, a robot infers its own sensor configuration while controlling its movements. The results confirm that by allowing discriminators to interrogate, the accuracy of models is improved.