Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

A Planning-based Approach for Music Composition

. Automatic music composition is a fascinating field within computational creativity. While different Artificial Intelligence techniques have been used for tackling this task, Planning – an approach for solving complex combinatorial problems which can count on a large number of high-performance systems and an expressive language for describing problems – has never been exploited. In this paper, we propose two different techniques that rely on automated planning for generating musical structures. The structures are then filled from the bottom with “raw” musical materials, and turned into melodies. Music experts evaluated the creative output of the system, acknowledging an overall human-enjoyable trait of the melodies produced, which showed a solid hierarchical structure and a strong musical directionality. The techniques proposed not only have high relevance for the musical domain, but also suggest unexplored ways of using planning for dealing with non-deterministic creative domains

Item Recommendation with Evolving User Preferences and Experience

Current recommender systems exploit user and item similarities by collaborative filtering. Some advanced methods also consider the temporal evolution of item ratings as a global background process. However, all prior methods disregard the individual evolution of a user's experience level and how this is expressed in the user's writing in a review community. In this paper, we model the joint evolution of user experience, interest in specific item facets, writing style, and rating behavior. This way we can generate individual recommendations that take into account the user's maturity level (e.g., recommending art movies rather than blockbusters for a cinematography expert). As only item ratings and review texts are observables, we capture the user's experience and interests in a latent model learned from her reviews, vocabulary and writing style. We develop a generative HMM-LDA model to trace user evolution, where the Hidden Markov Model (HMM) traces her latent experience progressing over time -- with solely user reviews and ratings as observables over time. The facets of a user's interest are drawn from a Latent Dirichlet Allocation (LDA) model derived from her reviews, as a function of her (again latent) experience level. In experiments with five real-world datasets, we show that our model improves the rating prediction over state-of-the-art baselines, by a substantial margin. We also show, in a use-case study, that our model performs well in the assessment of user experience levels

Generative Design in Minecraft (GDMC), Settlement Generation Competition

This paper introduces the settlement generation competition for Minecraft, the first part of the Generative Design in Minecraft challenge. The settlement generation competition is about creating Artificial Intelligence (AI) agents that can produce functional, aesthetically appealing and believable settlements adapted to a given Minecraft map - ideally at a level that can compete with human created designs. The aim of the competition is to advance procedural content generation for games, especially in overcoming the challenges of adaptive and holistic PCG. The paper introduces the technical details of the challenge, but mostly focuses on what challenges this competition provides and why they are scientifically relevant.Comment: 10 pages, 5 figures, Part of the Foundations of Digital Games 2018 proceedings, as part of the workshop on Procedural Content Generatio

Assessment of generative strategies in self-support groups in people affected by the Colombian armed political conflict

The paper presents a research applied from an appreciative approach that aimed to assess the use of generative strategies in self-support groups in a population affected by the armed political conflict in Colombia. The generative approach intends to build a notion of desirable and possible future, by means of dialogic strategies. These dialogues strengthen community ties, show new action strategies and stimulate metaphors in relation to violent affectation amid the conflict. The research sample was formed by 11 people divided into two groups, residents in the city of Bucaramanga, Colombia. The advantaged actions inside the self-support groups were the identification of the discursive links among the participants, the clarification of the thematic nodes among them, the selection of metaphors and the transformation of narratives. The results point out that the intervention generated the observation and identification of resources, action planning, search of new experiences, reflexivity,and recognition of the novelty to transform conflict affectation. It is concluded that through self-support groups, people transform the meaning of their experience by sharing stories and action resources for the future, which verifies the purposes of the generative approach. The psychologist was a facilitator of the process amid the relevance of languageas a means of transformation. It was also concluded that through self-support groups emerges the truth of a conflict through public practices by telling the stories of affectation.Generative approach; Self-support Group; Language; Colombia; Narrative