Norms and the normative processes that enforce them such as social maintenance are considered fundamental building blocks of human societies, shaping many aspects of our cognition. However, emerging work argues that the building blocks of normativity emerged much earlier in evolution than previously considered. In light of this, we argue that normative processes must be taken into account to consider the evolution of even ancient processes such as affect. We show through an agent-based model (with an evolvable model of affect) that different affective dispositions emerge when taking into account social maintenance. Further, we demonstrate that social maintenance results in the emergence of a minimal population regulation mechanism in a dynamic environment, without the need to predict the state of the environment or reason about the mental state of others. We use a cultural interpretation of our model to derive a new definition of norm emergence which distinguishes between indirect and direct social maintenance. Indirect social maintenance tends to one equilibrium (similar to environmental scaffolding) and the richer direct social maintenance results in many possible equilibria in behaviour, capturing an important aspect of normative behaviour in that it bears a certain degree of arbitrariness. We also distinguish between single-variable and mechanistic normative regularities. A mechanistic regularity, rather than a particular behaviour specified by one value e.g. walking speed, is a collection of values that specify a culturally patterned version of a psychological mechanism e.g. a disposition. This is how culture reprograms entire cognitive and physiological systems.

The social world is replete with norms, an important aspect Going beyond continuous game theory, Aubert-Kato et al. of organising societies. Social norms reduce the degrees (2015) investigated the emergence of frugal and greedy of freedom in the actions of individuals, making them behaviours in an embodied version of a dilemma where more predictable and stabilising societies (FeldmanHall and agents varied in how long they exploited a food source Shenhav, 2019). Norms also enable unrelated agents to - the longer it exploits the food source, the more selfish manage shared resources (Mathew et al., 2013), thereby the agent is. Michaeli and Spiro (2015) showed how extending cooperation beyond genetic relatives (Richerson "liberal" and "conservative" punishment regimes can affect et al., 2016).

With growing interest in Procedural Content Generation (PCG) it becomes increasingly important to develop methods and tools for evaluating and comparing alternative systems. There is a particular lack regarding the evaluation of generative pipelines, where a set of generative systems work in series to make iterative changes to an artifact. We introduce a novel method called Generative Shift for evaluating the impact of individual stages in a PCG pipeline by quantifying the impact that a generative process has when it is applied to a pre-existing artifact. We explore this technique by applying it to a very rich dataset of Minecraft game maps produced by a set of alternative settlement generators developed as part of the Generative Design in Minecraft Competition (GDMC), all of which are designed to produce appropriate settlements for a pre-existing map. While this is an early exploration of this technique we find it to be a promising lens to apply to PCG evaluation, and we are optimistic about the potential of Generative Shift to be a domain-agnostic method for evaluating generative pipelines.

There are a range of metrics that can be applied to the artifacts produced by procedural content generation, and several of them come with qualitative claims. In this paper, we adapt a range of existing PCG metrics to generated Minecraft settlements, develop a few new metrics inspired by PCG literature, and compare the resulting measurements to existing human evaluations. The aim is to analyze how those metrics capture human evaluation scores in different categories, how the metrics generalize to another game domain, and how metrics deal with more complex artifacts. We provide an exploratory look at a variety of metrics and provide an information gain and several correlation analyses. We found some relationships between human scores and metrics counting specific elements, measuring the diversity of blocks and measuring the presence of crafting materials for the present complex blocks.

This article outlines what we learned from the first year of the AI Settlement Generation Competition in Minecraft, a competition about producing AI programs that can generate interesting settlements in Minecraft for an unseen map. This challenge seeks to focus research into adaptive and holistic procedural content generation. Generating Minecraft towns and villages given existing maps is a suitable task for this, as it requires the generated content to be adaptive, functional, evocative and aesthetic at the same time. Here, we present the results from the first iteration of the competition. We discuss the evaluation methodology, present the different technical approaches by the competitors, and outline the open problems.

Live Action Role-Playing (LARP) games and similar experiences are becoming a popular game genre. Here, we discuss how artificial intelligence techniques, particularly those commonly used in AI for Games, could be applied to LARP. We discuss the specific properties of LARP that make it a surprisingly suitable application field, and provide a brief overview of some existing approaches. We then outline several directions where utilizing AI seems beneficial, by both making LARPs easier to organize, and by enhancing the player experience with elements not possible without AI.

In cognitive science and social psychology Warmth and Competence are considered fundamental dimensions of social There is a large body of work evaluating the perception of cognition, i.e., the social judgment of our peers [1], and interaction with robots. In this paper we are interested [7]. Fiske et al. provide evidence that those dimensions are in understanding which metrics indicate human preferences, universal and reliable for social judgment across stimuli, cultures i.e., which robot a person would choose to interact with and time [1]. People perceived as warm and competent again, if given a choice. Agreeing upon a metric for this elicit uniformly positive emotions [1], are in general more in human-robot interaction (HRI) would provide important favored, and experience more positive interaction with their benefits [2], but raises the question which metric we should peers [6]. The opposite is true for people scoring low on use? The human engagement in an interaction could serve these dimensions, meaning they experience more negative as an indicator for their preference.

Deep reinforcement learning has learned to play many games well, but failed on others. To better characterize the modes and reasons of failure of deep reinforcement learners, we test the widely used Asynchronous Actor-Critic (A2C) algorithm on four deceptive games, which are specially designed to provide challenges to game-playing agents. These games are implemented in the General Video Game AI framework, which allows us to compare the behavior of reinforcement learning-based agents with planning agents based on tree search. We find that several of these games reliably deceive deep reinforcement learners, and that the resulting behavior highlights the shortcomings of the learning algorithm. The particular ways in which agents fail differ from how planning-based agents fail, further illuminating the character of these algorithms. We propose an initial typology of deceptions which could help us better understand pitfalls and failure modes of (deep) reinforcement learning. Introduction In reinforcement learning (RL) (Sutton and Barto 1998) an agent is tasked with learning a policy that maximizes expected reward based only on its interactions with the environment. In general, there is no guarantee that any such procedure will lead to an optimal policy; while convergence proofs exist, they only apply to a tiny and rather uninteresting class of environments. Reinforcement learning still performs well for a wide range of scenarios not covered by those convergence proofs. However, while recent successes in game-playing with deep reinforcement learning (Justesen et al. 2017) have led to a high degree of confidence in the deep RL approach, there are still scenarios or games where deep RL fails. Some oft-mentioned reasons why RL algorithms fail are partial observability and long time spans between actions and rewards. But are there other causes?

At the 2017 Artificial and Computational Intelligence in Games meeting at Dagstuhl, Julian Togelius asked how to make spaces where every way of filling in the details yielded a good game. This study examines the possibility of enriching search spaces so that they contain very high rates of interesting objects, specifically game elements. While we do not answer the full challenge of finding good games throughout the space, this study highlights a number of potential avenues. These include naturally rich spaces, a simple technique for modifying a representation to search only rich parts of a larger search space, and representations that are highly expressive and so exhibit highly restricted and consequently enriched search spaces.

Automatic generation of level maps is a popular form of automatic content generation. In this study, a recently developed technique employing the {\em do what's possible} representation is used to create open-ended level maps. Generation of the map can continue indefinitely, yielding a highly scalable representation. A parameter study is performed to find good parameters for the evolutionary algorithm used to locate high-quality map generators. Variations on the technique are presented, demonstrating its versatility, and an algorithmic variant is given that both improves performance and changes the character of maps located. The ability of the map to adapt to different regions where the map is permitted to occupy space are also tested.