Corrado Grappiolo

Controllable procedural map generation via multiobjective evolution

This paper shows how multiobjective evolutionary algorithms can be used to procedurally generate complete and playable maps for real-time strategy (RTS) games. We devise heuristic objective functions that measure properties of maps that impact important aspects of gameplay experience. To show the generality of our approach, we design two different evolvable map representations, one for an imaginary generic strategy game based on heightmaps, and one for the classic RTS game StarCraft. The effect of combining tuples or triples of the objective functions are investigated in systematic experiments, in particular which of the objectives are partially conflicting. A selection of generated maps are visually evaluated by a population of skilled StarCraft players, confirming that most of our objectives correspond to perceived gameplay qualities. Our method could be used to completely automate in-game controlled map generation, enabling player-adaptive games, or as a design support tool for human designers.

A computational approach towards conflict resolution for serious games

Conflict is an unavoidable feature of life, but the development of conflict resolution management skills can facilitate the parties involved in resolving their conflicts in a positive manner. The goal of our research is to develop a serious game in which children may experiment with conflict resolution strategies and learn how to work towards positive conflict outcomes. While serious games related to conflict exist at present, our work represents the first attempt to teach conflict resolution skills through a game in a manner informed by sociological and psychological theories of conflict and current best practice for conflict resolution. In this paper, we present a computational approach to conflict generation and resolution. We describe the five phases involved in our conflict modeling process: conflict situation creation, conflict detection, player modeling and conflict strategy prediction, conflict management, and conflict resolution, and discuss the three major elements of our player model: assertiveness, cooperativeness, and relationship. Finally, we overview a simple resource management game we have developed in which we have begun experimenting with our conflict model concepts.

Towards Player Adaptivity in a Serious Game for Conflict Resolution

We present a technology demonstrator for an adaptive serious game for teaching conflict resolution and discuss the research questions associated with the project. The prototype is a single-player 3D mini-game which simulates a resource management conflict scenario. In order to teach the player how to resolve this type of conflict, the underlying system generates level content automatically which adapts to player experience and behaviour. Preliminary results demonstrate the efficiency of the procedural content generation mechanism in guiding the training of players towards targeted learning objectives.

Artificial evolution for the detection of group identities in complex artificial societies

This paper aims at detecting the presence of group structures in complex artificial societies by solely observing and analysing the interactions occurring among the artificial agents. Our approach combines: (1) an unsupervised method for clustering interactions into two possible classes, namely in-group and out-group, (2) reinforcement learning for deriving the existing levels of collaboration within the society, and (3) an evolutionary algorithm for the detection of group structures and the assignment of group identities to the agents. Under a case study of static societies - i.e. the agents do not evolve their social preferences - where agents interact with each other by means of the Ultimatum Game, our approach proves to be successful for small-sized social networks independently on the underlying social structure of the society; promising results are also registered for mid-size societies.

Interaction-based group identity detection via reinforcement learning and artificial evolution

We present a computational framework capable of inferring the existence of group identities, built upon social networks of reciprocal friendship, in Complex Adaptive Artificial Societies (CAAS) by solely observing the flow of interactions occurring among the agents. Our modelling framework infers the group identities by following two steps: first, it aims to learn the ongoing levels of cooperation among the agents and, second, it applies evolutionary computation, based on the learned cooperation values, to partition the agents into groups and assign group identities to the agents. Experimental investigations, based on CAAS of agents who interact with each other by means of the Ultimatum (or Bargain) Social Dilemma Game, show that a cooperation learning phase, based on Reinforcement Learning, can provide highly promising results for minimising the mismatch between the existing and the inferred group identities. The proposed method appears to be robust independently of the size and the ongoing social dynamics of the societies.

Towards Detecting Group Identities in Complex Artificial Societies

This paper presents a framework for modelling group structures and dynamics in both artificial societies and human-populated virtual environments such as computer games. The group modelling (GM) framework proposed focuses on the detection of existing, pre-defined group structures and is composed of a reinforcement learning method that infers collaboration values from the society’s local interactions and a clustering algorithm that detects group identities based on the learned collaboration values. An empirical evaluation of the framework in the social ultimatum bargain game shows that the GM method proposed is robust independently of the size of the society and the locality of the interactions.

Shifting niches for community structure detection

We present a new evolutionary algorithm for community structure detection in both undirected and unweighted (sparse) graphs and fully connected weighted digraphs (complete networks). Previous investigations have found that, although evolutionary computation can identify community structure in complete networks, this approach seems to scale badly due to solutions with the wrong number of communities dominating the population. The new algorithm is based on a niching model, where separate compartments of the population contain candidate solutions with different numbers of communities. We experimentally compare the new algorithm to the well-known algorithms of Pizzuti and Tasgin, and find that we outperform those algorithms for sparse graphs under some conditions, and drastically outperform them on complete networks under all tested conditions.

Modelling Global Pattern Formations for Collaborative Learning Environments

We present our research towards the design of a computational framework capable of modelling the formation and evolution of global patterns (i.e. group structures) in a population of social individuals. The framework is intended to be used in collaborative environments, e.g. social serious games and computer simulations of artificial societies. The theoretical basis of our research, together with current state of the art and future work, are briefly introduced.

Using reinforcement learning and artificial evolution for the detection of group identities in complex adaptive artificial societies

We present a computational framework capable of inferring the existence of groups, built upon social networks of re- ciprocal friendship, in Complex Adaptive Artificial Societies (CAAS). Our modelling framework infers the group identi- ties by following two steps: first, it aims to learn the on- going levels of cooperation among the agents and, second, it applies evolutionary computation, based on the learned cooperation values, to partition the agents into groups. Ex- perimental investigations, based on CAAS of agents who interact with each other by means of the Ultimatum Game, show that a cooperation learning phase, based on Reinforce- ment Learning, can provide highly promising results for min- imising the mismatch between the existing and the inferred groups, for two different society sizes under investigation.

Validating Generic Metrics of Fairness in Game-Based Resource Allocation Scenarios with Crowdsourced Annotations

Being able to effectively measure the notion of fairness is of vital importance as it can provide insight into the formation and evolution of complex patterns and phenomena, such as social preferences, collaboration, group structures and social conflicts. This paper presents a comparative study for quantitatively modelling the notion of fairness in one-to-many resource allocation scenarios -- i.e. one provider agent has to allocate resources to multiple receiver agents. For this purpose, we investigate the efficacy of six metrics and cross-validate them on crowdsourced human ranks of fairness annotated through a computer game implementation of the one-to-many resource allocation scenario. Four of the fairness metrics examined are well-established metrics of data dispersion, namely standard deviation, normalised entropy, the Gini coefficient and the fairness index. The fifth metric, proposed by the authors, is an ad-hoc context-based measure which is based on key aspects of distribution strategies. The sixth metric, finally, is machine learned via ranking support vector machines SVMs on the crowdsourced human perceptions of fairness. Results suggest that all ad-hoc designed metrics correlate well with the human notion of fairness, and the context-based metrics we propose appear to have a predictability advantage over the other ad-hoc metrics. On the other hand, the normalised entropy and fairness index metrics appear to be the most expressive and generic for measuring fairness for the scenario adopted in this study and beyond. The SVM model can automatically model fairness more accurately than any ad-hoc metric examined with an accuracy of 81.86% but it is limited by its expressivity and generalisability.