Affect and believability in game characters:a review of the use of affective computing in games

Virtual agents are important in many digital environments. Designing a character that highly engages users in terms of interaction is an intricate task constrained by many requirements. One aspect that has gained more attention recently is the effective dimension of the agent. Several studies have addressed the possibility of developing an affect-aware system for a better user experience. Particularly in games, including emotional and social features in NPCs adds depth to the characters, enriches interaction possibilities, and combined with the basic level of competence, creates a more appealing game. Design requirements for emotionally intelligent NPCs differ from general autonomous agents with the main goal being a stronger player-agent relationship as opposed to problem solving and goal assessment. Nevertheless, deploying an affective module into NPCs adds to the complexity of the architecture and constraints. In addition, using such composite NPC in games seems beyond current technology, despite some brave attempts. However, a MARPO-type modular architecture would seem a useful starting point for adding emotions

“An ethnographic seduction”: how qualitative research and Agent-based models can benefit each other

We provide a general analytical framework for empirically informed agent-based simulations. This methodology provides present-day agent-based models with a sound and proper insight as to the behavior of social agents — an insight that statistical data often fall short of providing at least at a micro level and for hidden and sensitive populations. In the other direction, simulations can provide qualitative researchers in sociology, anthropology and other fields with valuable tools for: (a) testing the consistency and pushing the boundaries, of specific theoretical frameworks; (b) replicating and generalizing results; (c) providing a platform for cross-disciplinary validation of results

Learning relationships from theory to design

This paper attempts to bridge the psychological and anthropological views of situated learning by focusing on the concept of a learning relationship, and by exploiting this concept in our framework for the design of learning technology. We employ Wenger's (1998) concept of communities of practice to give emphasis to social identification as a central aspect of learning, which should crucially influence our thinking about the design of learning environments. We describe learning relationships in terms of form (one‐to‐one, one‐to‐many etc.), nature (explorative, formative and comparative), distance (first‐, second‐order), and context, and we describe a first attempt at an empirical approach to their identification and measurement

Making sense of assets: Community asset mapping and related approaches for cultivating capacities

This working paper critically reviews some main aspects from asset based approaches highlights key strengths and weaknesses for future research/development. Drawing on a large body of reports and relevant literature we draw on different theoretical traditions and critiques, as well as practices and processes embedded within a broad range of approaches including, widely acknowledged frameworks such Asset Based Community Development (ABCD), Appreciative Inquiry (AI), Sustainable Livelihood Approaches (SLA) and Community Capitals Framework (CCF). Although these are presented as distinct approaches, there is a sense of evolution through them and many of them overlap (in terms of both theories and methodologies). We also include emerging frameworks, including geographical, socio-spatial, visual and creative approaches, stemming from a number of projects within AHRC’s Connected Communities programme and additional collaborations

First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

Mean-Field Theory of Meta-Learning

We discuss here the mean-field theory for a cellular automata model of meta-learning. The meta-learning is the process of combining outcomes of individual learning procedures in order to determine the final decision with higher accuracy than any single learning method. Our method is constructed from an ensemble of interacting, learning agents, that acquire and process incoming information using various types, or different versions of machine learning algorithms. The abstract learning space, where all agents are located, is constructed here using a fully connected model that couples all agents with random strength values. The cellular automata network simulates the higher level integration of information acquired from the independent learning trials. The final classification of incoming input data is therefore defined as the stationary state of the meta-learning system using simple majority rule, yet the minority clusters that share opposite classification outcome can be observed in the system. Therefore, the probability of selecting proper class for a given input data, can be estimated even without the prior knowledge of its affiliation. The fuzzy logic can be easily introduced into the system, even if learning agents are build from simple binary classification machine learning algorithms by calculating the percentage of agreeing agents.Comment: 23 page

The value of theoretical multiplicity for steering transitions towards sustainability

Transition management, as a theory of directing structural societal changes towards sustainable system innovations, has become a major topic in scientific research over the last years. In this paper we focus on the question how transitions towards sustainability can be steered, governed or managed, in particular by governmental actors. We suggest an approach of theoretical multiplicity, arguing that multiple theories will be needed simultaneously for dealing with the complex societal sustainability issues. Therefore, we address the steering question by theoretically comparing transition management theory to a number of related theories on societal change and intervention, such as multi-actor collaboration, network governance, configuration management, policy agenda setting, and adaptive management. We conclude that these related theories put the managerial assumptions of transition management into perspective, by adding other steering roles and leadership mechanisms to the picture. Finally we argue that new modes of steering inevitable have consequences for the actual governance institutions. New ways of governing change ask for change within governance systems itself and vice versa. Our argument for theoretical multiplicity implicates the development of multiple, potentially conflicting, governance capacitie