User Intention Modelling and Intention Recognition in Games using World State Indicators

The work presented in this thesis focuses on two main areas. First we develop a model of user intentions in games. That model defines various concepts related to player behaviour in (computer) games and interactive environments, such as actions, goals, plans and intentions. Additionally our model shows the relationship between those concepts and explains how they affect each other. The purpose of the model presented here is twofold. One the one hand it provides common definitions for research in the area of user behaviour modelling in games. On the other hand this model also forms the underlying basis for the remainder of our work presented here. The second main area of focus is intention recognition in games. We propose a novel approach which is solely based on monitoring the changes in the game world state, instead of observing player actions. We evaluate current approaches to plan and intention recognition, their strengths and weaknesses. We further compare existing research on intention recognition to our approach and evaluate the performance of our prototype system iRecognise in the context of a case study using the board game RISK. A range of experiments that were carried out demonstrates that our proposed approach to intention recognition is valid and therefore verifies its intention recognition capabilities in the context of games

AI Researchers, Video Games Are Your Friends!

If you are an artificial intelligence researcher, you should look to video games as ideal testbeds for the work you do. If you are a video game developer, you should look to AI for the technology that makes completely new types of games possible. This chapter lays out the case for both of these propositions. It asks the question "what can video games do for AI", and discusses how in particular general video game playing is the ideal testbed for artificial general intelligence research. It then asks the question "what can AI do for video games", and lays out a vision for what video games might look like if we had significantly more advanced AI at our disposal. The chapter is based on my keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad audience.Comment: in Studies in Computational Intelligence Studies in Computational Intelligence, Volume 669 2017. Springe

Using a Cognitive Architecture for Opponent Target Prediction

One of the most important aspects of a compelling game AI is that it anticipates the player’s actions and responds to them in a convincing manner. The first step towards doing this is to understand what the player is doing and predict their possible future actions. In this paper we show an approach where the AI system focusses on testing hypotheses made about the player’s actions using an implementation of a cognitive architecture inspired by the simulation theory of mind. The application used in this paper is to predict the target that the player is heading towards, in an RTS-style game. We improve the prediction accuracy and reduce the number of hypotheses needed by using path planning and path clustering

CAPIR: Collaborative Action Planning with Intention Recognition

We apply decision theoretic techniques to construct non-player characters that are able to assist a human player in collaborative games. The method is based on solving Markov decision processes, which can be difficult when the game state is described by many variables. To scale to more complex games, the method allows decomposition of a game task into subtasks, each of which can be modelled by a Markov decision process. Intention recognition is used to infer the subtask that the human is currently performing, allowing the helper to assist the human in performing the correct task. Experiments show that the method can be effective, giving near-human level performance in helping a human in a collaborative game.Comment: 6 pages, accepted for presentation at AIIDE'1

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

Best practices for deploying digital games for personal empowerment and social inclusion

Digital games are being increasingly used in initiatives to promote personal empowerment and social inclusion (PESI) of disadvantaged groups through learning and participation. There is a lack of knowledge regarding best practices, however. The literature on game-based learning insufficiently addresses the process and context of game-based practice and the diversity of contexts and intermediaries involved in PESI work. This paper takes an important step in addressing this knowledge gap using literature review, case studies, and expert consultation. Based on our findings, we formulate a set of best practices for different stakeholders who wish to set up a project using digital games for PESI. The seven cases in point are projects that represent various application domains of empowerment and inclusion. Case studies were conducted using documentation and interviews, covering background and business case, game format/technology, user groups, usage context, and impact assessment. They provide insight into each case’s strengths and weaknesses, allowing a meta-analysis of the important features and challenges of using digital games for PESI. This analysis was extended and validated through discussion at two expert workshops. Our study shows that a substantial challenge lies in selecting or designing a digital game that strikes a balance between enjoyment, learning and usability for the given use context. The particular needs of the target group and those that help implement the digital game require a highly specific approach. Projects benefit from letting both intermediaries and target groups contribute to the game design and use context. Furthermore, there is a need for multi-dimensional support to facilitate the use and development of game-based practice. Integrating game use in the operation of formal and informal intermediary support organiszations increases the chances at reaching, teaching and empowering those at risk of exclusion. The teachers, caregivers and counsellors involved in the implementation of a game-based approach, in turn can be helped through documentation and training, in combination with structural support