A method for automatic situation recognition in collaborative multiplayer Serious Games

EAI Endorsed Transactions on Serious Games Research Article A method for automatic situation recognition in collaborative multiplayer Serious Games Viktor Wendel1,∗, Marc-André Bär1, Robert Hahn1, Benedict Jahn1, Max Mehltretter1, Stefan Göbel1, Ralf Steinmetz1 1Technische Universität Darmstadt, Multimedia Communications Lab, Darmstadt, Germany Abstract One major Serious Games challenge is adaptation of game-based learning environments towards the needs of players with heterogeneous player and learner traits. For both an instructor or an algorithmic adaptation mechanism it is vital to have knowledge about the course of the game in order to be able to recognize player intentions, potential problems, or misunderstandings - both of the game(play) and the learning content. The main contribution of this paper is a mechanism to recognize high-level situations in a multiplayer Serious Game. The approach presented uses criteria and situations based on the game-state, player actions and events and calculates how likely it is that players are in a certain situation. The gathered information can be used to feed an adaptation algorithm or be presented to the instructor to improve instructor decision making. In a first evaluation, the situation recognition was able to correctly recognize all of the situations in a set of game sessions. Thus, the contribution of this paper contains a novel approach to automatically capture complex multiplayer game states influenced b y u npredictable p layer b ehavior, a nd t o i nterpret t hat i nformation to calculate probabilities of relevant game situations to be present from which player intentions can be derived. Received on 19 December 2014; accepted on 02 June 2015; published on 02 July 2015 Keywords: Serious Games, Collaborative Learning, Game Mastering, Adaptation Copyright © 2015 Viktor Wendel et al., licensed to ICST. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited. doi:10.4108/sg.1.4.e4 1. Motivation Especially for game-based collaborative learning scenarios, major fields of research are adaptation of learning content, difficulty, as well as game-pace and content. First approaches to address problems in those fields have been proposed, focusing on human instructor support and Game Mastering. Many of those approaches consider what information needs to be presented to the instructor and what adaptation mechanisms are necessary and should be regarded. A different approach is automatic adaptation of multiplayer Serious Games. Any adaptation algorithm, however, needs knowledge about the game state, the learner/player state, and player progress and behavior in order to be able to decide about proper adaptations for the present game state. In particular, an algorithm needs to be aware of problems and misunderstandings ∗ Corresponding author. Email: during the game. Whereas a human Game Master can rather easily recognize and judge what a player or a group of players is doing at a certain moment during the game session just by observing the scene, his/her background knowledge of the game, and human reasoning, this is extremely difficult to recognize automatically. Especially in game genres where players control an avatar in a rather open world and where they can move freely in that world, deciding for themselves about what to do next and how, it is a challenge to automatically recognize what a player - or a team - is doing at a certain moment. Examples for this are Second Life1 , mods of commercial role-playing games, or collaborative multiplayer Serious Games like Woodment [1] or Escape From Wilson Island [2]. The scenario observed here focuses on non-scenebased open world action-adventure-like games using 1 secondlife.com/ 1 EAI Endorsed Transactions on Serious Games 08 2014 – 07 2015 | Volume 1 | Issue 4 | e4 V. Wendel et al. avatars to (re-)present players. In this paper, we propose an approach for automatic situation recognition in multiplayer Serious Games. The goal is to automatically recognize what a single player or a group of players are likely doing at a certain point in a game, based on information about their locations, their movements, their actions, and their interactions. Therefore, an interface is defined to access elementary and abstract game states, current and past player actions, game quests and (learning) tasks, and game relevant attributes. Based on this, state diagrams regarding dependencies between states are created and different kinds of criteria to calculate probabilities of situations are used. Criteria are defined based on space, time, and state, like local or global criteria, distance criteria, or criteria based on game states or attributes. The concept further includes an algorithm which calculates which criteria are fulfilled using the data gathered from the game. Based on that, the algorithm calculates probabilities for players being in certain situations, like trying to solve a certain task or exploring the level, etc. We implemented our concept as an extension of the existing collaborative multiplayer Serious Game Escape From Wilson Island (EFWI) which offers group tasks designed in a way such that players need to work together and communicate in order to succeed [2]. A Game Master Frontend has been implemented to enable an instructor to perform instructor tasks from inside the game at run-time [3]. EFWI is a game with an open level in which multiple players can act freely and concurrently, hence altering the game in an often unpredictably way. Therefore, it is considered to be well suited as an evaluation platform for the situation recognition concept presented here. The situation recognition described in this paper is a direct extension to this Game Master framework with the objective of enabling the Game Master framework to automatically recognize game relevant situation in order to inform and support the Game Master and automatically perform adaptations based on the recognized situations. An initial study to evaluate the soundness and correctness of our approach has been carried out comparing the recognized situations with the situations recognized by a real GM. The initial results are very promising. The situation recognition was able to correctly recognize the defined game situations in all cases. However, further evaluation is required including a bigger set of users as well as additional games. Thus, the contribution of this paper contains a novel approach to automatically capture complex multiplayer game states influenced by unpredictable player behavior, and to interpret that information to calculate probabilities of relevant game situations to be present from which player intentions can be derived. 2. Related Work To the best of our knowledge, there are no similar appr (...truncated)