In previous research we concluded that a personality profile, based on the Five Factor Model, can be constructed from observations of a player’s behavior in a module that we designed for Neverwinter Nights (Lankveld et al. 2011a). In the present research, we investigate whether we can do the same thing in an actual modern commercial video game, in this case the game Fallout 3. We stored automatic observations on 36 participants who played the introductory stages of Fallout 3. We then correlated these observations with the participants’ personality profiles, expressed by values for five personality traits as measured by the standard NEO-FFI questionnaire. Our analysis shows correlations between all five personality traits and the game observations. These results validate and generalize the results from our previous research (Lankveld et al. 2011a). We may conclude that Fallout 3, and by extension other modern video games, allows players to express their personality, and can therefore be used to create personality profiles.

Serious games offer an opportunity for players to learn communication skills by practicing conversations with nonplaying characters (NPCs). To realize this potential, the player needs freedom of play to discover the relationships between its actions and their effects on the partner and the conversation. Scripting is currently the common approach to design in-game dialogue. Although scripting is a robust technique, the approach tends to produce deterministic conversations, allowing little control to the player. It is claimed that a Belief-Desire-Intention (BDI) approach to model the behavior of NPCs allows greater freedom to the player, and delivers better scalability and re-use of dialogues. This claim is evaluated by using BDI in the development of a sales-talk training game in the real-estate domain. It is concluded that BDI enables representative NPCs that respond appropriately and the game allows the player its freedom of choice to explore. The results also showed that BDI brings about new challenges to address, in order to further increase the quality of in-game dialogue.

Recent development in game AI has seen action planning and its derivates being adapted for controlling agents in classical types of games, such as FPSs or RPGs. Complementary, one can seek new types of gameplay elements inspired by planning. We propose and formally define a new game "genre" called anticipation games and demonstrate that planning can be used as their key concept both at design time and run time. In an anticipation game, a human player observes a computer controlled agent or agents, tries to predict their actions and indirectly helps them to achieve their goal. The paper describes an example prototype of an anticipation game we developed. The player helps a burglar steal an artifact from a museum guarded by guard agents. The burglar has incomplete knowledge of the environment and his plan will contain pitfalls. The player has to identify these pitfalls by observing burglar's behavior and change the environment so that the burglar replans and avoids the pitfalls. The game prototype is evaluated in a small-scale human-subject study, which suggests that the anticipation game concept is promising.

Location-based games are an emerging paradigm fortraining, simulation, entertainment, health and many other domains. In this paper, we consider the role of artificialagents in such games. We also examine how human teams perform when given the same game, playedin both a real environment with mobile devices and alsoin a virtual environment that replicates the real environment.We perform the first direct comparison of real andvirtual instantiations of the same location-based game.We show the similarities and differences in game playand then investigate how adding an advice-giving agentchanges the experience.

What features contribute to player enjoyment and player retentionhas been a popular research topic in video games research;however, the question of what causes players to quit agame has received little attention by comparison. In this paper,we examine 5 quantitative features of the game Scrabblesquein order to determine what behaviors are predictors ofa player prematurely ending a game session. We identified afeature transformation that notably improves prediction accuracy.We used a naive Bayes model to determine that there areseveral transformed feature sequences that are accurate predictorsof players terminating game sessions before the endof the game.We also identify several trends that exist in thesesequences to give a more general idea as to what behaviorsare characteristic early indicators of players quitting.

Feedback on player experience and behaviour can be invaluable to game designers, but there is need for specialised knowledge discovery tools to deal with high volume playtest data. We describe a study witha commercial third-person shooter, in which integrated player activity and experience data was captured and mined for design-relevant knowledge. We demonstrate that association rule learning and rule templates can be used to extractmeaningful rules relating player activity and experience during combat. We found that the number, type and quality of rules varies between experiences, and is affected by feature distributions. Further work is required on rule selection and evaluation.

Characters are vital to large video game worlds as they bring a sense of life to the world. However, background characters are known to rarely exhibit any sign of motivated behavior or emotional state. We want to change this by assigning these characters emotions that can be identified through their non-verbal behavior. We feel the addition of emotion will allow players to feel more connected to the game world and make the game world more believable. This paper presents the results of an experiment to test two ways of conveying emotion: 1) through a character's gait and 2) through a character's interactions with the game world. Results from the experiment suggest that a combination of gait and interactions is the most effective method to convey emotion.

We describe ANGELINA3, a system that can automatically develop games along a defined theme, by selecting appropriate multimedia content from a variety of sources and incorporating it into a game's design. We discuss these capabilities in the context of the FACE model for assessing progress in the building of creative systems, and discuss how ANGELINA3 can be improved through further work.

Non-player character diversity enriches game environments increasing their replay value. We propose a method for obtaining character behavior diversity based on the diversity of plans enacted by characters, and demonstrate this method in a scenario in which characters have multiple choices. Using case-based planning techniques, we reuse plans for varied character behavior, which simulate different personality traits.

Heuristic search has been very successful in abstract game domains such as Chess and Go. In video games, however, adoption has been slow due to the fact that state and move spaces are much larger, real-time constraints are harsher, and constraints on computational resources are tighter. In this paper we present a fast search method — Alpha-Beta search for durative moves— that can defeat commonly used AI scripts in RTS game combat scenarios of up to 8 vs. 8 units running on a single core in under 5ms per search episode. This performance is achieved by using standard search enhancements such as transposition tables and iterative deepening, and novel usage of combat AI scripts for sorting moves and state evaluation via playouts. We also present evidence that commonly used combat scripts are highly exploitable — opening the door for a promising line of research on opponent combat modelling.