PATRONATO NACIONAL DE LA INFANCIA

The content from the literature review contains a diverse amount of interesting information that should be discussed to gain further understanding and comprehension of the subject matter. This process will verify if a potential gap in the literature exists and if appropriate research can be conducted to fill it.

It is obvious from the literature that the player's experience when playing a First Person Shooter video game is an important aspect to consider when designing elements of FPS bot AI. Considering the player's point of view is important as ultimately the players should have a somewhat valid understanding of what they do or do not find enjoyable in FPS games. However, it is also important to consider participant and researcher expectation bias when exploring topics related to participant preference, especially in any research done directly related to this project. This is particularly true when considering player's evaluations of and preferences for bot AI in existing research, as well as their preferences for playing against other human players.

Immersion and GameFlow are topics directly related to the level of enjoyment a player achieves when playing games. It is necessary to consider the effects that certain game related systems can have on player's immersion levels. The effects of immersion will implicate the design of several aspects of this research, from possible studies to conceptual mechanisms relating to bot AI. How players interact with, process and respond to game related stimuli should be evaluated critically in order

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formulate a solid understanding of player decision making processes. Recognising player skill and cognitive processes will assist in identifying which aspects of human behaviour are lacking in current bot AI implementations, if any. If aspects of player behaviour and decision making processes are to be integrated into a working bot technology, some understanding of how humans interact with and perform in video games will be necessary.

It is also obvious that significant research has been conducted in the last decade regarding improving bot AI from a technological standpoint. While Finite State Machines still seem like a reliable means for creating core AI behaviour, the need for more advanced behaviours to be built on top of these fundamental architectures is becoming ever more apparent. Various technologies have been applied to bot AI, often in conjunction, which has resulted in many interesting but varying results. While the primary technologies of Evolutionary Algorithms, Neural Networks, Machine Learning and Player Modelled Scripting all have noticeable benefits, none of these technologies stand out as a superior method for improving bot AI in commercial video games. It is also wise to consider some of the drawbacks and limitations of current bot AI technology, such as the lack of embodiment, emergent decision making and their practical implementation in modern video games. Until these concerns are addressed, it is unlikely that completely player-like bot AI behaviour can be achieved.

2.8.1 Understanding the Gap

After careful consideration, it seems the most apparent gap in the research literature appears to be research that considers modelling some aspect of player's cognitive processes during decision making, particularly those affected by individual skill and experience. Replicating cognition-based decision making and skill in FPS games suggests an avenue for implementing some measure of embodiment and self- awareness into bot AI. As evidenced by many researchers throughout the literature, aspects of player-like behaviour related to cognitive and skill-based activities are viewed to be missing in bot implementations. Specifically, observations made concerning navigation and map control (Gorman & Thurau, 2006; Laird, 2001; Thurau et al., 2003), collecting health (Bauckhage et al., 2007; Bauckhage et al., 2003; McPartland & Gallagher, 2012), having specialised circumstantial weapon

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choices (Bauckhage & Thurau, 2004), reacting to external environmental stimuli (Arrabales et al., 2009; Bauckhage et al., 2007; Bauckhage & Thurau, 2004; Priesterjahn, 2007) and generally being designed to behave more like humans (Hingston, 2009; McPartland & Gallagher, 2012) were seen to be ideal behaviours for bot AI to employ.

The research explored in the Literature Review that has both a focus on the player experience and also explores modelling aspects of human behaviour appear to have interesting and promising results. The literature suggests that there are niche components of human psychological and cognitive behaviours that are likely overlooked when developing bot AI. This suggestion is apparent because there appears to be a lack of research which adequately addresses and acknowledges it. While there is a plethora of research regarding both the player experience and improving bot AI technologies, very little has been done in-between to bridge this gap. This is mostly due to the very small amount of overlap these domains have traditionally had with each other regarding this issue, despite the obvious contextual similarities contained within them. It would therefore be interesting to create a practical but simplistic bot AI that has some element of these missing but identifiable human behaviours. Creating a model that mimics some aspect of player skill and cognition and integrating it into a bot AI would be an effective approach towards breaching this gap. This relationship is detailed in Figure 2.7. It is expected that a bot employing this behaviour will provide both a more realistic and less predictable opponent, resulting in more enjoyable game play.

Figure 2.7 - Bot Model Overlay The Player

Experience _Technology Bot AI

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3 Approach

This chapter describes how the research is to be approached in retrospect to the Literature Review and project's aims as well as further exploring the research questions. As stated previously, the aims of this research are:

Aim 1: To formulate an understanding of player interactions with bot and humans in a competitive FPS context.

Aim 2: To effectively model aspects of player-like behaviours within FPS bot AI.

Aim 3: To develop a prototype system of bot AI that builds on the identified model to measure levels of player enjoyment.

From the Literature Review it is possible to discern that player-like processes and decision making related to a player's cognitive functions are elements missing from most bot AI. Evidence suggests that incorporating these elements should help address some of the issues regarding the player's enjoyment of FPS bot AI, particularly with regards to predictable and unrealistic bot behaviours. In addressing the aims of this research, a method of integrating some element of a player's cognitive processes, possibly with relation to the skill and experience of the players, is recommended. Additionally, the first aim demands some consideration of the FPS player's game play experience. Emulation of player-like behaviour should have the capacity to noticeably change the bot AI behaviour for the benefit of the player's enjoyment.

3.1 UNDERSTANDING THE INTRICACIES OF PLAYER-LIKE