The search for effective collaboration between humans and computer systems is one of the biggest challenges in Artificial Intelligence. One of the more effective mechanisms that humans use to coordinate with one another is theory of mind (ToM). ToM can be described as the ability to `take someone else's perspective and make estimations of their beliefs, desires and intentions, in order to make sense of their behaviour and attitudes towards the world'. If leveraged properly, this skill can be very useful in Human-AI collaboration. This introduces the question how we implement ToM when building an AI system. Humans and AI Systems work quite differently, and ToM is a multifaceted concept, each facet rooted in different research traditions across the cognitive and developmental sciences. We observe that researchers from artificial intelligence and the computing sciences, ourselves included, often have difficulties finding their way in the ToM literature. In this paper, we identify four common misconceptions around ToM that we believe should be taken into account when developing an AI system. We have hyperbolised these misconceptions for the sake of the argument, but add nuance in their discussion. The misconceptions we discuss are: (1) "Humans Use a ToM Module, So AI Systems Should As Well". (2) "Every Social Interaction Requires (Advanced) ToM". (3) "All ToM is the Same". (4) "Current Systems Already Have ToM". After discussing the misconception, we end each section by providing tentative guidelines on how the misconception can be overcome.

As humans, we live in a remarkably complex social environment. One cognitive tool which helps us manage all this complexity is our theory of mind, the ability to reason about the mental states of others. By deducing what other people want, feel and think, we can understand their actions, predict how our actions will influence them, and decide how we should behave to be successful. Theory of mind is the cognitive capacity to understand and predict external behavior of others and oneself by attributing internal mental states, such as knowledge, beliefs, and intentions [17]. This is thought to be the pinnacle of social cognition.

The field of game AI is largely industry driven, lacking an agreed upon formalism for AI representation. Ad-hoc scripting languages, simple finite state machines, behaviour trees, and planners are employed, but not in a fashion adhering to any standard. As a result, reuse is sparse between games and formal analysis techniques are undeveloped. As research for a Ph.D. thesis, we propose to show that a layered Statechart-based AI is a suitable formalism for Game AI, enabling the use of model-driven development techniques such as reuse and high-level analysis including model-checking. The fundamentally modular nature of this approach leads naturally to reuse as a fundamental component of the design process. Supported by a clearly defined formalism, useful behavioural analyses become possible, such as testing reactions to various inputs at design time. We also explore transformations at the modelling level to enable procedural generation, allowing rapid deployment of varying AIs. Additionally, such a model allows for the generation of efficient code that can be directly inserted into games. Tool support for reuse, generation, and analysis will be developed, then employed in creating an industrial scale AI, proving that this formalism is appropriate for industrial use.