## 6 Further Considerations
IFO considers challenges relevant to ToM, such as perceptual encoding (vision, proprioception) [158], embodiment mismatch [159], and differences in viewpoint [160]. A clear direction for expansion of IRL methods, and especially so for their applicability as algorithmic ToM, is to settings where the observer not only observes but also acts in the environment or is able to communicate with the actor. In the cooperative setting, although the reward function may be common to both, the policies must complement each other in maximising rewards [161]. Incorporating human feedback in the learning process can be done by querying the expert for action at specific states [55], correcting suboptimal behaviour as it occurs [162], providing pairwise preferences between segments of trajectories [163], or evaluating counterfactual (\"what if?\") scenarios generated by the observer and thus reducing the number of interactions with the environment [50]. Human expertise can also be used to teach features to the observer [164]. The natural and most promising extension of the participative setting is to interactions where both agents have ToM abilities. This gives raise to game-theoretic considerations as both agents model each other's strategies. In psychological game theory, payoffs associated with emotions such as guilt or anger are operationalised into the utility function, going beyond the material payoff-based utility of classical game theory [29]. This framework has been used to predict behaviour in cooperative games [165] and to model the perception of other's intentions [166; 167]. Emotions and mental states are closely interrelated, and computational ToM approaches may benefit from incorporating empathy and affective mentalising, as well as providing a foundation to develop standalone models thereof [168; 169]. The overlap between IRL and game theory is studied in the game identification literature in econometrics [170; 171], wherein the payoffs are estimated from behaviour analytically. Others do so algorithmically, by employing the game-theoretic concept of regret in conjunction with MaxEnt [172], or efficient linear programming in succinct games [173], or learning both the system dynamics and the reward function of multiagent nonzero-sum multistage games [174]. The extension from two-player games to the multiagent setting (e.g., [175; 176; 177]) is nontrivial and may result in emergent phenomena, particularly when sophisticated ToM is present. An interesting application of algorithmic ToM is as applied to oneself, i.e., as a means to introspection [178]. Behaviour-rating methods for reward learning (e.g., [121]) could provide a way for an agent to rate their own behaviour retrospectively and adjust their own mental models accordingly. Most of the considerations, issues, and extensions covered in this review may be applicable to the introspection application of algorithmic ToM, providing an avenue for more sophisticated artificial agents with better social abilities. Priors and inductive biases are built into IRL algorithms and play an important role in narrowing down the space of reward function candidates. These afford a degree of flexibility for encoding relevant ToM heuristics in an algorithm's design, such as normative assumptions [134; 179], neurophysiological correlates [21; 180; 181], or models of human decision-making [182] and habits [183]. More generally, an observer may include their experiences into the prior over time, and develop different structured priors in a hierarchical model, selecting them based on the agent's perceived \"type\" [184; 185]. Furthermore, beyond generalisation, the observer may build agent-specific models that encode their idiosyncrasies, refined through repeated interactions [22]. Models for planning, including ToM, must be abstract, causal, and structured [11]. The IRL approach to ToM is highly abstract--compressing an agent's mental dispositions as a single reward function and environment model.