Learning mental states estimation through self-observation: a developmental synergy between intentions and beliefs representations in a deep-learning model of Theory of Mind (2407.18022v1)

Abstract: Theory of Mind (ToM), the ability to attribute beliefs, intentions, or mental states to others, is a crucial feature of human social interaction. In complex environments, where the human sensory system reaches its limits, behaviour is strongly driven by our beliefs about the state of the world around us. Accessing others' mental states, e.g., beliefs and intentions, allows for more effective social interactions in natural contexts. Yet, these variables are not directly observable, making understanding ToM a challenging quest of interest for different fields, including psychology, machine learning and robotics. In this paper, we contribute to this topic by showing a developmental synergy between learning to predict low-level mental states (e.g., intentions, goals) and attributing high-level ones (i.e., beliefs). Specifically, we assume that learning beliefs attribution can occur by observing one's own decision processes involving beliefs, e.g., in a partially observable environment. Using a simple feed-forward deep learning model, we show that, when learning to predict others' intentions and actions, more accurate predictions can be acquired earlier if beliefs attribution is learnt simultaneously. Furthermore, we show that the learning performance improves even when observed actors have a different embodiment than the observer and the gain is higher when observing beliefs-driven chunks of behaviour. We propose that our computational approach can inform the understanding of human social cognitive development and be relevant for the design of future adaptive social robots able to autonomously understand, assist, and learn from human interaction partners in novel natural environments and tasks.

Insights into a Deep Learning Model of Theory of Mind

The paper "Learning mental states estimation through self-observation: a developmental synergy between intentions and beliefs representations in a deep-learning model of Theory of Mind," presents significant advancements in the computational understanding of Theory of Mind (ToM) through deep learning methodologies. It offers insights into how a machine can be equipped to simulate a rudimentary form of ToM, which fundamentally involves understanding and predicting others' mental states and intentions by leveraging self-observation.

Main Contributions

The paper provides a robust framework that integrates multi-task learning to predict low-level mental states, such as intentions and actions, while attributing high-level beliefs to others. This deep learning model demonstrates that an explicit representation of others' beliefs, when learned concurrently with intentional behavior prediction, profoundly enhances the accuracy and speed of learning intention predictions. Unlike previous models focusing solely on intention prediction, this framework uses a feed-forward deep learning model to simulate a synergy where learning improves when both intentions and beliefs are addressed together.

Strong Numerical Results

The findings, substantiated through a series of rigorous experiments within an 11x11 gridworld environment, showcase significant improvements in prediction outcomes. The most noteworthy performance improvement was a 1.89% increase in accuracy—comparing models that predict intentions alone to those incorporating beliefs—when trained with 750 task executions. This improvement was particularly pronounced in scenarios with hidden targets, illustrating a gain of approximately 14% over the basic models reliant on intention prediction only. Such performance marks a critical step in demonstrating the benefits of integrated intention and belief learning in computational models.

Implications and Future Developments

This paper's findings have profound implications for both theoretical and practical developments in AI and robotics. Practically, the model's enhanced ability to infer mental states can significantly improve human-robot interaction (HRI), contributing to more adaptive and socially aware robotic systems. Robots using such architecture could better anticipate and respond to human intentions, thus improving collaborative tasks. In the field of social robotics, embedding such a model could make machines seem more intuitive and socially cognizant, potentially elevating user trust and acceptance.

Theoretically, this work challenges existing beliefs around ToM emergence and its underlying mechanisms, suggesting that humans aid their understanding of others' intentions by leveraging their own experiences, thus advancing developmental psychology perspectives. This "like-them" hypothesis posited in the paper suggests that self-observation could serve as a critical component in developing predictive capabilities regarding others' mental states, bypassing previously assumed requirements for self-other physical or cognitive similarity.

Conclusion

In sum, this paper acts as a pivotal exploration at the intersection of developmental psychology, AI, and robotics, systematically breaking ground on the potential of machines to approximate human-like social cognition. Further research in this space may not only refine computational models of ToM but also accelerate the integration of AI systems capable of navigating complex social environments, enhancing both theoretical frameworks and practical applications in real-world scenarios. This contribution is non-trivial as it lays down a novel pathway toward cultivating computational entities that understand and react to human behavior with a degree of sophistication previously unachievable.