Theory of mind refers to the human ability to reason about the mental content of other people, such as their beliefs, desires, and goals. People use their theory of mind to understand, reason about, and explain the behaviour of others. Having a theory of mind is especially useful when people collaborate, since individuals can then reason on what the other individual knows as well as what reasoning they might do. Similarly, hybrid intelligence systems, where AI agents collaborate with humans, necessitate that the agents reason about the humans using computational theory of mind. However, to try to keep track of all individual mental attitudes of all other individuals becomes (computationally) very difficult. Accordingly, this paper provides a mechanism for computational theory of mind based on abstractions of single beliefs into higher-level concepts. These abstractions can be triggered by social norms and roles. Their use in decision making serves as a heuristic to choose among interactions, thus facilitating collaboration. We provide a formalization based on epistemic logic to explain how various inferences enable such a computational theory of mind. Using examples from the medical domain, we demonstrate how having such a theory of mind enables an agent to interact with humans effectively and can increase the quality of the decisions humans make.

Type II diabetes is a complex health condition requiring patients to closely and continuously collaborate with healthcare professionals and other caretakers on lifestyle changes. While intelligent products have tremendous potential to support such Diabetes Lifestyle Management (DLM), existing products are typically conceived from a technology-centered perspective that insufficiently acknowledges the degree to which collaboration and inclusion of stakeholders is required. In this article, we argue that the emergent design philosophy of Hybrid Intelligence (HI) forms a suitable alternative lens for research and development. In particular, we (1) highlight a series of pragmatic challenges for effective AI-based DLM support based on results from an expert focus group, and (2) argue for HI’s potential to address these by outlining relevant research trajectories.

Empowering artificially intelligent agents with capabilities that humans use regularly is crucial to enable effective human-agent collaboration. One of these crucial capabilities is the modeling of Theory of Mind (ToM) reasoning: the human ability to reason about the mental content of others such as their beliefs, desires, and goals. However, it is generally impractical to track all individual mental attitudes of all other individuals and for many practical situations not even necessary. Hence, what is important is to capture enough information to create an approximate model that is effective and flexible. Accordingly, this paper proposes a computational ToM mechanism based on abstracting beliefs and knowledge into higher-level human concepts, called abstractions, similar to the ones that guide humans to effectively interact with each other (e.g., trust). We develop an agent architecture based on epistemic logic to formalize the computational dynamics of ToM reasoning. We identify important challenges regarding effective maintenance of abstractions and accurate use of ToM reasoning and demonstrate how our approach addresses these challenges over multiagent simulations.

In order to enhance collaboration between humans and artificially intelligent agents, it is crucial to equip the computational agents with capabilities commonly used by humans. One of these capabilities is called Theory of Mind (ToM) reasoning, which is the human ability to reason about the mental contents of others, such as their beliefs, desires, and goals. For an agent to efficiently benefit from having a functioning computational ToM of its human partner in a collaboration, it needs to be practical in computationally tracking their mental attitudes and it needs to create approximate ToM models that can be effectively maintained. In this paper, we propose a computational ToM mechanism based on abstracting beliefs and knowledge into higher-level human concepts, referred to as abstractions. These abstractions, similar to those guiding human interactions (e.g., trust), form the basis of our modular agent architecture. We address an important challenge related to maintaining abstractions effectively, namely abstraction consistency. We propose different approaches to study this challenge in the context of a scenario inspired by a medical domain and provide an experimental evaluation over agent simulations.

In everyday life, people often depend on their theory of mind, i.e., their ability to reason about unobservable mental content of others to understand, explain, and predict their behaviour. Many agent-based models have been designed to develop computational theory of mind and analyze its effectiveness in various tasks and settings. However, most existing models are not generic (e.g., only applied in a given setting), not feasible (e.g., require too much information to be processed), or not human-inspired (e.g., do not capture the behavioral heuristics of humans). This hinders their applicability in many settings. Accordingly, we propose a new computational theory of mind, which captures the human decision heuristics of reasoning by abstracting individual beliefs about others. We specifically study computational affinity and show how it can be used in tandem with theory of mind reasoning when designing agent models for human-agent negotiation. We perform two-agent simulations to analyze the role of affinity in getting to agreements when there is a bound on the time to be spent for negotiating. Our results suggest that modeling affinity can ease the negotiation process by decreasing the number of rounds needed for an agreement as well as yield a higher benefit for agents with theory of mind reasoning.

Theory of mind refers to the human ability to reason about mental content of other people such as beliefs, desires, and goals. In everyday life, people rely on their theory of mind to understand, explain, and predict the behaviour of others. Having a theory of mind is especially useful when people collaborate, since individuals can then reason on what the other individual knows as well as what reasoning they might do. Realization of hybrid intelligence, where an agent collaborates with a human, will require the agent to be able to do similar reasoning through computational theory of mind. Accordingly, this paper provides a mechanism for computational theory of mind based on abstractions of single beliefs into higher-level concepts. These concepts can correspond to social norms, roles, as well as values. Their use in decision making serves as a heuristic to choose among interactions, thus facilitating collaboration on decisions. Using examples from the medical domain, we demonstrate how having such a theory of mind enables an agent to interact with humans efficiently and can increase the quality of the decisions humans make.