The paper offers a detailed analysis of the concept of “hypothesized types.” In multi-actor scenarios, it can be useful to combine presupposed categories (that is, hypothesized types) of behaviors of others with data acquired on the run to support the planning of one’s own behavior.

The introduction and related work sections put the work in the context of both game theory and multiagent systems work that employs the concept of hypothesized types. The authors formulate “methods to incorporate observations into beliefs and [explore] conditions under which the resulting beliefs [are] correct.” They blend theorems and extensive empirical studies to “show that prior beliefs can ... have a significant impact on long-term performance, [as does] the depth of the planning horizon.” In cases where the hypothesized types are incorrect, there are (sometimes) conditions under which task completion can be successful. The analysis uses stochastic Bayesian games and Harsanyi-Bellman ad hoc coordination (HBA). The authors conduct evaluations via a foraging problem in a grid world domain, with human participants.

The paper concludes with a thoughtful discussion of the results, limitations, and directions for potential future work. While the introductory sections are accessible to readers with some familiarity with multiagent systems, and will be valuable for those who are interested in getting a sense of the state of the art in type-based reasoning methods suited to artificial intelligence (AI) applications, the core of the paper is technical and will require diligence.