Dataset used in the article: Evaluation of goal recognition systems on unreliable data and uninspectable agents

Goal or intent recognition, where one agent recognizes the goals or
intentions of another, can be a powerful tool for effective teamwork and
improving interaction between agents. Such reasoning can be challenging to
perform, however, because observations of an agent can be unreliable and,
often, an agent does not have access to the reasoning processes and mental
models of the other agent. Despite this difficulty, recent work has made
great strides in addressing these challenges. In particular, two
Artificial Intelligence (AI)-based approaches to goal recognition have
recently been shown to perform well: goal recognition as planning, which
reduces a goal recognition problem to the problem of plan generation; and
Combinatory Categorical Grammars (CCGs), which treat goal recognition as a
parsing problem. Additionally, new advances in cognitive science with
respect to Theory of Mind reasoning have yielded an approach to goal
recognition that leverages analogy in its decision making. However, there
is still much unknown about the potential and limitations of these
approaches, especially with respect to one another. Here, we explore this
space and compare three state-of-the-art approaches to goal recognition
along two different axes: reliability of observations and inspectability
of the other agent’s mental model. Overall, we show that no approach
dominates across all cases and discuss the relative strengths and
weaknesses of these approaches. Scientists interested in goal recognition
problems can use this knowledge as a guide to select the correct starting
point for their specific domains and tasks.  Research Topic:
Advances in Goal, Plan and Activity Recognition