Abstract
Recognizing the actions, plans, and goals of a person in an unconstrained environment is a key feature that future robotic systems will need in order to achieve a natural human-machine interaction. Indeed, we humans are constantly understanding and predicting the actions and goals of others, which allows us to interact in intuitive and safe ways. While action and plan recognition are tasks that humans perform naturally and with little effort, they are still an unresolved problem from the point of view of artificial intelligence. The immense variety of possible actions and plans that may be encountered in an unconstrained environment makes current approaches be far from human-like performance. In addition, while very different types of algorithms have been proposed to tackle the problem of activity, plan, and goal (intention) recognition, these tend to focus in only one part of the problem (e.g., action recognition), and techniques that address the problem as a whole have been not so thoroughly explored. This review is meant to provide a general view of the problem of activity, plan, and goal recognition as a whole. It presents a description of the problem, both from the human perspective and from the computational perspective, and proposes a classification of the main types of approaches that have been proposed to address it (logic-based, classical machine learning, deep learning, and brain-inspired), together with a description and comparison of the classes. This general view of the problem can help on the identification of research gaps, and may also provide inspiration for the development of new approaches that address the problem in a unified way.
Highlights
The ability to recognize human actions, plans and goals is a necessary skill that future robotic systems will need to implement in order to achieve natural and intuitive human-machine interaction
Summary In summary, classical machine learning approaches to the problem of activity, plan and goal recognition have shown as their main advantage being good at handling uncertainty
This makes them useful to deal with situations that are common in real environments, such as handling interrupted or interleaved plans, coping with partial observability or noisy data, or even dealing with nonrational agents or dynamic domains
Summary
The ability to recognize human actions, plans and goals is a necessary skill that future robotic systems will need to implement in order to achieve natural and intuitive human-machine interaction This is a task that we humans perform constantly when we interact with or observe other humans. Several research studies have shown that humans attribute plans and goals to observed agents performing sequences of actions, and are able to predict the actions (Schmidt et al, 1978; Cohen et al, 1981) All these skills seem to contribute to executive function (which is responsible for the cognitive control of behavior), and this contribution seems to be bidirectional. This way, several authors have suggested that mirror neurons are the basis for the theory of mind, supporting the simulation theory (Gallese and Goldman, 1998)
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