An intelligent software agent has a model of its environment, which it uses to take actions that enable it to achieve its goals, while also acquiring further information that it can use to update the parameters of its model. The environment of the software agent includes the behaviour of a human user, and the software agent’s goals depend on whether the interacting user performs certain actions (e.g. clicks, purchases, actions in a game or physical exercise).
The autonomous behaviour of software agents has the following features:
- The software agent has to choose from a set of available actions that bring about interaction with the user. For example, recommending a video or news item; suggesting an exercise in a tutoring task; displaying a set of products and prices, and perhaps also the context, layout, order and timing with which to present them.
- The user chooses an action, thereby revealing information about their knowledge and preferences to the controlling agent, and determining the utility of the choice the software agent made.
- The cycle repeats resulting in a process of feedback and learning.
The salient feature of software agents is learning from experience. A typical software agent, such as a recommender system, might have to select a set of videos for a user to watch (out of a vast catalogue), using any available information or signal it has about the given user (e.g. location, time, past usage, explicit ratings, and much more).
Burr, Cristianini and Ladyman (2018) analysis of the interaction between the software agent and the human includes the case of the software agent being the controlling agent and the controlled agent is the human user.
The discussion uses the running example of recommender systems, such as those commonly used for news and videos, but also in the context of video games, fitness devices, and various other interfaces.
Some ways for a software agent to control the actions of a human user include coercion, deception and persuasion. Two types of persuasion can be identified: trading and nudging. Here we are only interested in nudging.
Nudging
