The notifier agent we have been describing so far, at any level of self-direction, can only operate within the bounds of a very specific goal: to decide when and how to display a message to the user.
Figure 2-2. The self-direction continuum
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There is another level of agency that is useful to consider: one where the agents do not just operate to achieve well-defined goals but can actually gen-erate their own goals.
Imagine your workplace has just been outfitted with the latest “intelligent”
office energy management system. This system has a target of ensuring you don’t spend more than 100 “units” of energy per week and that the occupants of the workplace get the maximum amount of comfort out of those energy units. To achieve this target, it references all the available data, preferences, and rules around what constitutes efficient energy use and comfort and begins taking action. It begins formulating specific goals (desirable environmental states) that it wishes to achieve.
For example, it may decide that it should switch off certain devices because they seem to have been forgotten—switched on but not actively being used.
It may also decide to just ever so slightly drop the office temperature so as to conserve energy. These are different goals that stem from its attempt to meet its higher level target. This is software with its “own” agenda and goals that is using whatever capabilities it has in order to fulfil that agenda.
Let us look at another example. Suppose you have a “wellness” agent whose target is to ensure that all members of a team get a chance to participate in company activities. This wellness agent is given certain capabilities such as access to and the ability to reason about people’s diaries, or the ability to map out relationships based on interactions through e-mails or in chat software.
Using that information, it can then decide to act based on its findings. It will have to make decision such as:
“Do I move that project review meeting and affect the schedules of five people so that Alexis can join a yoga session, or do I have Alexis stay past a certain time in the office to do yoga?”
These are different goals driven by a higher level target. We call these higher level targets motivations, and the ability of agents to pick a goal in order to satisfy their motivations autonomy.
■ Autonomous agents generate or choose between different goals, using higher order motivations.
Autonomy describes an agent’s ability to vary its decisions about what goal to achieve. As with the other concepts discussed so far, autonomy lies on a con-tinuum. Take, for example, the wellness agent from before. We said that it can monitor diaries to ensure that everyone is participating adequately in social activities. What should it do, however, if someone is not participating in social activities? This will depend on how autonomous the agent is. It could,
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for example, simply notify the line manager of the person in question to high-light the issue and leave it at that. Alternatively, it could decide to change an employee’s work schedule so that the employee can then take the opportu-nity to book some time for a social activity. It could change the work schedule and book the social activity without asking anyone’s permission.
As we introduce AI in our work processes, we need to carefully consider exactly how much autonomy we are providing. More autonomy means we are delegating more decision-making power to computer software, and we will potentially reap more efficiency out of it. It also means that we may suffer and have to deal with unintended consequences.
■ An informative example of this is the now infamous Microsoft Tay chatbot released on Twitter. Tay was given considerable autonomy in terms of what messages it could produce and that led to an embarrassment for Microsoft. As trolls “taught” Tay racist and extremist phrases, the chatbot used those phrases in interactions with other people. Microsoft had to recall the bot, blaming a “vulnerability”— the vulnerability was that Tay had no constraints on what it could say and no guidance as to the quality of what it learned.
Before moving on, I want to reiterate the different levels. We will use this terminology throughout the book, so it’s useful to make sure we have it all well laid out.
• Agents are software programs that have some capabilities and can effect change in their environment through those capabilities. The desired change is called a goal.
• Passive agents are ascribed goals by their user. It is the user that manipulates a passive agent’s capabilities. Most software we use behaves like passive agents.
• Active agents have an explicit representation of a goal to achieve and can manipulate their own capabilities to achieve a goal.
• Self-direction refers to the agent’s ability to vary the ways in which it achieves a goal.
• Autonomy refers to the agent’s ability to choose between different goals, in service of a higher order target or motivation.
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