Hi everyone, my name is Nazli Cila and I would like to talk about designing human-agent collaborations in this presentation.
When I use the word "agent", I use it to refer to a multitude of artifacts.
In this example, it could be a smart product such as the smart racket of Djokovic, which helped him to win the Australian Open in 2020.
It could be a robot that we get to see more and more often in the manufacturing lines, working alongside with human employees.
Or it could be a software agent, which helps a doctor to diagnose certain types of skin cancer.
What is common in all these agents is that they are able to collect data and act on data autonomously. They can learn and evolve.
And these capabilities, make interactions as we know it, turn into collaborations.
And this idea of collaboration has been addressed in the HCI literature under the names of Man-Machine Symbiosis, Human-Computer Integration, or Human-AI Teaming.
But regardless of the name it was given, we still don't know what is required from humans and agents in terms of collaboration capabilities.
And also what are the qualities that are relevant for designing these collaborations? And this paper is an attempt to answer these questions.
And to do so I started by the Shared Cooperative Activity framework of the philosopher Michael Bratman. Bratman use the human to human collaboration as a starting point,
and he identified three qualities that are important for effective and pleasant collaborations.
First of these is that the human parties should be committed to the joint activity.
They should be mutually responsive to each other's actions and intentions. And lastly, they should be committed to giving support when there is a need.
And what I did is that I made a large review of the literature of human-agent interaction looking into the literatures in HCI, and also human robot interaction,
and identified some core qualities that are addressed by the researchers.
And I categorised them underneath the human to human collaboration qualities identified by Michael Bratman.
For example, using autonomy and control as an example, afterwards I identified some design considerations that every researcher or designer who is intending to design a collaboration between a human and agent should take into account should try to answer.
At the end, the paper yielded 11 design considerations, and also some promising starting points that designers or researchers can take into consideration in order to answer these design considerations.
So in this presentation, I would like to talk about autonomy and control as an example.
But I would also like to invite the audience to check the paper in order to get a more comprehensive overview on how to design human-agent collaborations.
So first of all, when thinking about autonomy and control one important thing to decide on is how much or what level of autonomy should be delegated to an agent.
And here Beer and her colleagues advice thinking along the primitives of "sense, plan and act".
This is the Packbot, which is a military bomb search robot which is also tele-operated, and it has quite low autonomy when it comes to sensing the obstacles along its path.
So, the human operator should intervene and plan a course of action for Packbot. But once this is done, Packbot is highly autonomous in acting around this obstacle.
Compared to Roomba, for example, Roomba has a medium to high autonomy along these three primitives.
It can sense the obstacles, it can really make a plan around it, and it can act on this plan autonomously.
Furthermore, we also need to think that the human actors and also agents have different capabilities, different skills and knowledge.
It has been shown in relation to wilderness search and rescue robots that human actors are able to focus resources better on likely search areas,
whereas agents such as robots or drones are able to systematically cover ground more effectively.
So the first design consideration is to think what level of autonomy is appropriate for this particular agent.
And a promising starting point would be break the task into its components and assigning each either to an agent to human are both according to their skills and capabilities.
Still talking about autonomy and control, another relevant question is about when and how these agents should intervene in a social context.
And here, the work of Michal Luria and her colleagues investigated the interventions of a voice activated personal assistant.
And the interventions of this agents were welcomed when a boy is waiting for the dinner to get ready,
whereas these interventions were found to be socially awkward, and like disturbing when a couple is quarrelling or making up after a fight.
So, a promising starting point would be to somehow create a mechanism that the agent is able to read a social context,
judge situations according to social norms, and to be able to identify emergencies to intervene.
In HCI literature, there are also some important concepts that are mentioned in relation to autonomy.
For example, flexible autonomy, that's a user can change the autonomy level of an agent dynamically anytime he or she requests that.
Or to be able to identify exceptional inputs to the system, so that the system should ignore them when building models and making predictions.
Or to be able to opt-in or opt-out from the decisions of agents so that any delegation of control to an agent would be overridable.
So, when it comes to design considerations about when or how to release or retain autonomy, as designers,
we should be able to create flexible and open systems that users can adjust agents level of autonomy anytime, flag exceptions in data, and to be able to opt out from the decisions of an agent.
These are examples of the design considerations that were identified in the paper.
And as I mentioned, there are 11 of this.
And the intention behind all this work is to increase the awareness about the qualities of human-agent collaborations,
but also to present a broad overview about the current challenges in HCI and HRI literature when it comes to designing collaborations.
And to be able to support researchers in locating their work, and planning future research. Thank you very much.