Abstract
Identifying and resolving conflicts of interests is a key challenge when designing autonomous agents. For example, such conflicts often occur when complex information systems interact persuasively with humans and are in the future likely to arise in non-human agent-to-agent interaction. We introduce a theoretical framework for an empathic autonomous agent that proactively identifies potential conflicts of interests in interactions with other agents (and humans) by considering their utility functions and comparing them with its own preferences using a system of shared values to find a solution all agents consider acceptable. To illustrate how empathic autonomous agents work, we provide running examples and a simple prototype implementation in a general-purpose programing language. To give a high-level overview of our work, we propose a reasoning-loop architecture for our empathic agent.
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Notes
- 1.
- 2.
As we will explain later, the scenario and the resulting specification can be gradually extended to allow for better real-world applicability.
- 3.
We allow for utility functions to return a null value for action tuples that are considered impossible, e.g. in case some actions are mutually exclusive. While we concede that the elegance of this approach is up for debate, we opted for it because of its simplicity.
- 4.
The \({{\,\mathrm{arg\,max}\,}}\) operator takes the function it precedes and returns all argument tuples that maximize the function.
- 5.
I.e., for the same actions, an agent should only receive a different utility outcome than another agent if the impact on the two is distinguishable in its consequences. We again allow for null values to be returned in case of impossible action tuples.
- 6.
As different aggregation approaches are possible (for example: sum, product) to determine the maximal shared utility, we introduce the not further specified aggregation function \(aggregate(u_{0}, ..., u_{n})\). In our running examples (see Sect. 3), we use the product of the individual utility function outcomes to introduce some notion of fairness; inequality should not be in the interest of the empathic agent. However, the design choice for this implementation detail can be discussed.
- 7.
To facilitate readability, we switch to a pseudo-code notation for the following algorithms.
- 8.
We already use null to denote impossible action tuples. This implies an acceptable action tuple should always exists. To achieve a distinction, a value of \(- \infty \) could be assigned.
- 9.
See the Nash equilibrium definition provided by Osborne and Rubinstein [19, p. 11 et sqq.].
- 10.
As state above, we assume that the first function sorts the action tuples in a deterministic order before returning the first element.
- 11.
\(drive_A \wedge wait_A\) and \(drive_B \wedge wait_B\), respectively, are mutually exclusive (\(\{drive_A \oplus wait_A, drive_B \oplus wait_B\}\), with \(A \oplus B := (A \vee B) \wedge \lnot (A \wedge B)\)). I.e., the functions return null if \(drive_A \wedge wait_A \vee drive_B \wedge wait_B\).
- 12.
The scenario is an adjusted and extended version of the “Bach or Stravinsky? (BoS)” example presented by Osborne and Rubinstein [19, pp. 15–16].
- 13.
Note that the if-condition that triggers the return of a null value simply defines that \(Bach_A\), \(Stravinsky_A\), and \(Mozart_A\) are mutually exclusive, as are \(Bach_B\), \(Stravinsky_B\), and \(Mozart_B\).
- 14.
The code, as well as documentation and tests, are available at http://s.cs.umu.se/qxgbfi.
- 15.
However, the same can be achieved with temporal and probabilistic logic.
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Acknowledgments
We thank the anonymous reviewers for their constructive critical feedback. This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.
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Kampik, T., Nieves, J.C., Lindgren, H. (2019). Empathic Autonomous Agents. In: Weyns, D., Mascardi, V., Ricci, A. (eds) Engineering Multi-Agent Systems. EMAS 2018. Lecture Notes in Computer Science(), vol 11375. Springer, Cham. https://doi.org/10.1007/978-3-030-25693-7_10
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