AI agents learn to commit despite disagreement, opening door to faster decisions
Researchers have developed a framework allowing artificial agents to reach partial agreements and commit to decisions without needing total consensus. The advance could accelerate autonomous systems in business, policy, and multi-party negotiations where stakeholders rarely see eye-to-eye but need to move forward decisively.
Originaltitel: Disagree and commit: degrees of argumentation-based agreements
<p>In cooperative human decision-making, agreements are often not total; a partial degree of agreement is sufficient to commit to a decision and move on, as long as one is somewhat confident that the involved parties are likely to stand by their commitment in the future, given no drastic unexpected changes. In this paper, we introduce the notion of agreement scenarios that allow artificial autonomous agents to reach such agreements, using formal models of argumentation, in particular abstract argumentation and value-based argumentation. We introduce the notions of degrees of satisfaction and (minimum, mean, and median) agreement, as well as a measure of the impact a value in a value-based argumentation framework has on these notions. We then analyze how degrees of agreement are affected when agreement scenarios are expanded with new information, to shed light on the reliability of partial agreements in dynamic scenarios. An implementation of the introduced concepts is provided as part of an argumentation-based reasoning software library.</p>