When communication between teammates is limited to observations of each other’s actions, agents may need to improvise to stay coordinated. Unfortunately, current methods inadequately capture the uncertainty introduced by a lack of direct communication. This paper augments existing frameworks to introduce Simple Temporal Networks for Improvisational Teamwork (STN-IT) — a formulation that captures both the temporal dependencies and uncertainties between agents who need to coordinate, but lack reliable communication. We define the notion of strong controllability for STN-ITs, which establishes a static scheduling strategy for controllable agents that produces a consistent team schedule, as long as non-communicative teammates act within known problem constraints. We provide both an exact and approximate approach for finding strongly controllable schedules, empirically demonstrate the trade-offs between these two approaches on a benchmark of STN-ITs, and show analytically that the exact method is correct. In addition, we provide an empirical analysis of the exact and approximate approaches’ efficiency
This content will become publicly available on September 19, 2023
Learning to Design Fair and Private Voting Rules
Voting is used widely to identify a collective decision for a group of agents, based on their preferences. In this paper, we focus on evaluating and designing voting rules that support both the privacy of the voting agents and a notion of fairness over such agents. To do this, we introduce a novel notion of group fairness and adopt the existing notion of local differential privacy. We then evaluate the level of group fairness in several existing voting rules, as well as the trade-offs between fairness and privacy, showing that it is not possible to always obtain maximal economic efficiency with high fairness or high privacy levels. Then, we present both a machine learning and a constrained optimization approach to design new voting rules that are fair while maintaining a high level of economic efficiency. Finally, we empirically examine the effect of adding noise to create local differentially private voting rules and discuss the three-way trade-off between economic efficiency, fairness, and privacy.This paper appears in the special track on AI & Society.
- Publication Date:
- NSF-PAR ID:
- 10388995
- Journal Name:
- Journal of Artificial Intelligence Research
- Volume:
- 75
- Page Range or eLocation-ID:
- 1139 to 1176
- ISSN:
- 1076-9757
- Sponsoring Org:
- National Science Foundation
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