Strategyproof cost-sharing mechanisms for set cover and facility location games
Decision Support Systems - Special issue: The fourth ACM conference on electronic commerce
Mobile opportunistic commerce: mechanisms, architecture, and application
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Computationally feasible VCG mechanisms
Journal of Artificial Intelligence Research
Coordinate: probabilistic forecasting of presence and availability
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Bayesphone: precomputation of context-sensitive policies for inquiry and action in mobile devices
UM'05 Proceedings of the 10th international conference on User Modeling
Cost sharing and strategyproof mechanisms for set cover games
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Location-based crowdsourcing: extending crowdsourcing to the real world
Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries
StarTrack next generation: a scalable infrastructure for track-based applications
OSDI'10 Proceedings of the 9th USENIX conference on Operating systems design and implementation
A mechanism for dynamic ride sharing based on parallel auctions
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Mining regular routes from GPS data for ridesharing recommendations
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Location-based reasoning about complex multi-agent behavior
Journal of Artificial Intelligence Research
Towards ridesharing with passenger transfers
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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We develop and test computational methods for guiding collaboration that demonstrate how shared plans can be created in real-world settings, where agents can be expected to have diverse and varying goals, preferences, and availabilities. The methods are motivated and evaluated in the realm of ridesharing, using GPS logs of commuting data. We consider challenges with coordination among self-interested people aimed at minimizing the cost of transportation and the impact of travel on the environment. We present planning, optimization, and payment mechanisms that provide fair and efficient solutions to the rideshare collaboration challenge. We evaluate different VCG-based payment schemes in terms of their computational efficiency, budget balance, incentive compatibility, and strategy proofness. We present the behavior and analyses provided by the ABC ridesharing prototype system. The system learns about destinations and preferences from GPS traces and calendars, and considers time, fuel, environmental, and cognitive costs. We review how ABC generates rideshare plans from hundreds of real-life GPS traces collected from a community of commuters and reflect about the promise of employing the ABC methods to reduce the number of vehicles on the road, thus reducing CO2 emissions and fuel expenditures.