Resolving crises through automated bilateral negotiations
Artificial Intelligence
Simplest Instructions: Finding Easy-to-Describe Routes for Navigation
GIScience '08 Proceedings of the 5th international conference on Geographic Information Science
Using reasoning patterns to help humans solve complex games
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Discounting the future in systems theory
ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
Interaction weaknesses of personal navigation devices
Proceedings of the 2nd International Conference on Automotive User Interfaces and Interactive Vehicular Applications
A study of computational and human strategies in revelation games
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
SC'04 Proceedings of the 4th international conference on Spatial Cognition: reasoning, Action, Interaction
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We present a novel computational method for advice-generation in path selection problems which are difficult for people to solve. The advisor agent's interests may conflict with the interests of the people who receive the advice. Such optimization settings arise in many human-computer applications in which agents and people are self-interested but also share certain goals, such as automatic route-selection systems that also reason about environmental costs. This paper presents an agent that clusters people into one of several types, based on how their path selection behavior adheres to the paths presented to them by the agent who does not necessarily suggest their most preferred paths. It predicts the likelihood that people will deviate from these suggested paths and uses a decision theoretic approach to suggest paths to people which will maximize the agent's expected benefit, given the people's deviations. This technique was evaluated empirically in an extensive study involving hundreds of human subjects solving the path selection problem in mazes. Results showed that the agent was able to outperform alternative methods that solely considered the benefit to the agent or the person, or did not provide any advice.