Planning and control
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
A Value-Driven System for Autonomous Information Gathering
Journal of Intelligent Information Systems
BIG: an agent for resource-bounded information gathering and decision making
Artificial Intelligence - Special issue on Intelligent internet systems
Resource-Bounded Searches in an Information Marketplace
IEEE Internet Computing
An Approximate Nonmyopic Computation for Value of Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Time Sensitive Sequential Myopic Information Gathering
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 6 - Volume 6
A statistical decision-making model for choosing among multiple alternatives
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Choosing between heuristics and strategies: an enhanced model for decision-making
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Efficiently gathering information in costly domains
Decision Support Systems
| Hi-index | 0.00 |
An agent operating in the real world must often choose from among alternatives in incomplete information environments, and frequently it can obtain additional information about them. Obtaining information can result in a better decision, but the agent may incur expenses for obtaining each unit of information. The problem of finding an optimal strategy for obtaining information appears in many domains. For example, in ecommerce when choosing a seller, and in solving programming problems when choosing heuristics. We focus on cases where the agent has to decide in advance on how much information to obtain about each alternative. In addition, each unit of information about an alternative gives the agent only partial information about the alternative, and the range of each information unit is continues. We first formalize the problem of deciding how many information units to obtain about each alternative, and we specify the expected utility function of the agent, given a combination of information units. This function should be maximized by choosing the optimal number of information units. We proceed by suggesting methods for finding the optimal allocation of information units between the different alternatives.