Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Learning metric-topological maps for indoor mobile robot navigation
Artificial Intelligence
Learning in graphical models
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Robot Motion Planning
Coverage for robotics – A survey of recent results
Annals of Mathematics and Artificial Intelligence
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
Representing and Solving Decision Problems with Limited Information
Management Science
Sensor management using an active sensing approach
Signal Processing
Information-driven sensor path planning by approximate cell decomposition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonmyopic Multiaspect Sensing With Partially Observable Markov Decision Processes
IEEE Transactions on Signal Processing
Evolutionary computation and games
IEEE Computational Intelligence Magazine
Discrimination gain to optimize detection and classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Influence diagrams with multiple objectives and tradeoff analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information-driven sensor path planning by approximate cell decomposition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.00 |
This paper presents an information-driven sensor management problem, referred to as treasure hunt, which is relevant to mobile-sensor applications such as mine hunting, monitoring, and surveillance. The objective is to infer a hidden variable or treasure by selecting a sequence of measurements associated with multiple fixed targets distributed in the sensor workspace. The workspace is represented by a connectivity graph, where each node represents a possible sensor deployment, and the arcs represent possible sensor movements. An additive conditional entropy reduction function is presented to efficiently compute the expected benefit of a measurement sequence over time. Then, the optimal treasure hunt strategy is determined by a novel label-correcting algorithm operating on the connectivity graph. The methodology is illustrated through the board game of CLUE®, which is shown to be a benchmark example of the treasure hunt problem. The game results show that a computer player implementing the strategies developed in this paper outperforms players implementing Bayesian networks, Q-learning, or constraint satisfaction, as well as human players.