Multiagent systems
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A logic for uncertain probabilities
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Electric Elves: Applying Agent Technology to Support Human Organizations
Proceedings of the Thirteenth Conference on Innovative Applications of Artificial Intelligence Conference
A POMDP formulation of preference elicitation problems
Eighteenth national conference on Artificial intelligence
The Case for a Hybrid Passive/Active Network Monitoring Scheme in the Wireless Internet
ICON '00 Proceedings of the 8th IEEE International Conference on Networks
R-max - a general polynomial time algorithm for near-optimal reinforcement learning
The Journal of Machine Learning Research
If not now, when?: the effects of interruption at different moments within task execution
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Heuristic search value iteration for POMDPs
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Experiences creating three implementations of the repast agent modeling toolkit
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A utility-based sensing and communication model for a glacial sensor network
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
Multi-objective exploration and search for autonomous rescue robots: Research Articles
Journal of Field Robotics
The cost of interrupted work: more speed and stress
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Cyber Physical Systems: Design Challenges
ISORC '08 Proceedings of the 2008 11th IEEE Symposium on Object Oriented Real-Time Distributed Computing
The permutable POMDP: fast solutions to POMDPs for preference elicitation
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Like an intuitive and courteous butler: a proactive personal agent for task management
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Online planning algorithms for POMDPs
Journal of Artificial Intelligence Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Solving deep memory POMDPs with recurrent policy gradients
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Observer effect from stateful resources in agent sensing
Autonomous Agents and Multi-Agent Systems
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One important problem in multiagent systems is determining how to gather information through sensing to support agent reasoning. This problem commonly arises in real-world applications such as robotics, mixed initiative systems, and others. One promising solution is to use active sensing to explicitly reason about the benefits e.g., information gain, accuracy and costs e.g., resource use, knowledge corruption of sensing actions, then proactively sense to maximize benefits and/or minimize costs. However, properties of complex environments make active sensing more difficult, necessitating further research and evaluation before deploying such solutions. In this paper, we describe MineralMiner, a novel simulation environment that extends previous environments to provide eight common complex environment properties in order to enable the effective study of active sensing. These properties can be fine-tuned using several simulation parameters in order to properly mimic environments likely to occur in real-world applications, allowing for insightful and successful pre-deployment testing, evaluation, and debugging of active sensing solutions, as well as on-going research into active sensing. Furthermore, we describe how several applications of sensing problems benefiting from active sensing can be abstracted and studied within MineralMiner, demonstrating the breadth and depth of its applicability to active sensing research.