SOAR: an architecture for general intelligence
Artificial Intelligence
Automating Image Processing for Scientific Data Analysis of a Large Image Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Borg: A Knowledge-Based System for Automatic Generation of Image Processing Programs
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The MAXQ Method for Hierarchical Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Learning Probabilistic Models for Decision-Theoretic Navigation of Mobile Robots
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
A POMDP formulation of preference elicitation problems
Eighteenth national conference on Artificial intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Sensor management using an active sensing approach
Signal Processing
Stanley: The robot that won the DARPA Grand Challenge: Research Articles
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
Continual planning and acting in dynamic multiagent environments
PCAR '06 Proceedings of the 2006 international symposium on Practical cognitive agents and robots
The Journal of Machine Learning Research
Foundations and Applications of Sensor Management
Foundations and Applications of Sensor Management
Towards an integrated robot with multiple cognitive functions
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Preference elicitation with subjective features
Proceedings of the third ACM conference on Recommender systems
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Continual planning and acting in dynamic multiagent environments
Autonomous Agents and Multi-Agent Systems
Goal-oriented sensor selection for intelligent phones: (GOSSIP)
Proceedings of the 2011 international workshop on Situation activity & goal awareness
Active visual sensing and collaboration on mobile robots using hierarchical POMDPs
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Towards active event recognition
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Flexible, general-purpose robots need to autonomously tailor their sensing and information processing to the task at hand. We pose this challenge as the task of planning under uncertainty. In our domain, the goal is to plan a sequence of visual operators to apply on regions of interest (ROIs) in images of a scene, so that a human and a robot can jointly manipulate and converse about objects on a tabletop. We pose visual processing management as an instance of probabilistic sequential decision making, and specifically as a Partially Observable Markov Decision Process (POMDP). The POMDP formulation uses models that quantitatively capture the unreliability of the operators and enable a robot to reason precisely about the trade-offs between plan reliability and plan execution time. Since planning in practical-sized POMDPs is intractable, we partially ameliorate this intractability for visual processing by defining a novel hierarchical POMDP based on the cognitive requirements of the corresponding planning task. We compare our hierarchical POMDP planning system (HiPPo) with a non-hierarchical POMDP formulation and the Continual Planning (CP) framework that handles uncertainty in a qualitative manner. We show empirically that HiPPo and CP outperform the naive application of all visual operators on all ROIs. The key result is that the POMDP methods produce more robust plans than CP or the naive visual processing. In summary, visual processing problems represent a challenging domain for planning techniques and our hierarchical POMDP-based approach for visual processing management opens up a promising new line of research.