Dynamic Programming
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Plan recognition for interface agents
Artificial Intelligence Review
Towards Efficient Belief Update for Planning-Based Web Service Composition
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Maximum entropy inverse reinforcement learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
ANTIPA: an agent architecture for intelligent information assistance
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
A robust dead-reckoning pedestrian tracking system with low cost sensors
PERCOM '11 Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications
Accurate GSM indoor localization
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
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Low-cost navigation solutions for indoor environments have a variety of real-world applications ranging from emergency evacuation to mobility aids for people with disabilities. Challenges for commercial indoor navigation solutions include robust localization, intuitive recognition of user navigation goals, and efficient route-planning and re-planning techniques for resource-constrained platforms like smart-phones and mobile phones. In this paper, we present an architecture for indoor navigation using an Android smartphone that integrates observed behavior for recognizing user navigation goals and estimating future paths without direct input from the user. Our architecture contains three core components: plan recognition, map representation and route planning, and effective localization. To evaluate the feasibility of our solution, we develop a prototype application on a commercial smart-phone and tested it in multiple indoor environments.