Experience with a learning personal assistant
Communications of the ACM
Neuro-Dynamic Programming
A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Probabilistic State-Dependent Grammars for Plan Recognition
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Getting from here to there: interactive planning and agent execution for optimizing travel
Eighteenth national conference on Artificial intelligence
Exploiting belief bounds: practical POMDPs for personal assistant agents
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Who's asking for help?: a Bayesian approach to intelligent assistance
Proceedings of the 11th international conference on Intelligent user interfaces
Efficient solution algorithms for factored MDPs
Journal of Artificial Intelligence Research
Policy recognition in the abstract hidden Markov model
Journal of Artificial Intelligence Research
A decision-theoretic approach to task assistance for persons with dementia
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Implicit interaction for pro-active assistance in a context-adaptive warehouse application
Mobility '07 Proceedings of the 4th international conference on mobile technology, applications, and systems and the 1st international symposium on Computer human interaction in mobile technology
Spoken language interaction with model uncertainty: an adaptive human-robot interaction system
Connection Science - Language and Robots
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
A relational hierarchical model for decision-theoretic assistance
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
ANTIPA: an agent architecture for intelligent information assistance
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
An agent architecture for prognostic reasoning assistance
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
People, sensors, decisions: Customizable and adaptive technologies for assistance in healthcare
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
Interactive activity recognition and prompting to assist people with cognitive disabilities
Journal of Ambient Intelligence and Smart Environments - Home-based Health and Wellness Measurement and Monitoring
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There is a growing interest in intelligent assistants for a variety of applications from organizing tasks for knowledge workers to helping people with dementia. In this paper, we present and evaluate a decision-theoretic framework that captures the general notion of assistance. The objective is to observe a goal-directed agent and to select assistive actions in order to minimize the overall cost. We model the problem as an assistant POMDP where the hidden state corresponds to the agent's unobserved goals. This formulation allows us to exploit domain models for both estimating the agent's goals and selecting assistive action. In addition, the formulation naturally handles uncertainty, varying action costs, and customization to specific agents via learning. We argue that in many domains myopic heuristics will be adequate for selecting actions in the assistant POMDP and present two such heuristics. We evaluate our approach in two domains where human subjects perform tasks in game-like computer environments. The results show that the assistant substantially reduces user effort with only a modest computational effort.