Planning and acting in partially observable stochastic domains
Artificial Intelligence
Region-based incremental pruning for POMDPs
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Stochastic local search for POMDP controllers
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Value-function approximations for partially observable Markov decision processes
Journal of Artificial Intelligence Research
Speeding up the convergence of value iteration in partially observable Markov decision processes
Journal of Artificial Intelligence Research
Towards adjustable autonomy for the real world
Journal of Artificial Intelligence Research
An improved grid-based approximation algorithm for POMDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Computing optimal policies for partially observable decision processes using compact representations
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Solving POMDPs by searching the space of finite policies
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Incremental pruning: a simple, fast, exact method for partially observable Markov decision processes
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Conflicts in teamwork: hybrids to the rescue
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Winning back the CUP for distributed POMDPs: planning over continuous belief spaces
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
RIAACT: a robust approach to adjustable autonomy for human-multiagent teams
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Improving adjustable autonomy strategies for time-critical domains
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
A decision-theoretic model of assistance
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Asimovian multiagents: applying laws of robotics to teams of humans and agents
ProMAS'06 Proceedings of the 4th international conference on Programming multi-agent systems
A relational hierarchical model for decision-theoretic assistance
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Function allocation for NextGen airspace via agents
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: Industry track
Distributed model shaping for scaling to decentralized POMDPs with hundreds of agents
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
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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Agents or agent teams deployed to assist humans often face the challenges of monitoring the state of key processes in their environment (including the state of their human users themselves) and making periodic decisions based on such monitoring. POMDPs appear well suited to enable agents to address these challenges, given the uncertain environment and cost of actions, but optimal policy generation for POMDPs is computationally expensive. This paper introduces three key techniques to speedup POMDP policy generation that exploit the notion of progress or dynamics in personal assistant domains. Policy computation is restricted to the belief space polytope that remains reachable given the progress structure of a domain. We introduce new algorithms; particularly one based on applying Lagrangian methods to compute a bounded belief space support in polynomial time. Our techniques are complementary to many existing exact and approximate POMDP policy generation algorithms. Indeed, we illustrate this by enhancing two of the fastest existing algorithms for exact POMDP policy generation. The order of magnitude speedups demonstrate the utility of our techniques in facilitating the deployment of POMDPs within agents assisting human users.