A POMDP formulation of preference elicitation problems
Eighteenth national conference on Artificial intelligence
Decision-theoretic active sensing for autonomous agents
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Heuristic search value iteration for POMDPs
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
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
Online planning algorithms for POMDPs
Journal of Artificial Intelligence Research
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Active sensing in complex multiagent environments
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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One popular approach to active perception is using POMDPs to maximize rewards received for sensing actions towards task accomplishment and/or continually refining the agent's knowledge. Multiple types of reward functions have been proposed to achieve these goals: (1) state-based rewards which minimize sensing costs and maximize task rewards, (2) belief-based rewards which maximize belief state improvement, and (3) hybrid rewards combining the other two types. However, little attention has been paid to understanding the differences between these function types and their impact on agent sensing and task performance. In this paper, we begin to address this deficiency by providing (1) an intuitive comparison of the strengths and weaknesses of the various function types, and (2) an empirical evaluation of our comparison in a simulated active perception environment.