Adaptive Constraint-Based Agents in Artificial Environments

[INCOMPLETE KNOWLEDGE]   [Single Plan]   [Missing Information]   [Information Gathering]   [Partial Knowledge]

[ Please note: The project has been discontinued as of May 31, 2005 and is superseded by the projects of the ii Labs. There won't be further updates to these pages. ]

Information Gathering

(Related publications: [PUBLink] [PUBLink])

Classical planners normally try to satisfy all goal states. But in an incomplete environment they cannot decide whether an unknown state is already satisfied or not. An unknown state like color(door, blue) could only be satisfied by painting the door blue. If the door is already blue, this action would be unnecessary, and a lack of blue paint would even entail an inconsistency. The ability to plan sensory actions too was realized in various STRIPS-based approaches, such as IPEM by Ambros-Ingerson and Steel [PUBLink], UWL and XII by Etzioni et al. [PUBLink] [PUBLink], Sage by Knoblock [PUBLink] and Occam by Kwok and Weld [PUBLink].


[INCOMPLETE KNOWLEDGE]   [Single Plan]   [Missing Information]   [Information Gathering]   [Partial Knowledge]

For questions, comments or suggestions, please contact us.

Last update:
May 20, 2001 by Alexander Nareyek