Uncertainty in Artificial Intelligence
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Constraining Influence Diagram Structure by Generative Planning: An Application to the Optimization of Oil Spill Response
John Agosta
Abstract:
This paper works through the optimization of a real world planning problem, with a combination of a generative planning tool and an influence diagram solver. The problem is taken from an existing application in the domain of oil spill emergency response. The planning agent manages constraints that order sets of feasible equipment employment actions. This is mapped at an intermediate level of abstraction onto an influence diagram. In addition, the planner can apply a surveillance operator that determines observability of the state---the unknown trajectory of the oil. The uncertain world state and the objective function properties are part of the influence diagram structure, but not represented in the planning agent domain. By exploiting this structure under the constraints generated by the planning agent, the influence diagram solution complexity simplifies considerably, and an optimum solution to the employment problem based on the objective function is found. Finding this optimum is equivalent to the simultaneous evaluation of a range of plans. This result is an example of bounded optimality, within the limitations of this hybrid generative planner and influence diagram architecture.
Keywords: Influence diagrams, generative planning, emergency response.
Pages: 11-19
PS Link: ftp://ftp.erg.sri.com/pub/people/johnmark/uc96/GPIDhybrids2.ps
PDF Link: /papers/96/p11-agosta.pdf
BibTex:
@INPROCEEDINGS{Agosta96,
AUTHOR = "John Agosta ",
TITLE = "Constraining Influence Diagram Structure by Generative Planning: An Application to the Optimization of Oil Spill Response",
BOOKTITLE = "Proceedings of the Twelfth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-96)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1996",
PAGES = "11--19"
}


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