This research builds from our
previous work in constraint-based scheduling and focuses on two key obstacles
to the development and application of intelligent systems in large-scale
planning and scheduling domains such as manufacturing management and crisis-action
logisitics planning:
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Current systems do not effectively support user tasks
and requirements - They force users to operate on their terms, provide
no guidance in understanding, diagnosing and improving results, and do
not support the iterative, evolving process of problem understanding, requirements
determination, conflict resolution and solution refinement that is inherent
in large-scale, multi-agent problem solving.
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Current application building efforts are difficult,
costly and time consuming tasks. The power of knowledge-based planning
and scheduling technologies, i.e., their ability to encode and productively
exploit high-fidelity models of the target environment and domain-specific
heuristics, is also their "Achilles' heel" from a system development perspective.
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