GAP Analysis – Dec. Support Time Crit. & Uncertainty

1. GAP Analysis – Dec. SupportTime Crit. & Uncertainty Technical Challenges SUMMARY OF THE STATE OF THE ART Research Areas CURRENT LIMITATIONS TC: Basic algo-design wrt approximation quality/time most anytime algos are designed in an ad-hoc manner, case by case engineering, few generic algos (like anytime A*) No design principle for anytime algos Develop such a design principle TC: Integeration in larger (e.g. sequential) systems Solved for an extremly small class of cases (composition of anytime algos) Unknown what happens for the other classes Software environments, solve the composition problem TC: Describe measures of trade-offs, predict and analyse algo performance One-dimensional trade-offs (like time-quality), simple distributions Multi-dimensional quality - resource measures (e.g. security, cost, time) Address the limitations TC: Adapt prediction to instance Not taken into account so far, empirical analysis does not allow calibration to instances Tune prediction during runtime based on the progress so far Very little work on that… some machine learning on instance hardness prediction

2. GAP Analysis – Dec. SupportTime Crit. & Uncertainty Technical Challenges SUMMARY OF THE STATE OF THE ART Research Areas CURRENT LIMITATIONS Framework: Model Evaluation (“best of breed”), Trade-off of model accuracy and solution quality, Model Ensembles, Learn when each model works Human decision, “camp of advocacy”, models chosen that can be handled rather than models that are accurate No understanding of how simpler models affect solution quality Uncertainty: Model Accuracy, problem formulation “Computation, computation, computation”, little understanding of identification of critical parts of the data “Types” of data, which data needs to be assessed more accurately to enhance the robustness of solutions repeated solution with slightly varying inputs, heuristics rather than optimality Uncertainty: Data Accuracy – Sensitivity Analysis Solution quality vs robustness, recovery rather than best new plan, online scenarios too pessimistic Adaptation to long term and short term goals Recovery rather than best new plan, re-scheduling and -planning Uncertainty along a timeline. Uncertainty: Measure the accuracy of the prediction of quality over time Measure approximation accuracy over the runtime, hybrid parallel algorithms, normalization, dependence of profiles on problem instance complexity Time consuming, dependency on computer architecture various profiling techniques describing expected quality, or quality distribution