GBDTs.jl

GBDTs.jl is a software package written in Julia for learning Grammar-Based Decision Trees (GBDTs).  GBDT combines decision trees, context-free grammars, and expression optimization for automatically categorizing and explaining data.  The package has been applied to categorize scenarios of near mid-air collisions (NMACs) in prototypes of the Next-Generation Airborne Collision Avoidance System (ACAS X).

Related Publications:

  • R. Lee, M. J. Kochenderfer, O. J. Mengshoel, and J. Silbermann, “Interpretable Categorization of Heterogeneous Time Series Data,” in International Conference on Data Mining (SDM), 2018.
    [Bibtex]
    @InProceedings{Lee2018b,
    author = {Lee, Ritchie and Kochenderfer, Mykel J. and Mengshoel, Ole J. and Silbermann, Joshua},
    title = {Interpretable Categorization of Heterogeneous Time Series Data},
    booktitle = sdm,
    year = {2018},
    Organization = {SIAM},
    Owner = {rcnlee}
    }

ExprOptimization.jl

ExprOptimization.jl is a software package written in Julia for the optimization of expression trees.  The package can be used to learn expressions or executable source code from input/output examples, also known as “program induction”.  The package implements a number of tree optimization algorithms including Genetic Programming, Monte Carlo, and Grammatical Evolution.  The software has been used to learn interpretable rules in multivariate heterogeneous time series datasets.

Related Publications:

  • R. Lee, M. J. Kochenderfer, O. J. Mengshoel, and J. Silbermann, “Interpretable Categorization of Heterogeneous Time Series Data,” in International Conference on Data Mining (SDM), 2018.
    [Bibtex]
    @InProceedings{Lee2018b,
    author = {Lee, Ritchie and Kochenderfer, Mykel J. and Mengshoel, Ole J. and Silbermann, Joshua},
    title = {Interpretable Categorization of Heterogeneous Time Series Data},
    booktitle = sdm,
    year = {2018},
    Organization = {SIAM},
    Owner = {rcnlee}
    }

AdaptiveStressTesting.jl

AdaptiveStressTesting.jl is a software package written in Julia for stress testing safety-critical systems.  The package uses reinforcement learning to search for the highest-probability path from an initially healthy system state to a failure event.  The package has been applied to find scenarios of near mid-air collisions (NMACs) in prototypes of the Next-Generation Airborne Collision Avoidance System (ACAS X).

Related Publications:

  • R. Lee, M. J. Kochenderfer, O. J. Mengshoel, G. P. Brat, and M. P. Owen, “Adaptive Stress Testing of Airborne Collision Avoidance Systems,” in Digital Avionics Systems Conference (DASC), 2015.
    [Bibtex]
    @InProceedings{Lee2015,
    author = {Ritchie Lee and Mykel J. Kochenderfer and Ole J. Mengshoel and Guillaume P. Brat and Michael P. Owen},
    title = {Adaptive Stress Testing of Airborne Collision Avoidance Systems},
    booktitle = dasc,
    organization = {AIAA/IEEE},
    year = {2015},
    note = {Best Paper of Session},
    owner = {rcnlee},
    timestamp = {2015.05.20},
    }

Multi-Fidelity Simulator (MFSim)

MFSim is a fast-time national airspace simulator written in Java.  The simulator uses a hierarchical backend that allows aircraft in different regions of the airspace to be simulated at different levels of fidelity.  The simulator has been used as a platform for research in multi-agent reinforcement learning of air traffic automation.

Related Publications:

  • A. Agogino, A. Iscen, R. Lee, D. Bowers, K. Tumer, and G. Brat, “Scalable Hierarchical Multifidelity Simulation and Multiagent Optimization of Air Traffic,” in AAMAS Workshop on Massive Multiagent Systems, 2015.
    [Bibtex]
    @InProceedings{Agogino2015,
    Title = {Scalable Hierarchical Multifidelity Simulation and Multiagent Optimization of Air Traffic},
    Author = {Adrian Agogino and Atil Iscen and Ritchie Lee and Devin Bowers and Kagan Tumer and Guillaume Brat},
    Booktitle = {{AAMAS} Workshop on Massive Multiagent Systems},
    Year = {2015},
    Owner = {rcnlee}
    }

Network-Form Games Library (libnfg)

Libnfg is a software library written in C++ for implementing Network-Form Games (NFGs). NFGs model systems with multiple human actors whose strategic decisions interact in the context of the system.  The algorithms use game theory, probabilistic inference, and reinforcement learning to predict the dynamics of the overall system.

Related Publications:

  • E. J. Schlicht, R. Lee, D. H. Wolpert, M. J. Kochenderfer, and B. Tracey, “Predicting the Behavior of Interacting Humans by Fusing Data from Multiple Sources,” in Conference on Uncertainty in Artificial Intelligence (UAI), 2012.
    [Bibtex]
    @InProceedings{Schlicht2012,
    Title = {Predicting the Behavior of Interacting Humans by Fusing Data from Multiple Sources},
    Author = {Erik J. Schlicht and Ritchie Lee and David H. Wolpert and Mykel J. Kochenderfer and Brendan Tracey},
    Booktitle = uai,
    Organization = {AUAI},
    Year = {2012},
    Owner = {rcnlee},
    }
  • G. Yan, R. Lee, A. Kent, and D. H. Wolpert, “Towards a Bayesian Network Game Framework for Evaluating DDoS Attacks and Defense,” in Conference on Computer and Communications Security (CCS), 2012, p. 553–566.
    [Bibtex]
    @InProceedings{Yan2012,
    Title = {Towards a {B}ayesian Network Game Framework for Evaluating {DDoS} Attacks and Defense},
    Author = {Yan, Guanhua and Lee, Ritchie and Kent, Alex and Wolpert, David H.},
    Booktitle = acmccs,
    Year = {2012},
    Organization = {ACM},
    Pages = {553--566},
    Owner = {rcnlee}
    }
  • Y. Yildiz, R. Lee, and G. Brat, “Using Game Theoretic Models to Predict Pilot Behavior in NextGen Merging and Landing Scenario,” in SciTech, Modeling and Simulation Technologies Conference (MST), 2012.
    [Bibtex]
    @InProceedings{Yildiz2012,
    Title = {Using Game Theoretic Models to Predict Pilot Behavior in {N}ext{G}en Merging and Landing Scenario},
    Author = {Yildiz, Yildiray and Lee, Ritchie and Brat, Guillaume},
    Organization = {AIAA},
    booktitle = aiaa_mst,
    Year = {2012},
    Owner = {rcnlee}
    }