HBML: A Representation Language for Quantitative Behavioral Models in the Human Terrain

Transcription

1 HBML: A Representation Language for Quantitative Behavioral Models in the Human Terrain Nils F. Sandell, Robert Savell David Twardowski, George Cybenko Conference on Social Computing, Behavioral Modeling, and Prediction April 1st, 2009

2 Outline Introduction Motivation by example The issue of representation of behaviors Our suggested approach Uses of these models Implementation thus far Conclusions 2

3 Introduction-Process Query Systems Given streams of observed events, how do we: Recognize processes Associate events with processes Track the state of processes Observed event sequence:.abcbbbaaaababbabcccbdddbebdbabcbabe. Catalog of Processes/Models 3

4 Introduction-Learning Human Behavior Armed with PQS, we can detect and track behaviors But how do we build human behavior models models for it? Need techniques for mining, learning. To better understand what techniques, we chose a number of human behavior scenarios to explore 4

5 CRAWDAD Wireless access point data for Dartmouth campus Recorded when MAC addresses connected and disconnected Can we learn users behaviors? 5

6 CRAWDAD 6

7 CRAWDAD 7

8 Web Traffic Can we learn a user s web browsing behavior? Can we classify users with these models? 8

9 Web Traffic Web Browsing Model 9

10 Other Domains NASDAQ Can we learn the behavior of market makers? Can we predict their movement in a stock? Can we classify market maker types? MIT Reality Data Cell phone tower association data for test set Can we learn the test users behaviors? Find social networks? 10

11 Problem We have many disparate domains in which to learn human behavior No widespread terminology and framework in which to describe, represent behavior Useful for collaboration and generalization of learning techniques to general behavior HBML Terms, definitions, structure for human behavior 11

12 Problem We have many disparate domains in which to learn human behavior No widespread terminology and framework in which to describe, represent behavior Useful for collaboration and generalization of learning techniques to general behavior HBML Terms, definitions, structure for human behavior Probabilistic Models 12

13 Tracking Terrorists in Human Terrain Interested in building locational behavior model for a group of entities Limited sensing resources: camera LOS occlusion, number of UAVs, etc. Single individual POI shown on right Use this example to elucidate our terms and definitions 13

14 Terms Environment Environment The behavioral domain of interest Encodes rules of interaction as well as topology Example The locational dynamics of the town Road / Path topology One person, one location Expected times of transit 14

15 Terms Entities An object in the domain capable of exhibiting behavior Can be atomic or decomposed further into other entities Example Entities People are atomic entities Working groups, social groups are aggregate 15

16 Terms Profile Behavioral description of an entity relative to a certain set of behaviors Profile Our people have locational behavior profiles Our groups may have a profile describing when they are meeting or not meeting 16

17 Terms Relations Graphically encode interdependencies among the entities Relations Example Social net of targets 2 17

18 Terms Attributes Properties and metadata describing the entities Attributes Can be fixed Name, occupation, skills Or transient Threat score Weight 18

19 Terms Behaviors Taxonomy adapted from Shannon s Information Theory 0th Order Functional Interface 1st Order Conditional Frequencies 2nd Order Dynamical Model Behaviors 19

20 Terms Behaviors Taxonomy adapted from Shannon s Information Theory 0th Order Functional Interface Example The locations Behaviors 0th Order Home (B) Work (A) Meet House (C) Meet House (D) Meet House (E) 20

21 Terms Behaviors Taxonomy adapted from Shannon s Information Theory 1st Order Conditional Frequencies Behaviors 1st Order Weekday Frequencies Weekend Frequencies Home (B) Work (A) Meet House (C) Meet House (D) Meet House (E)

22 Terms Behaviors Taxonomy adapted from Shannon s Information Theory 2nd Order Dynamical Model Behaviors 2nd Order 22

23 Behavioral Modeling Uses Prediction True prediction Given some context and/or observations of past behaviors, determine likely future behaviors Filtering and smoothing Determine likely current and past behavior Kalman filters, Viterbi algorithm, particle filters, etc. Example, predict target s next location 23

24 Behavioral Modeling Uses Classification Determine types of behaviors And types of individuals Based on similarity of distributions Supervised/Unsupervised Classification/Clustering Example Classify group functions Classify relation types Classify group member roles 24

25 Behavioral Modeling Uses Anomaly Detection Identify observed behavior as anomalous i.e., unlikely given the model Requires evaluating probability of a given behavior sequence Example Determine when the groups do something unusual - sign of upcoming event/attack? 25

26 Current Work and Implementation Basic implementation ongoing XML Schema Java reader, writer Conditional probabilities Rudimentary Gibbs sampler 26

27 Future Work Environment rules A priori : Specify behavior types and how they are measured, specify entity labels, automatically formulate mining procedures From data : Generate entity types behavior types, relations between entities, etc. Iterate : Building environment rules and populating instances from data Game theoretic notions Community Suggestions 27

28 Conclusions Formulation of HBML is preliminary Feedback appreciated Useful way of conveying a behavioral domain 28

29 Acknowledgements Research team at Dartmouth Dr. George Cybenko Dr. Vincent Berk Dr. John Murphy Dr. Robert Savell James Tom House Alexy Khrabrov Maj. David Robinson Ian Gregorio-de Souza David Twardowski 29

30 Questions? 30