Out zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA C. Upton - PDF document

Using zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA Meta-Heuristics t o Explore the Many Would-be Worlds of Combat Stephen zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA Simulations Oxford, UK zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA Out zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA C. Upton 18th ISMOR @ MCCDC PmjectAlbe.t - 31 27 August 2001 I ine TheIdea The Methodology Current Work Future Work Summary 1

The zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA Idea X b zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA I. Find “interesting” regions zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA C zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA continuous x zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA discrete . . . noisy coinplex stiucture 2. Find “relationships” among zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA chaotic interdependent nonsmooth nonlinear . . . X and YI: C Y EX-*, y E Yl: Approach: apply optimum-seeking heuristics and data mining techniques Generative Analysis Proof -of -Concept Project The Idea - Using [multi-agent] simulations and advanced(?) search heuristics (optimum-seeking), have computer generate alternatives . Rationale for idea - Generate surprises - Red-team Cat’s Pajamas concepts - Support Future Concept Development and Exploration, e.g., Urban Warrior Test Idea - Simple Analysis Experiment * - High Resolution - Low Fidelity - Urban setting - What tactical concepts are most effective?

0 zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA j zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA b ect ive T o develop a methodology, and associated software, that automatically finds and relates good solutions within the possibility space of a simulation, where "good" may be: * the best or worst performing * the most robust (best performing over many environments) . the most interesting, e.g., the chaotic, the stable, the unstable . the most surprising or novel Principal Components - Algorithm(s) for generating a "solution" * Specification o f fitness for a "solution" zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA Evaluation mechanism (object t o explore) - simulation or model Algorithm(s) for analyzing a "solution" or set of solutions - Representation for a potential "solution" Mechanism for generator-analyzer t o "talk" that can be manipulated by the generating algorithm(s) 3

zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA Possible Generator zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA I ' hms A laori t t zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA Meta-heuristics Natural Algorithms - Simulated Annealing, Evolutionary Algorithms, Cultural Algorithms,. . . Tabu Search hms Machine Learning A lgor i . . . . . Possible Analyzer Algorithms Multiple Regression Cluster Analysis Association Analysis Bayesian Techniques Neural Networks . . . . . . .

S M T - zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA -* E . - - 0 I . q The Meta-Algorithm WiylOnm. E - 4 E - W - next trial solutions zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA during " A n a w to M p genefate I- Potential Uses Simulation Verification zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA +Concept Exploration (e.g., tactics or system) * Analysis o f Alternatives - Force Structure Design RedTeaming * Concept Generation c - Active Nonlinear Test (ANT) idea Validation (?)

Issues zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA - Estimating performance (fitness) Runtime execution of simulation/model Ensuring diversity o f solutions * Data structure operations (manipulating general objects) * Multi-objectives * Role of constraints . Ordering of search _- zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA - Knowledge extraction I - Current Status * Implemented basic GAn software framework (Condor, JADE, NALEX) * Adding code t o use with Mana zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA * "Tested" software using JIVESTown with GA for search on single machine (over rule base space) * "Tested" software using Socrates with EP algorithm for search on cluster of 20 machines (over weapon, comm, and sensor ranges) * Implementing POSA, Cultured EP, Immune Algorithm 6

To Do List Begin initial experiment with Cultured EP Post software as Albert-ware for general use Examine the role of constraints zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA Combine data mining algorithms, e.g., clustering, with heuristics t o possibly enhance search Combine statistical methodologies with heuristic techniques t o possibly enhance search Adapt generator operators for discrete combinations Examine multi-objective problems and generation of the Pareto optimal set Summary We believe Generative Analysis is a new para and tool for the study and exploration of the possibility space o f a simulation The generation, and analysis, of a large number o f simulations becomes feasible with advances n - high-performance and distributed computing, - automatic programming methods, - heuristic search techniques, and - agent-based modeling

Questions? upton@mitre.org http://www.projectalbert.org/ "Active Nonlinear Tests(ANTs) o f Complex Simulation Models", zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA References aPr~;;~N~k# Genetic Programming: On the Programming o f Computers by Means of Natural Selection, John R. Koza, 1992 Evolutionary Design by Computers, Peter J. Bentley (Editor), 1999 How t o Solve It: Modern Heuristics, Z. Michalewicz and D.B. Fogel, 2000 J. H. Miller, Management Science, Vol. 44, No. 6, June 1998 "Multiojbective Optimization Usinq Evolutionary Alqorithms - A Comparative Case.Study", E. Zitzler and L. Thiele, FPSN V, LCNS 1498, pp 292-301,1998 "Distributed evolutionary algorithms f o r simulation optimization", H. Pierreval and J.L. Paris, IEEE Transactions on Systems Man and Cybernetics, Part A, Vol. 30(#1), pp 15-24, Jan 2000 "Unexpectedness as a measure o f interestingness in knowledge discovery", B. Padmanabhan and A. Tuzhilin", Decision Support Systems, Vol. 27. pp 303-318,1999 8