1. Eng 101 – Seeds of Success Social and Ethical Implications of Artificial Intelligence Daniel Tauritz, Ph.D. Associate Professor of Computer Science

2. Algorithm An algorithm is a sequence of well-defined instructions that can be executed in a finite amount of time in order to solve some problem.

3. Optimization Algorithm An optimization algorithm is an algorithm which takes as input a solution space, an objective function which maps each point in the solution space to a linearly ordered set, and a desired goal element in the set.

4. Stochastic Algorithm A stochastic algorithm is an algorithm which when executed multiple times with the same input, produces different outputs drawn from some underlying probability distribution.

5. Evolutionary Algorithm A stochastic optimization algorithm inspired by genetics and natural evolution theory.

7. Deriving Gas-Phase Exposure History through Computationally Evolved Inverse Diffusion Analysis Joshua M. Eads Undergraduate in Computer Science Daniel Tauritz Associate Professor of Computer Science Glenn Morrison Associate Professor of Environmental Engineering Ekaterina Smorodkina Former Ph.D. Student in Computer Science

8. Introduction Unexplained Sickness Examine Indoor Exposure History Find Contaminants and Fix Issues

9. Background Indoor air pollution top five environmental health risks $160 billion could be saved every year by improving indoor air quality Current exposure history is inadequate A reliable method is needed to determine past contamination levels and times

10. Problem Statement A forward diffusion differential equation predicts concentration in materials after exposure An inverse diffusion equation finds the timing and intensity of previous gas contamination Knowledge of early exposures would greatly strengthen epidemiological conclusions

11. Gas-phase concentration history and material absorption

12. Proposed Solution x^2 + sin(x) sin(x+y) + e^(x^2) 5x^2 + 12x - 4 x^5 + x^4 - tan(y) / pi sin(cos(x+y)^2) x^2 - sin(x) X+ / Sin ? Use Genetic Programming (GP) as a directed search for inverse equation Fitness based on forward equation

13. Related Research It has been proven that the inverse equation exists Symbolic regression with GP has successfully found both differential equations and inverse functions Similar inverse problems in thermodynamics and geothermal research have been solved

14. Candidate Solutions Population Fitness Interdisciplinary Work Collaboration between Environmental Engineering, Computer Science, and Math Parent Selection ReproductionReproduction CompetitionCompetition Genetic Programming Algorithm Forward Diffusion Equation

15. Genetic Programming Background + * X Si n *X XPi Y = X^2 + Sin( X * Pi )

16. Summary Ability to characterize exposure history will enhance ability to assess health risks of chemical exposure

17. Social and Ethical Impacts Tool to improve health

18. A Coevolutionary Arms-Race Methodology for Improving Electric Power Transmission System Reliability

19. Delivery Problems Transmission Grid Expansion Hampered –Social, environmental, and economic constraints Transmission Grid Already “Stressed” –Already carrying more than intended –Dramatic increase in incidence reports

20. The Grid

21. The Grid: Failure

22. The Grid: Redistribution

23. The Grid: A Cascade

24. The Grid: Redistribution

25. The Grid: Unsatisfiable

27. Failure Summary Failure spreads relatively quickly –Too quickly for conventional control Cascade may be avoidable –Utilize unused capacities (Flow compensation) Unsatisfiable condition may be avoidable –Better power flow control to reduce severity

28. Measuring hardening performance In practice: evaluate over a representative sampling of scenarios Sampling approaches –Pruned Exhaustive (e.g., n-1 security index in power systems) –Monte Carlo –Intelligent adversary

29. Intelligent Adversary Game Theoretic: Two-player game of defenders & attackers Dependent search spaces: grid hardening space (defenders) & scenario space (attackers) Computational methods for dependent search: –Iterative approach –Competitive Coevolution approach –Generalized Co-Optimization approach

30. Competitive Coevolution Type of Evolutionary Algorithm where solution quality is dependent on other solutions For two-player games an arms-race is created by having two opposing populations of solutions where solution quality is inversely dependent on solutions in the opposing population

32. Co-Optimization Generalization of Coevolution Evolutionary principles are replaced by arbitrary black-box optimization techniques Allows matching of interactive problem domains to optimization techniques

33. Summary of methodology Improve grid robustness by creating an arms-race between hardenings (defenders) and fault scenarios (attackers) through the use of Co-Optimization Hardenings are evolved to minimize economic loss Fault scenarios are evolved to maximize economic loss Stair stepping of ability

34. Advanced Power Transmission System with Distributed Power Electronics Devices - Case Study Hardenings: Unified Power Flow Controller (UPFC) placements –Control power flow through transmission lines –UPFCs are a powerful type of Flexible AC Transmission System (FACTS) device Fault scenarios: line outages

35. FACTS Interaction Laboratory HIL Line UPFC Simulation Engine

36. Social & Ethical Implications Grid really improved? –Margins versus profits –New weaknesses introduced Who decides delivery priorities? Can this research be misused?

37. Coevolutionary Automated Software Correction (CASC) ISC Sponsored Project Ph.D. student: Josh Wilkerson

38. Objective: Find a way to automate the process of software testing and correction. Approach: Create Coevolutionary Automated Software Correction (CASC) system which will take a software artifact as input and produce a corrected version of the software artifact as output.

40. Coevolutionary Cycle

41. Population Initialization

45. Initial Evaluation

47. Reproduction Phase

50. Evaluation Phase

52. Competition Phase

54. Termination

56. Social and Ethical Impacts Will this lead to higher unemployment?